CN111160649A

CN111160649A - Resource scheduling scheme obtaining method and device

Info

Publication number: CN111160649A
Application number: CN201911400286.1A
Authority: CN
Inventors: 李小勇; 李妍蓉; 侯立洋; 孔文萍
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2019-12-30
Filing date: 2019-12-30
Publication date: 2020-05-15
Anticipated expiration: 2039-12-30
Also published as: CN111160649B

Abstract

The embodiment of the application provides a method and a device for acquiring a resource scheduling scheme, wherein the method comprises the following steps: obtaining resource scheduling data; obtaining a first objective function and a second objective function for dealing with disaster-affected resource scheduling; the first objective function is a function taking the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is a function taking the optimal resource scheduling cost as an objective; and optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point. The resource scheduling scheme acquired by the resource scheduling scheme acquisition method can simultaneously meet the requirements of optimal resource scheduling time satisfaction and optimal resource scheduling cost, and the purpose of acquiring a target scheduling scheme simultaneously meeting a plurality of objective functions is achieved.

Description

Resource scheduling scheme obtaining method and device

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for acquiring a resource scheduling scheme.

Background

In accident rescue activities, various types of materials are urgently needed to be dispatched to disaster-stricken points by each material supply point. Such as water, food, and clothing. In this case, making a reasonably feasible resource scheduling scheme is a central priority for emergency management.

In order to make a reasonably feasible resource scheduling scheme, multiple objectives are often required to be optimized, for example, an objective of time required for delivering materials to a disaster-stricken point is optimized, and an objective of cost consumed for delivering materials to the disaster-stricken point is optimized. However, in the related art, each objective is often converted into a single objective function for solving, and then a plurality of objective functions are converted into a single objective function for solving through weighting, so as to obtain a resource scheduling scheme.

The inventor discovers that: the resource scheduling scheme obtained by the related technology is greatly associated with the weight values of a plurality of objective functions, and because the weight values of the objective functions can only be determined according to the experience of technicians, the error is often large, and a better resource scheduling scheme cannot be obtained.

Disclosure of Invention

An object of the embodiments of the present application is to provide a method and an apparatus for acquiring a resource scheduling scheme, so as to implement an optimal resource scheduling scheme that can simultaneously satisfy multiple objective functions. The specific technical scheme is as follows:

in a first aspect, a method for acquiring a resource scheduling scheme is provided, where the method includes: obtaining resource scheduling data; wherein the resource scheduling data comprises: the method comprises the steps of obtaining a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point for each material to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each material to be scheduled which can be loaded by each vehicle, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed. Obtaining a first objective function and a second objective function for dealing with disaster-affected resource scheduling; the first objective function is a function which is constructed by taking a first quantity, a second quantity, a variety quantity, a demand quantity, a disaster level, a distance, a road congestion coefficient, a resource scheduling speed, a preset maximum time length and the quantity of supplying each type of goods to be scheduled to each disaster-affected point by each goods and materials supply point as parameters, and takes the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is a function which is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods and materials to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters and takes the optimal cost of the resource scheduling as an objective. And optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point.

Wherein the first objective function is:

the second objective function is:

in particular, f₁(x) Representing a first objective function: n represents a first number of supply points for the material; m represents a second number of disaster-affected points; r represents the number of the types of the materials to be dispatched; x is the number of_i，j，kThe quantity of the kth material to be scheduled is scheduled to the jth disaster-stricken point by the ith material supply point; d_i,jRepresenting the distance between the ith material supply point and the jth disaster-stricken point; v. of_i,jRepresenting the resource scheduling speed between the ith material supply point and the jth disaster-stricken point; l is_jRepresenting the disaster level of the jth disaster-affected point; n is a radical of_j，kRepresenting the demand of the jth disaster-stricken point to the kth material to be scheduled; maxTime represents a preset maximum duration;

representing the average speed of the resource transport; mu.s_i,jRepresenting a road congestion coefficient between the ith material supply point and the jth disaster-stricken point; i represents the ith material supply point, wherein i is more than 0 and less than or equal to n; j represents the jth disaster-affected point, wherein j is more than 0 and less than or equal to m; k represents the kth material to be scheduled, wherein k is more than 0 and less than or equal to r; f. of₂(x) Representing a second objective function;

and the third quantity of the kth material to be dispatched can be loaded by each vehicle.

Optionally, the method includes optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the material to be scheduled to each disaster-affected point, and includes: determining a first preset number of weight vectors which are subject to uniform distribution, and determining a second preset number of adjacent weight vectors corresponding to each weight vector. And determining a constraint condition for dispatching the materials to be dispatched from the material supply point to the disaster-affected point. Randomly selecting a first preset number of resource scheduling schemes from a feasible domain defined by a constraint condition to obtain an initialized population; the resource scheduling schemes correspond to the weight vectors one to one. Inputting each resource scheduling scheme in the initialized population into a first objective function, calculating to obtain a first objective function value corresponding to each resource scheduling scheme, and taking the minimum first objective function value as a first minimum function value; and inputting each resource scheduling scheme into a second objective function, calculating to obtain a second objective function value corresponding to each resource scheduling scheme, and taking the minimum second objective function value as a second minimum function value.

When the iterated times are smaller than the preset iterated times, adding one to the iterated times, and executing the following operations: and aiming at each resource scheduling scheme, determining the adjacent weight vector of the weight vector corresponding to the resource scheduling scheme, and obtaining a target adjacent weight vector set. A first weight vector and a second weight vector are randomly selected from the set of target neighboring weight vectors. And calculating a resource scheduling scheme corresponding to the first weight vector and a new resource scheduling scheme generated by a resource scheduling scheme corresponding to the second weight vector according to a preset mutation crossover operator. Inputting the new resource scheduling scheme into a first objective function, and calculating to obtain a first objective function value as a first numerical value; and inputting the new resource scheduling scheme into a second objective function, and calculating to obtain a second objective function value as a second numerical value. The first minimum function value and/or the second minimum function value is updated when the first value is less than the first minimum function value and/or when the second value is less than the second minimum function value. Calculating a first weighting difference and a second weighting difference aiming at a resource scheduling scheme corresponding to each adjacent weight vector in the target adjacent weight vector set; the first weighting difference is a weighted value of a difference value between a first objective function value and a first minimum function value obtained by inputting the resource scheduling scheme to a first objective function, and the second weighting difference is a weighted value of a difference value between a second objective function value and a second minimum function value obtained by inputting the resource scheduling scheme to a second objective function. Replacing a target resource scheduling scheme in the resource scheduling scheme corresponding to the target adjacent weight vector set by using the new resource scheduling scheme; the target resource scheduling scheme is a resource scheduling scheme corresponding to the maximum weighting difference in the first weighting difference and the second weighting difference; and acquiring a pareto optimal solution in the new resource scheduling scheme obtained by the iterative operation calculation and the new resource scheduling scheme obtained by the last iterative operation calculation as a target scheduling scheme. And when the iterated times are equal to the preset iterated times, outputting a target scheduling scheme.

