Khan et al., 2011 - Google Patents
A multi-objective simulated annealing algorithm for permutation flow shop scheduling problemKhan et al., 2011
- Document ID
- 16226821113551861675
- Author
- Khan B
- Govindan K
- Publication year
- Publication venue
- International Journal of Advanced Operations Management
External Links
Snippet
Exact and heuristic algorithms have been proposed over the years for solving static permutation flow shop scheduling problems with the objectives of minimising makespan, total tardiness, and total flow time, etc. Of late, attempts are being made to consider more …
- 101700050571 SUOX 0 title abstract description 28
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0631—Resource planning, allocation or scheduling for a business operation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chaudhry et al. | A research survey: review of flexible job shop scheduling techniques | |
Karimi et al. | Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach | |
Kheiri et al. | A sequence-based selection hyper-heuristic utilising a hidden Markov model | |
Wang et al. | Simulated Annealing‐Based Krill Herd Algorithm for Global Optimization | |
Fan et al. | A decreasing inertia weight particle swarm optimizer | |
Shao et al. | Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem | |
Liu et al. | Generative adversarial construction of parallel portfolios | |
Tao et al. | A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow | |
Zhang et al. | UCPSO: A uniform initialized particle swarm optimization algorithm with cosine inertia weight | |
Genova et al. | A survey of solving approaches for multiple objective flexible job shop scheduling problems | |
Lin | Particle swarm optimization algorithm for unrelated parallel machine scheduling with release dates | |
Assareh et al. | Forecasting energy demand in Iran using genetic algorithm (GA) and particle swarm optimization (PSO) methods | |
Tran et al. | Solving resource-constrained project scheduling problems using hybrid artificial bee colony with differential evolution | |
Nikoofal Sahl Abadi et al. | Multiobjective model for solving resource‐leveling problem with discounted cash flows | |
Peng et al. | Genetic Algorithm‐Based Task Scheduling in Cloud Computing Using MapReduce Framework | |
El-Shorbagy et al. | Constrained multiobjective equilibrium optimizer algorithm for solving combined economic emission dispatch problem | |
Xuan et al. | An Improved Discrete Artificial Bee Colony Algorithm for Flexible Flowshop Scheduling with Step Deteriorating Jobs and Sequence‐Dependent Setup Times | |
Ali et al. | An efficient differential evolution algorithm for solving 0–1 knapsack problems | |
Khalilzadeh et al. | A modified PSO algorithm for minimizing the total costs of resources in MRCPSP | |
Zhang et al. | Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling | |
Engin et al. | A fuzzy logic based methodology for multi-objective hybrid flow shop scheduling with multi-processor tasks problems and solving with an efficient genetic algorithm | |
Khan et al. | A multi-objective simulated annealing algorithm for permutation flow shop scheduling problem | |
Wang et al. | A novel memetic algorithm based on decomposition for multiobjective flexible job shop scheduling problem | |
Xu et al. | Hybrid discrete differential evolution algorithm for lot splitting with capacity constraints in flexible job scheduling | |
Xu et al. | A Multistrategy‐Based Multiobjective Differential Evolution for Optimal Control in Chemical Processes |