Optionally, calculating a new resource scheduling scheme generated by the resource scheduling scheme corresponding to the first weight vector and the resource scheduling scheme corresponding to the second weight vector according to a preset mutation crossover operator, includes: and calculating a first new resource scheduling scheme and a second new resource scheduling scheme according to the crossover operator in the mutation crossover operator, the resource scheduling scheme corresponding to the first weight vector and the resource scheduling scheme corresponding to the second weight vector. And calculating a first weighting difference of the first new resource scheduling scheme, a second weighting difference of the first new resource scheduling scheme, a first weighting difference of the second new resource scheduling scheme and a second weighting difference of the second new resource scheduling scheme to obtain the resource scheduling scheme with smaller first weighting difference and second weighting difference as the resource scheduling scheme to be mutated. And carrying out mutation on the resource scheduling scheme to be mutated according to mutation operators in the mutation crossover operators to obtain a new resource scheduling scheme.

Wherein, the calculation formula of the first weighting difference is as follows: a. the₁＝λ_i1*|f₁(x_i)-z₁L, |; the second weighted difference is calculated by the formula: a. the₂＝λ_i2*|f₂(x_i)-z₂L, |; specifically, A₁Representing a first weighted difference; a. the₂Representing a second weighted difference; lambda [ alpha ]_i1An ith weight vector representing that the ith resource scheduling scheme corresponds to the first objective function; lambda [ alpha ]_i2An ith weight vector representing that the ith resource scheduling scheme corresponds to the second objective function; x is the number of_iRepresenting the ith resource scheduling scheme; z is a radical of₁Representing the updated first minimum function value; z is a radical of₂Representing the updated second minimum function value.

Optionally, the mutating the resource scheduling scheme to be mutated according to a mutation operator in the mutation crossover operator to obtain a new resource scheduling scheme, including: aiming at each material to be scheduled of a material supply point, when the supplied quantity of the material to be scheduled by the material supply point is equal to the owned quantity in a resource scheduling scheme to be varied and the demand quantity of the material to be scheduled by a disaster point is not met, calculating the variable quantity of the material to be scheduled supplied to each disaster point by the material supply point by using a first mutation operator, and adjusting the resource scheduling scheme to be varied according to the variable quantity to obtain a new resource scheduling scheme. And aiming at each material to be scheduled of the material supply points, when the supplied quantity of the material to be scheduled by the material supply points is smaller than the owned quantity in the resource scheduling scheme to be varied and the demand quantity of the disaster point on the material to be scheduled is met, calculating the variable quantity of the material to be scheduled supplied to the disaster point by the material supply points except for the material supply points by using a second mutation operator, and adjusting the resource scheduling scheme to be varied according to the variable quantity to obtain a new resource scheduling scheme. And aiming at each material to be scheduled of the material supply points, when the supplied quantity of the material supply points to be scheduled is equal to the owned quantity and the demand of the disaster point to the material to be scheduled is met in the resource scheduling scheme to be varied, calculating the variation quantity of each material supply point supplying the material to be scheduled to each disaster point by using a third variation operator, and adjusting the resource scheduling scheme to be varied according to the variation quantity to obtain a new resource scheduling scheme.

In a second aspect, an apparatus for acquiring a resource scheduling scheme is provided, and the apparatus includes:

a first obtaining module, configured to obtain resource scheduling data; wherein the resource scheduling data comprises: the method comprises the steps of obtaining a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point for each material to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each material to be scheduled which can be loaded by each vehicle, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed. The second obtaining module is used for obtaining a first objective function and a second objective function for dealing with the scheduling of the disaster-suffered resources; the first objective function is a function which is constructed by taking a first quantity, a second quantity, a variety quantity, a demand quantity, a disaster level, a distance, a road congestion coefficient, a resource scheduling speed, a preset maximum time length and the quantity of supplying each type of goods to be scheduled to each disaster-affected point by each goods and materials supply point as parameters, and takes the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is a function which is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods and materials to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters and takes the optimal cost of the resource scheduling as an objective. And the optimization module is used for optimizing the first objective function and the second objective function according to the resource scheduling data and the decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for scheduling the materials to be scheduled to each disaster-affected point by each material supply point.

Wherein the first objective function is:

the second objective function is:

in particular, f₁(x) Representing a first objective function: n represents a first number of supply points for the material; m represents a second number of disaster-affected points; r represents the number of the types of the materials to be dispatched; x is the number of_i，j，kThe quantity of the kth material to be scheduled is scheduled to the jth disaster-stricken point by the ith material supply point; d_i，jRepresenting the distance between the ith material supply point and the jth disaster-stricken point; v. of_i，jRepresenting the resource scheduling speed between the ith material supply point and the jth disaster-stricken point; l is_jRepresenting the disaster level of the jth disaster-affected point; n is a radical of_j，kRepresenting the demand of the jth disaster-stricken point to the kth material to be scheduled; maxTime represents a preset maximum duration;

representing the average speed of the resource transport; mu.s_i，jRepresenting a road congestion coefficient between the ith material supply point and the jth disaster-stricken point; i represents the ith material supply point, wherein i is more than 0 and less than or equal to n; j represents the jth disaster-affected point, wherein j is more than 0 and less than or equal to m; k represents the kth material to be scheduled, wherein k is more than 0 and less than or equal to r; f. of₂(x) Representing a second objective function;

Optionally, the optimizing module includes:

the first determining submodule is used for determining a first preset number of weight vectors which are subjected to uniform distribution and determining a second preset number of adjacent weight vectors corresponding to each weight vector. And the second determination sub-simulation is used for determining the constraint condition for scheduling the materials to be scheduled from the material supply point to the disaster-affected point. The selection submodule is used for randomly selecting a first preset number of resource scheduling schemes from the feasible domain defined by the constraint condition to obtain an initialized population; the resource scheduling schemes correspond to the weight vectors one to one. The calculation submodule is used for inputting each resource scheduling scheme in the initialized population into a first objective function, calculating to obtain a first objective function value corresponding to each resource scheduling scheme, and taking the minimum first objective function value as a first minimum function value; and inputting each resource scheduling scheme into a second objective function, calculating to obtain a second objective function value corresponding to each resource scheduling scheme, and taking the minimum second objective function value as a second minimum function value. The iteration submodule is used for adding one to the iterated times when the iterated times are smaller than the preset iterated times and executing the following operations: and aiming at each resource scheduling scheme, determining the adjacent weight vector of the weight vector corresponding to the resource scheduling scheme, and obtaining a target adjacent weight vector set. A first weight vector and a second weight vector are randomly selected from the set of target neighboring weight vectors. And calculating a resource scheduling scheme corresponding to the first weight vector and a new resource scheduling scheme generated by a resource scheduling scheme corresponding to the second weight vector according to a preset mutation crossover operator. Inputting the new resource scheduling scheme into a first objective function, and calculating to obtain a first objective function value as a first numerical value; and inputting the new resource scheduling scheme into a second objective function, and calculating to obtain a second objective function value as a second numerical value. The first minimum function value and/or the second minimum function value is updated when the first value is less than the first minimum function value and/or when the second value is less than the second minimum function value. Calculating a first weighting difference and a second weighting difference aiming at a resource scheduling scheme corresponding to each adjacent weight vector in the target adjacent weight vector set; the first weighting difference is a weighted value of a difference value between a first objective function value and a first minimum function value obtained by inputting the resource scheduling scheme to a first objective function, and the second weighting difference is a weighted value of a difference value between a second objective function value and a second minimum function value obtained by inputting the resource scheduling scheme to a second objective function. Replacing a target resource scheduling scheme in the resource scheduling scheme corresponding to the target adjacent weight vector set by using the new resource scheduling scheme; the target resource scheduling scheme is the resource scheduling scheme corresponding to the maximum weighting difference of the first weighting difference and the second weighting difference. And acquiring a pareto optimal solution in the new resource scheduling scheme obtained by the iterative operation calculation and the new resource scheduling scheme obtained by the last iterative operation calculation as a target scheduling scheme. And the output submodule is used for outputting the target scheduling scheme when the iterated times are equal to the preset iterated times.

Optionally, the iteration sub-module is specifically configured to: and calculating a first new resource scheduling scheme and a second new resource scheduling scheme according to the crossover operator in the mutation crossover operator, the resource scheduling scheme corresponding to the first weight vector and the resource scheduling scheme corresponding to the second weight vector. And calculating a first weighting difference of the first new resource scheduling scheme, a second weighting difference of the first new resource scheduling scheme, a first weighting difference of the second new resource scheduling scheme and a second weighting difference of the second new resource scheduling scheme to obtain the resource scheduling scheme with smaller first weighting difference and second weighting difference as the resource scheduling scheme to be mutated. And carrying out mutation on the resource scheduling scheme to be mutated according to mutation operators in the mutation crossover operators to obtain a new resource scheduling scheme.

In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus; a memory for storing a computer program; a processor for implementing the method steps of any one of the first aspect when executing a program stored in the memory.

In a fourth aspect, a computer-readable storage medium is provided, having stored therein a computer program which, when executed by a processor, carries out the method steps of any one of the first aspect.

In a fifth aspect, there is provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method steps of any of the first aspects.

According to the method and the device for acquiring the resource scheduling scheme, the electronic equipment can acquire resource scheduling data; wherein the resource scheduling data comprises: the method comprises the following steps of (1) setting a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point to each material to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each vehicle capable of containing each material to be scheduled, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed; obtaining a first objective function and a second objective function for dealing with disaster-affected resource scheduling; the first objective function is a function which is constructed by taking a first quantity, a second quantity, a variety quantity, a demand quantity, a disaster level, a distance, a road congestion coefficient, a resource scheduling speed, a maximum time and the quantity of each material to be scheduled supplied to each disaster point by each material supply point as parameters, and takes the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods and materials to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters, and takes the optimal resource scheduling cost as an objective; and optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point. The resource scheduling scheme acquired by the resource scheduling scheme acquisition method can simultaneously meet the requirements of optimal resource scheduling time satisfaction and optimal resource scheduling cost, and the purpose of acquiring a target scheduling scheme simultaneously meeting a plurality of objective functions is achieved.

Of course, not all advantages described above need to be achieved at the same time in the practice of any one product or method of the present application.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a method for acquiring a resource scheduling scheme according to an embodiment of the present application;

FIG. 2 is a flowchart of a method for obtaining a target scheduling scheme based on a multi-objective evolutionary algorithm of decomposition according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a resource scheduling scheme obtaining apparatus according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

A detailed description will be given below of a resource scheduling scheme acquisition method provided in the present application with reference to a specific embodiment, as shown in fig. 1:

step 101: obtaining resource scheduling data; wherein the resource scheduling data comprises: the method comprises the steps of obtaining a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point for each material to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each material to be scheduled which can be loaded by each vehicle, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed.

Step 102: obtaining a first objective function and a second objective function for dealing with disaster-affected resource scheduling; the first objective function is a function which is constructed by taking a first quantity, a second quantity, a variety quantity, a demand quantity, a disaster level, a distance, a road congestion coefficient, a resource scheduling speed, a preset maximum time length and the quantity of supplying each type of goods to be scheduled to each disaster-affected point by each goods and materials supply point as parameters, and takes the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is a function which is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods and materials to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters and takes the optimal cost of the resource scheduling as an objective.

In the embodiment of the application, the electronic device can obtain the disaster level of each disaster-affected point, the third quantity of each material to be scheduled which can be loaded by each vehicle, the distance between each material supply point and each disaster-affected point, the road congestion coefficient, the resource scheduling speed and other resource scheduling data, and generate the corresponding first objective function and second objective function, so that when the resource scheduling scheme is obtained, a plurality of influence factors in the process of scheduling a plurality of materials to be scheduled can be considered.

Step 103: and optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point.

In the embodiment of the application, the electronic device can simultaneously optimize the first objective function taking the optimal satisfaction degree of the resource scheduling time as the target and the second objective function taking the optimal expense degree of the resource scheduling as the target, so as to obtain the optimal scheduling scheme simultaneously meeting the optimal satisfaction degree of the resource scheduling time and the optimal resource scheduling time. Due to the fact that the multi-objective evolutionary algorithm based on decomposition is high in calculation speed, strong in search strength and large in search range, the calculation time for optimizing the first objective function and the second objective function by using the multi-objective evolutionary algorithm based on decomposition is shortened, and the optimal scheduling scheme can be obtained more quickly.

According to the method and the device for acquiring the resource scheduling scheme, the electronic equipment can acquire resource scheduling data; wherein the resource scheduling data comprises: the method comprises the following steps of (1) setting a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point to each material to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each vehicle capable of containing each material to be scheduled, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed; obtaining a first objective function and a second objective function for dealing with disaster-affected resource scheduling; the first objective function is a function which is constructed by taking a first quantity, a second quantity, a variety quantity, a demand quantity, a disaster level, a distance, a road congestion coefficient, a resource scheduling speed, a maximum time and the quantity of each material to be scheduled supplied to each disaster point by each material supply point as parameters, and takes the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods and materials to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters, and takes the optimal resource scheduling cost as an objective; and optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain an optimal scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point. The resource scheduling scheme acquired by the resource scheduling scheme acquisition method can simultaneously meet the requirements of optimal resource scheduling time satisfaction and optimal resource scheduling cost, and the purpose of acquiring a target scheduling scheme simultaneously meeting a plurality of objective functions is achieved.

Optionally, the first objective function is:

the second objective function is:

wherein f is₁(x) Representing a first objective function: n represents a first number of supply points for the material; m represents a second number of disaster-affected points; r represents the number of the types of the materials to be dispatched; x is the number of_i，j，kThe quantity of the kth material to be scheduled is scheduled to the jth disaster-stricken point by the ith material supply point; d_i，jRepresenting the distance between the ith material supply point and the jth disaster-stricken point; v. of_i，jRepresenting the resource scheduling speed between the ith material supply point and the jth disaster-stricken point; l is_jRepresenting the disaster level of the jth disaster-affected point; n is a radical of_j，kRepresenting the demand of the jth disaster-stricken point to the kth material to be scheduled; maxTime represents a preset maximum duration;

representing the average speed of the resource transport; mu.s_i，kRepresenting a road congestion coefficient between the ith material supply point and the jth disaster-stricken point; i represents the ith material supply point, wherein i is more than 0 and less than or equal to n; j represents the jth disaster-affected point, wherein j is more than 0 and less than or equal to m; k represents the kth material to be scheduled, wherein k is more than 0 and less than or equal to r; f. of₂(x) Representing a second objective function;

In the embodiment of the application, when a first objective function with the optimal resource scheduling time satisfaction as a target is designed, the dissatisfaction of each disaster-affected point to the receiving time of the received goods and the dissatisfaction to the waiting time of the non-received goods and the goods to be scheduled are calculated. Therefore, when the first objective function is optimized, the unsatisfies of the waiting time of the acquired resources and the waiting time of the unacquired resources at the disaster-affected point can be simultaneously minimized, and the optimal satisfaction of the resource scheduling time is realized. Meanwhile, the method and the device realize the prior dispatching of the materials to be dispatched to the disaster-stricken points with higher disaster-stricken level under the condition of insufficient resources, and improve the satisfaction of the acquired resource dispatching scheme. The specific first objective function comprises the dissatisfaction degree of each material supply point for supplying each material time to be scheduled to each disaster-affected point; the second objective function comprises the dispatching cost of each material to be dispatched, which is supplied to each disaster-stricken point by each material supply point.

Optionally, as shown in fig. 2, optimizing the first objective function and the second objective function according to the resource scheduling data and the decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the material to be scheduled to each disaster-affected point, where the target scheduling scheme includes:

s201: determining a first preset number of weight vectors which are subject to uniform distribution, and determining a second preset number of adjacent weight vectors corresponding to each weight vector.

S202: and determining a constraint condition for dispatching the materials to be dispatched from the material supply point to the disaster-affected point.

Specifically, the constraint condition may include: the quantity of the materials to be dispatched from the ith material supply point to the jth disaster-stricken point is not less than 0; the quantity of the materials to be dispatched, which are distributed to each disaster-stricken point by the material supply point, is not more than the quantity of the materials to be dispatched, which is required by the disaster-stricken point; the quantity of the materials to be dispatched, which are distributed to each disaster-stricken point by the material supply point, is not more than the storage quantity of the materials to be dispatched in the material supply point.

S203: randomly selecting a first preset number of resource scheduling schemes from a feasible domain defined by a constraint condition to obtain an initialized population; the resource scheduling schemes correspond to the weight vectors one to one.

In the embodiment of the application, the electronic equipment randomly samples from the feasible domain defined by the constraint condition to obtain the initialization population, so that the constraint condition is effectively processed, the calculated amount of the initialization population is reduced, and the more accurate initialization population can be quickly obtained.

S204: inputting each resource scheduling scheme in the initialized population into a first objective function, calculating to obtain a first objective function value corresponding to each resource scheduling scheme, and taking the minimum first objective function value as a first minimum function value; and inputting each resource scheduling scheme into a second objective function, calculating to obtain a second objective function value corresponding to each resource scheduling scheme, and taking the minimum second objective function value as a second minimum function value.

S205: judging whether the iterated times are smaller than the preset iterated times, and if so, executing the step S206; if the number of iterations is equal to the preset number of iterations, step S214 is performed.

S206: and adding one to the iterated times, and determining the adjacent weight vector of the weight vector corresponding to each resource scheduling scheme aiming at each resource scheduling scheme to obtain a target adjacent weight set.

S207: a first weight vector and a second weight vector are randomly selected from the set of target neighboring weight vectors.

S208: and calculating a resource scheduling scheme corresponding to the first weight vector and a new resource scheduling scheme generated by a resource scheduling scheme corresponding to the second weight vector according to a preset mutation crossover operator.

S209: inputting the new resource scheduling scheme into a first objective function, and calculating to obtain a first objective function value as a first numerical value; and inputting the new resource scheduling scheme into a second objective function, and calculating to obtain a second objective function value as a second numerical value.

S210: the first minimum function value and/or the second minimum function value is updated when the first value is less than the first minimum function value and/or when the second value is less than the second minimum function value.

S211: calculating a first weighting difference and a second weighting difference aiming at a resource scheduling scheme corresponding to each adjacent weight vector in the target domain weight vector set; the first weighting difference is a weighted value of a difference value between a first objective function value and a first minimum function value obtained by inputting the resource scheduling scheme to a first objective function, and the second weighting difference is a weighted value of a difference value between a second objective function value and a second minimum function value obtained by inputting the resource scheduling scheme to a second objective function.

S212: replacing a target resource scheduling scheme in the resource scheduling scheme corresponding to the target field weight vector set by using the new resource scheduling scheme; the target resource scheduling scheme is a resource scheduling scheme corresponding to the maximum weighting difference in the first weighting difference and the second weighting difference; the process returns to step S205 and step S213 is executed.

In the embodiment of the present application, a resource scheduling scheme corresponding to a maximum weighting difference in a resource scheduling scheme corresponding to a target domain weight vector set is replaced with a new resource scheduling scheme, so that an absolute difference between a function value of the resource scheduling scheme corresponding to the target domain weight vector set and a first minimum function value and an absolute difference between the first minimum function value and a second minimum function value are smaller, and when next iterative computation is performed, the first objective function and the second objective function can be further optimized by using a resource scheduling scheme corresponding to the updated target domain weight vector set.

S213: and acquiring a pareto optimal solution in the new resource scheduling scheme obtained by the iterative operation calculation and the new resource scheduling scheme obtained by the last iterative operation calculation as a target scheduling scheme.

Specifically, the formula for calculating the pareto optimal solution is as follows:

wherein, P represents a first preset number of resource scheduling schemes of the initialization population; x is the number of_ARepresenting a resource scheduling scheme A; x is the number of_BRepresents a resource scheduling scheme B; f (x)_A) A function value representing an input objective function of the resource scheduling scheme A; f (x)_B) A function value representing an input objective function of the resource scheduling scheme B;

is a term of full weight in mathematical logic, meaning any, all, and the like;

representing quantifier of presence in mathematical logic, representing presence, some of it, etc.; Λ is a conjunctive term in mathematical logic, meaning "and", etc. Wherein when f (x)_A) And f (x)_B) When the calculation formula of the pareto optimal solution is satisfied, the resource scheduling scheme A is the pareto optimal solution superior to the resource scheduling scheme B.

S214: and when the iterated times are equal to the preset iterated times, outputting a target scheduling scheme.

In the embodiment of the application, the electronic device optimizes the first objective function and the second objective function simultaneously based on the decomposed multi-objective evolutionary algorithm, so that the problem that the weight of each objective function is selected when the multi-objective function is converted into a single objective function in the prior art is solved, the optimization goal of each objective function can be considered in a balanced manner, and the optimal resource scheduling scheme which simultaneously meets a plurality of objective functions is obtained. The electronic device may adjust the preset iteration number according to the first preset number of the resource scheduling schemes in the initialization population, so as to achieve comprehensive optimization of the resource scheduling schemes in the initialization population.

Optionally, the calculation formula of the first weighting difference is: a. the₁＝λ_i1*|f₁(x_i)-z₁L, |; the second weighted difference is calculated by the formula: a. the₂＝λ_i2*|f₂(x_i)-z₂L. Wherein A is₁Representing a first weighted difference; a. the₂Representing a second weighted difference; lambda [ alpha ]_i1An ith weight vector representing that the ith resource scheduling scheme corresponds to the first objective function; lambda [ alpha ]_i2An ith weight vector representing that the ith resource scheduling scheme corresponds to the second objective function; x is the number of_iRepresenting the ith resource scheduling scheme; z is a radical of₁Representing the updated first minimum function value; z is a radical of₂Representing the updated second minimum function value.

Optionally, the mutating the resource scheduling scheme to be mutated according to a mutation operator in the mutation crossover operator to obtain a new resource scheduling scheme, including: aiming at each material to be scheduled of a material supply point, when the supplied quantity of the material to be scheduled by the material supply point is equal to the owned quantity in a resource scheduling scheme to be varied and the demand quantity of the material to be scheduled by a disaster point is not met, calculating the variable quantity of the material to be scheduled supplied to each disaster point by the material supply point by using a first mutation operator, and adjusting the resource scheduling scheme to be varied according to the variable quantity to obtain a new resource scheduling scheme. Wherein the first mutation operator is:

delta＝min(rand(0，x_i，j′，_，k)，(N_j，k-has_j，k))

X_i，j，k＝x_i，j，k+delta

X_i，j′，k＝x_i，j′，k-delta

for example, when there are two supply points: s1 and S2, and disaster-stricken points are three: at D1, D2, and D3, randomly selecting one of the scheduling schemes to be varied for the material to be scheduled as shown in table one:

watch 1

For the kind of material to be scheduled at S1, when the supplied amount of the material to be scheduled by S1 in the resource scheduling scheme to be mutated is equal to the owned amount, and there is a demand amount of D2 for the kind of material to be scheduled that is not satisfied, the amount of the material to be scheduled supplied by S1 to the disaster-stricken point other than D2 may be reduced, and the amount of the material to be scheduled supplied by S1 to D2 may be increased. When choosing to reduce the supply of S1 to D1, the first mutation operator is:

delta＝min(rand(0，x_1，2，1)，(N_1，1-has_1，1))

X_1，1，1＝x_1，1，1+delta

X_1，2，1＝x_1，2，1-delta

it can be calculated that the variation is 1, that is, the S1 decreases the amount of the resource to be scheduled supplied by D1 by one unit, the S1 increases the amount of the resource to be scheduled supplied by D2 by one unit, and the S1 does not change the amount of the resource to be scheduled supplied by D3, and the obtained new resource scheduling scheme is shown in table two:

watch two

And aiming at each material to be scheduled of the material supply points, when the supplied quantity of the material to be scheduled by the material supply points is smaller than the owned quantity in the resource scheduling scheme to be varied and the demand quantity of the disaster point on the material to be scheduled is met, calculating the variable quantity of the material to be scheduled supplied to the disaster point by the material supply points except for the material supply points by using a second mutation operator, and adjusting the resource scheduling scheme to be varied according to the variable quantity to obtain a new resource scheduling scheme. Wherein the second mutation operator is:

delta＝min(rand(0，x_i′，j，k)，(N_j，k-has_j，k))

x_i，j，k＝x_i，j，k+delta

x_i′，j，k＝x_i′，j，k-delta

for example, when there are two supply points: s1 and S2, and disaster-stricken points are three: at D1, D2, and D3, one of the to-be-scheduled material scheduling schemes is randomly selected as shown in table three:

watch III

For the kind of material to be scheduled at S1, when the supplied amount of the material to be scheduled at S1 in the resource scheduling scheme to be mutated is smaller than the owned amount and the required amount of the material to be scheduled by D2 is satisfied, the amount of the material to be scheduled supplied to D2 at S1 may be reduced, and the amount of the material to be scheduled supplied to D2 at a material supply point other than S1 may be increased. When the supply of D2 from S2 is chosen to be increased, the second mutation operator:

delta＝min(rand(0，x_2，1，1)，(N_1，1-has_1，1))

X_1，1，1＝x_1，1，1+delta

X_2，1，1＝x_2，1，1-delta

it can be calculated that the variation is 1, that is, the S1 increases the amount of D2 supplying the resource to be scheduled by one unit, and the S2 decreases the amount of D2 supplying the resource to be scheduled by one unit, and the obtained new resource scheduling scheme is shown in table four:

watch four

And aiming at each material to be scheduled of the material supply points, when the supplied quantity of the material supply points to be scheduled is equal to the owned quantity and the demand of the disaster point to the material to be scheduled is met in the resource scheduling scheme to be varied, calculating the variation quantity of each material supply point supplying the material to be scheduled to each disaster point by using a third variation operator, and adjusting the resource scheduling scheme to be varied according to the variation quantity to obtain a new resource scheduling scheme. Wherein the third operator is:

delta＝min(rand(0，min(x_i，j′，k，x_i′，j，k))，(N_j，k-has_j，k))

X_i，j，k＝x_i，j，k+delta；X_{i′，j′，k}＝x_{i′，j′，k}+delta

X_i，j′，k＝x_i，j′，k-delta；X_i′，j，k＝x_i′，j，k-delta

wherein, delta represents the amount of variation; x is the number of_i，j′，kThe method comprises the steps that an ith material supply point schedules the number of kth materials to be scheduled to a jth disaster-stricken point in a scheduling scheme of the resources to be varied; x is the number of_i′，j，kIndicating that in the scheduling scheme of the resources to be mutated, the ith' material supply point schedules the number of the kth materials to be scheduled to the jth disaster-affected point; x is the number of_{i′，j′，k}Representing that in the scheduling scheme of the resources to be varied, the ith 'material supply point schedules the number of the kth materials to be scheduled to the jth' disaster-affected point; x_i，j，kIndicating that in the new resource scheduling scheme, the ith material supply point schedules the number of kth materials to be scheduled to the jth disaster-stricken point; x_i，j′，kIndicating that in the new resource scheduling scheme, the ith material supply point schedules the number of kth materials to be scheduled to the jth disaster-affected point; x_i′，j，kIndicating that in the new resource scheduling scheme, the ith' material supply point schedules the number of kth materials to be scheduled to the jth disaster-stricken point; x_{i′，j′，k}Indicating that in the new resource scheduling scheme, the ith 'material supply point schedules the number of kth materials to be scheduled to the jth' disaster-affected point; n is a radical of_j，kRepresenting the demand of the jth disaster-stricken point to the kth resource; has_j，kAnd the number of the kth resource to be scheduled obtained by the jth disaster-affected point in the resource scheduling scheme to be mutated is shown.

For example, when there are two supply points: s1 and S2, and disaster-stricken points are three: at D1, D2, and D3, a scheduling scheme of randomly selecting one to-be-scheduled material among to-be-varied scheduling schemes is shown in table five:

watch five

For the kind of to-be-scheduled resource at S1, when the supplied amount of the S1 to the kind of to-be-scheduled resource in the to-be-mutated resource scheduling scheme is equal to the owned amount, and the demand amount of the D2 to the kind of to-be-scheduled resource is satisfied, the amount of the S1 to supply the kind of to-be-scheduled resource to the D2 may be increased, the amount of the S1 to supply the kind of to-be-scheduled resource to the disaster-stricken point other than the D2 may be decreased, and the amount of the S1 to supply the kind of to-be-scheduled resource to the D2 may be decreased. When the selected material supply point is S2 and the disaster point is D1, the third mutation operator is:

delta＝min(rand(0，min(x_1，2，1，x_2，1，1))，(N_1，1-has_1，1))

X_1，1，1＝x_1，1，1+delta；X_2，2，1＝x_2，2，1-delta

X_1，2，1＝x_1，2，1-delta；X_2，1，1＝x_2，1，1+delta

the variance of 1 can be calculated, that is, S1 increases the amount of D1 supplying this kind of resource to be scheduled by one unit; s1 reduces the amount of D2 supplying this kind of resource to be scheduled by one unit; s2 increasing the amount of D1 supplying this kind of resource to be scheduled by one unit; s2 reduces the amount of D2 supplying the resource to be scheduled by one unit, and the obtained new resource scheduling scheme is shown in Table six:

watch six

In the embodiment of the application, a technical person designs a new mutation operator, the search strength of a feasible resource scheduling scheme is enhanced, the diversity of the resource scheduling scheme in the initialized population is increased, and the feasibility of the resource scheduling scheme in the initialized population is enhanced at the same time. Different mutation operators for different conditions are designed, and resource scheduling scheme mutation is performed for the conditions of sufficient resources and insufficient resources in the scheme to be mutated, so that the defect that only one of the two conditions of sufficient resources or insufficient resources can be limited in the prior art is overcome, the conditions of sufficient resources and insufficient resources are effectively processed, a new resource scheduling scheme is more flexible, and the method can adapt to different conditions in practical application.

For example, the acquired resource scheduling data includes: four material supply points: s1, S2, S3 and S4; three disaster-affected points: d1, D2 and D3; the disaster level of the three disaster-affected points is 1, 4 and 2 in sequence, wherein the larger the disaster level numerical value is, the higher the disaster level is; 5 materials to be dispatched: r1, R2, R3, R4 and R5; the first preset number is 100; presetting the iteration number to be 200; the storage capacity of each material supply point for various materials to be scheduled is shown in table seven:

watch seven

The demand of each disaster point to each material to be scheduled is as shown in table eight:

table eight

The road congestion coefficients between each disaster-affected point and each material supply point are shown in table nine:

watch nine

The distance between each disaster point and each material supply point is shown in table ten:

watch ten

Then, a first objective function with optimal resource scheduling time satisfaction as a target and a second objective function with optimal resource scheduling cost as a target are obtained, resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm are used for optimizing the first objective function and the second objective function, and an optimal scheduling scheme for scheduling the goods and materials to be scheduled to each disaster-affected point by each goods and materials supply point is obtained, as shown in the eleventh table:

watch eleven

Based on the same concept, an embodiment of the present application further provides an apparatus for acquiring a resource scheduling scheme, as shown in fig. 3, the apparatus includes:

a first obtaining module 301, configured to obtain resource scheduling data; wherein the resource scheduling data comprises: the method comprises the following steps of firstly, obtaining a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point for each material to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each material to be scheduled which can be loaded by each vehicle, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed;

a second obtaining module 302, configured to obtain a first objective function and a second objective function for dealing with disaster-stricken resource scheduling; the first objective function is a function which is constructed by taking a first quantity, a second quantity, a variety quantity, a demand quantity, a disaster level, a distance, a road congestion coefficient, a resource scheduling speed, a preset maximum time length and the quantity of supplying each type of goods to be scheduled to each disaster-affected point by each goods and materials supply point as parameters, and takes the optimal satisfaction degree of the resource scheduling time as an objective; the second objective function is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods and materials to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters, and takes the optimal resource scheduling cost as an objective;

and the optimization module 303 is configured to optimize the first objective function and the second objective function according to the resource scheduling data and the decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the material to be scheduled to each disaster-affected point.

Optionally, the first objective function is:

the second objective function is:

Optionally, the optimizing module 303 includes:

the first determining submodule is used for determining a first preset number of weight vectors which are subjected to uniform distribution and determining a second preset number of adjacent weight vectors corresponding to each weight vector; the second determination sub-simulation is used for determining the constraint condition for scheduling the materials to be scheduled from the material supply point to the disaster-affected point; the selection submodule is used for randomly selecting a first preset number of resource scheduling schemes from the feasible domain defined by the constraint condition to obtain an initialized population; wherein, the resource scheduling scheme corresponds to the weight vector one by one; the calculation submodule is used for inputting each resource scheduling scheme in the initialized population into a first objective function, calculating to obtain a first objective function value corresponding to each resource scheduling scheme, and taking the minimum first objective function value as a first minimum function value; inputting each resource scheduling scheme into a second objective function, calculating to obtain a second objective function value corresponding to each resource scheduling scheme, and taking the minimum second objective function value as a second minimum function value; the iteration submodule is used for adding one to the iterated times when the iterated times are smaller than the preset iterated times and executing the following operations: for each resource scheduling scheme, determining an adjacent weight vector of the weight vector corresponding to the resource scheduling scheme to obtain a target adjacent weight vector set; randomly selecting a first weight vector and a second weight vector from a target neighborhood weight vector set; calculating a resource scheduling scheme corresponding to the first weight vector and a new resource scheduling scheme generated by a resource scheduling scheme corresponding to the second weight vector according to a preset mutation crossover operator; inputting the new resource scheduling scheme into a first objective function, and calculating to obtain a first objective function value as a first numerical value; inputting the new resource scheduling scheme into a second objective function, and calculating to obtain a second objective function value as a second numerical value; updating the first minimum function value and/or the second minimum function value when the first value is less than the first minimum function value and/or when the second value is less than the second minimum function value; calculating a first weighting difference and a second weighting difference aiming at a resource scheduling scheme corresponding to each adjacent weight vector in the target domain weight vector set; wherein, the first weighting difference is a weighted value of a first objective function value and a first numerical difference value obtained by inputting the resource scheduling scheme to a first objective function, and the second weighting difference is a weighted value of a second objective function value and a first numerical difference value obtained by inputting the resource scheduling scheme to a second objective function; replacing a target resource scheduling scheme in the resource scheduling scheme corresponding to the target field weight vector by using the new resource scheduling scheme; the target resource scheduling scheme is a resource scheduling scheme corresponding to the maximum weighting difference in the first weighting difference and the second weighting difference; acquiring a pareto optimal solution in a new resource scheduling scheme obtained by the iterative operation calculation and a new resource scheduling scheme obtained by the last iterative operation calculation as a target scheduling scheme; and the output submodule is used for outputting the target scheduling scheme when the iterated times are equal to the preset iterated times.

Optionally, the iteration sub-module is specifically configured to: calculating a first new resource scheduling scheme and a second new resource scheduling scheme according to a crossover operator in the mutation crossover operators, a resource scheduling scheme corresponding to the first weight vector and a resource scheduling scheme corresponding to the second weight vector; calculating a first weighting difference of the first new resource scheduling scheme, a second weighting difference of the first new resource scheduling scheme, a first weighting difference of the second new resource scheduling scheme and a second weighting difference of the second new resource scheduling scheme to obtain a resource scheduling scheme with smaller first weighting difference and second weighting difference as a resource scheduling scheme to be mutated; and carrying out mutation on the resource scheduling scheme to be mutated according to mutation operators in the mutation crossover operators to obtain a new resource scheduling scheme.

An embodiment of the present application further provides an electronic device, as shown in fig. 4, including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404, and the memory 403 is used for storing a computer program; the processor 401 is configured to implement any method step in the above method for acquiring the resource scheduling scheme when executing the program stored in the memory 403.

The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.

The communication interface is used for communication between the electronic equipment and other equipment.

The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.

The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.

In another embodiment provided by the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements any one of the method steps in the above resource scheduling scheme obtaining method embodiment.

In yet another embodiment provided by the present application, there is also provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform any of the method steps of the above-mentioned resource scheduling scheme acquisition method embodiment.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, the electronic device, the computer-readable storage medium, and the computer program product embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiments.

The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims

1. A method for acquiring a resource scheduling scheme, the method comprising:

obtaining resource scheduling data; wherein the resource scheduling data comprises: the method comprises the steps of obtaining a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point for each type of materials to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each type of materials to be scheduled which can be loaded by each vehicle, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed;

obtaining a first objective function and a second objective function for dealing with disaster-affected resource scheduling; wherein the first objective function is a function constructed by taking the first amount, the second amount, the category amount, the demand amount, the disaster level, the distance, the road congestion coefficient, the resource scheduling speed, the preset maximum duration, and the amount of each kind of the goods to be scheduled supplied to each disaster point by each goods and materials supply point as parameters, and taking the satisfaction degree of resource scheduling time as an objective; the second objective function is a function which is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters and takes the optimal resource scheduling cost as an objective;

and optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolution MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point.

2. The method of claim 1, wherein the first objective function is:

the second objective function is:

wherein, the f₁(x) Representing the first objective function: the n represents a first number of the supply points; said m represents a second number of said disaster-stricken points; the r represents the type number of the materials to be dispatched; said x_i,j,kThe quantity of the kth material to be scheduled is scheduled to the jth disaster-stricken point by the ith material supply point; said D_i,jRepresenting the distance between the ith material supply point and the jth disaster-stricken point; v is_i,jRepresenting the resource scheduling speed between the ith material supply point and the jth disaster-stricken point; said L_jRepresenting the disaster level of the jth disaster-stricken point; said N is_j,kRepresenting the demand of the jth disaster-stricken point to the kth material to be scheduled; the maxTime represents the preset maximum duration; the above-mentioned

Representing the average speed of the resource transport; the mu_i,jRepresenting a road congestion coefficient between the ith material supply point and the jth disaster-stricken point; the i represents the ith material supply point, wherein i is more than 0 and less than or equal to n; j represents the jth disaster-stricken point, wherein j is more than 0 and less than or equal to m; k represents the kth material to be scheduled, wherein k is more than 0 and less than or equal to r; the above-mentionedf₂(x) Representing the second objective function; the above-mentioned

3. The method according to claim 2, wherein the optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each of the material supply points to schedule the material to be scheduled to each of the disaster-stricken points comprises:

determining a first preset number of weight vectors which are subjected to uniform distribution, and determining a second preset number of adjacent weight vectors corresponding to each weight vector;

determining a constraint condition for dispatching the materials to be dispatched from the material supply point to the disaster-stricken point;

randomly selecting the first preset number of resource scheduling schemes from the feasible domain defined by the constraint condition to obtain an initialized population; the resource scheduling schemes correspond to the weight vectors one by one;

inputting each resource scheduling scheme in the initialized population into the first objective function, calculating to obtain a first objective function value corresponding to each resource scheduling scheme, and taking the minimum first objective function value as a first minimum function value; inputting each resource scheduling scheme into the second objective function, calculating to obtain a second objective function value corresponding to each resource scheduling scheme, and taking the minimum second objective function value as a second minimum function value;

when the iterated times are smaller than the preset iterated times, adding one to the iterated times, and executing the following operations:

for each resource scheduling scheme, determining an adjacent weight vector of the weight vector corresponding to the resource scheduling scheme to obtain a target adjacent weight vector set;

randomly selecting a first weight vector and a second weight vector from the set of target neighboring weight vectors;

calculating a resource scheduling scheme corresponding to the first weight vector and a new resource scheduling scheme generated by a resource scheduling scheme corresponding to the second weight vector according to a preset mutation crossover operator;

inputting the new resource scheduling scheme into the first objective function, and calculating to obtain a first objective function value as a first numerical value; inputting the new resource scheduling scheme into a second objective function, and calculating to obtain a second objective function value as a second numerical value;

updating the first minimum function value and/or the second minimum function value when the first value is less than the first minimum function value and/or when the second value is less than the second minimum function value;

calculating a first weighted difference and a second weighted difference aiming at a resource scheduling scheme corresponding to each adjacent weight vector in the target adjacent weight vector set; wherein, the first weighting difference is a weighted value of a first objective function value and a first minimum function value difference value obtained by inputting the resource scheduling scheme to the first objective function, and the second weighting difference is a weighted value of a second objective function value and a second minimum function value difference value obtained by inputting the resource scheduling scheme to the second objective function;

replacing a target resource scheduling scheme in the resource scheduling schemes corresponding to the target adjacent weight vector set by using the new resource scheduling scheme; the target resource scheduling scheme is a resource scheduling scheme corresponding to the maximum weighting difference in the first weighting difference and the second weighting difference;

acquiring a pareto optimal solution in the new resource scheduling scheme obtained by the iterative operation calculation and the new resource scheduling scheme obtained by the last iterative operation calculation as a target scheduling scheme;

and when the iterated times are equal to the preset iterated times, outputting the target scheduling scheme.

4. The method of claim 3, wherein the calculating a new resource scheduling scheme generated by the resource scheduling scheme corresponding to the first weight vector and the resource scheduling scheme corresponding to the second weight vector according to a predetermined mutation crossover operator comprises:

calculating a first new resource scheduling scheme and a second new resource scheduling scheme according to a crossover operator in the variant crossover operators, a resource scheduling scheme corresponding to the first weight vector and a resource scheduling scheme corresponding to the second weight vector;

calculating a first weighting difference of the first new resource scheduling scheme, a second weighting difference of the first new resource scheduling scheme, a first weighting difference of the second new resource scheduling scheme and a second weighting difference of the second new resource scheduling scheme to obtain a resource scheduling scheme with smaller first weighting difference and second weighting difference as a resource scheduling scheme to be mutated;

and carrying out mutation on the resource scheduling scheme to be mutated according to a mutation operator in the mutation crossover operator to obtain a new resource scheduling scheme.

5. The method of claim 4, wherein the first weighted difference is calculated by:

A₁＝λ_i1*|f₁(x_i)-z₁|；

the calculation formula of the second weighted difference is as follows:

A₂＝λ_i2*|f₂(x_i)-z₂|；

wherein, A is₁Representing the first weighted difference; a is described₂Representing the second weighted difference; said lambda_i1An ith weight vector representing that the ith resource scheduling scheme corresponds to the first objective function; said lambda_i2An ith weight vector representing that the ith resource scheduling scheme corresponds to a second objective function; said x_iRepresenting the ith resource scheduling scheme; z is₁Representing the updated first minimum function value; the above-mentionedz₂Representing the updated second minimum function value.

6. The method according to claim 4, wherein the mutating the resource scheduling scheme to be mutated according to a mutation operator in the mutation crossover operator to obtain a new resource scheduling scheme comprises:

for each material to be scheduled of the material supply points, when the supplied amount of the material to be scheduled by the material supply points in the resource scheduling scheme to be varied is equal to the owned amount and the demand of the disaster point for the material to be scheduled is not met, calculating the variation of supplying the material to be scheduled to each disaster point by using a first variation operator, and adjusting the resource scheduling scheme to be varied according to the variation to obtain a new resource scheduling scheme;

for each material to be scheduled of the material supply points, when the supplied amount of the material to be scheduled by the material supply points is smaller than the owned amount and the demand of the disaster point for the material to be scheduled is met in the resource scheduling scheme to be mutated, calculating the variable amount of the material to be scheduled supplied to the disaster point by the material supply points except for the material supply points by using a second mutation operator, and adjusting the resource scheduling scheme to be mutated according to the variable amount to obtain a new resource scheduling scheme;

and aiming at each material to be scheduled of the material supply points, when the supplied quantity of the material supply points to be scheduled is equal to the owned quantity and the demand of the disaster point to the material to be scheduled is met in the resource scheduling scheme to be varied, calculating the variable quantity of the material to be scheduled supplied to each disaster point by each material supply point by using a third variation operator, and adjusting the resource scheduling scheme to be varied according to the variable quantity to obtain a new resource scheduling scheme.

7. An apparatus for acquiring a resource scheduling scheme, the apparatus comprising:

a first obtaining module, configured to obtain resource scheduling data; wherein the resource scheduling data comprises: the method comprises the steps of obtaining a first quantity of material supply points, a second quantity of disaster-affected points, a preset maximum time length, a type quantity of materials to be scheduled, a demand quantity of each disaster-affected point for each type of materials to be scheduled, a disaster-affected grade of each disaster-affected point, a third quantity of each type of materials to be scheduled which can be loaded by each vehicle, a distance between each material supply point and each disaster-affected point, a road congestion coefficient and a resource scheduling speed;

the second obtaining module is used for obtaining a first objective function and a second objective function for dealing with the scheduling of the disaster-suffered resources; wherein the first objective function is a function constructed by taking the first amount, the second amount, the category amount, the demand amount, the disaster level, the distance, the road congestion coefficient, the resource scheduling speed, the preset maximum duration, and the amount of each kind of the goods to be scheduled supplied to each disaster point by each goods and materials supply point as parameters, and taking the satisfaction degree of resource scheduling time as an objective; the second objective function is a function which is constructed by taking the first quantity, the second quantity, the distance, the third quantity and the quantity of supplying each kind of goods to be scheduled to each disaster-stricken point by each goods and materials supply point as parameters and takes the optimal resource scheduling cost as an objective;

and the optimization module is used for optimizing the first objective function and the second objective function according to the resource scheduling data and a decomposition-based multi-objective evolutionary MOEA/D algorithm to obtain a target scheduling scheme for each material supply point to schedule the materials to be scheduled for each disaster-affected point.

8. The apparatus of claim 7, wherein the first objective function is:

the second objective function is:

Representing the average speed of the resource transport; the mu_i,jRepresenting a road congestion coefficient between the ith material supply point and the jth disaster-stricken point; the i represents the ith material supply point, wherein i is more than 0 and less than or equal to n; j represents the jth disaster-stricken point, wherein j is more than 0 and less than or equal to m; k represents the kth material to be scheduled, wherein k is more than 0 and less than or equal to r; f is₂(x) Representing the second objective function; the above-mentioned

9. The apparatus of claim 8, wherein the optimization module comprises:

the first determining submodule is used for determining a first preset number of weight vectors which are subjected to uniform distribution and determining a second preset number of adjacent weight vectors corresponding to each weight vector;

the second determination sub-simulation is used for determining a constraint condition for scheduling the material to be scheduled from the material supply point to the disaster-affected point;

the selection submodule is used for randomly selecting the first preset number of resource scheduling schemes from the feasible domain defined by the constraint condition to obtain an initialized population; the resource scheduling schemes correspond to the weight vectors one by one;

the calculation submodule is used for inputting each resource scheduling scheme in the initialized population into the first objective function, calculating to obtain a first objective function value corresponding to each resource scheduling scheme, and taking the minimum first objective function value as a first minimum function value; inputting each resource scheduling scheme into the second objective function, calculating to obtain a second objective function value corresponding to each resource scheduling scheme, and taking the minimum second objective function value as a second minimum function value;

the iteration submodule is used for adding one to the iterated times when the iterated times are smaller than the preset iterated times, and executing the following operations:

and the output sub-module is used for outputting the target scheduling scheme when the iterated times are equal to the preset iterated times.

10. The apparatus of claim 9, wherein the iteration sub-module is specifically configured to: