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WO2001027567A2 - Procede et appareil de mise en lots d'articles - Google Patents

Procede et appareil de mise en lots d'articles Download PDF

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Publication number
WO2001027567A2
WO2001027567A2 PCT/IS2000/000009 IS0000009W WO0127567A2 WO 2001027567 A2 WO2001027567 A2 WO 2001027567A2 IS 0000009 W IS0000009 W IS 0000009W WO 0127567 A2 WO0127567 A2 WO 0127567A2
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WO
WIPO (PCT)
Prior art keywords
items
batch
item
measure
assigning
Prior art date
Application number
PCT/IS2000/000009
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English (en)
Other versions
WO2001027567A3 (fr
Inventor
Pall Jensson
Original Assignee
Marel Hf.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Marel Hf. filed Critical Marel Hf.
Priority to EP00962797A priority Critical patent/EP1222443A2/fr
Priority to AU74436/00A priority patent/AU7443600A/en
Publication of WO2001027567A2 publication Critical patent/WO2001027567A2/fr
Publication of WO2001027567A3 publication Critical patent/WO2001027567A3/fr
Priority to IS6325A priority patent/IS6325A/is
Priority to NO20021523A priority patent/NO20021523L/no

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/387—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for combinatorial weighing, i.e. selecting a combination of articles whose total weight or number is closest to a desired value
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/22—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for apportioning materials by weighing prior to mixing them
    • G01G19/24—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for apportioning materials by weighing prior to mixing them using a single weighing apparatus
    • G01G19/30—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for apportioning materials by weighing prior to mixing them using a single weighing apparatus having electrical weight-sensitive devices
    • G01G19/303—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for apportioning materials by weighing prior to mixing them using a single weighing apparatus having electrical weight-sensitive devices involving digital counting

Definitions

  • the present invention relates to methods for batching items of irregular weights into portions of a substantially uniform weight.
  • the invention relates to a method wherein items is assigned to a bin based on a test fitting of real items with a known weight measure combined with a test fitting of imaginary items with imagined weight measures. This enables an improved the uniformity of the portions without having to expand the number of real items with known weight measures.
  • the raw material typically results in irregularities in size, shape and weight of items flowing through the processing system. Even though the raw material is mechanically cut into pieces of an intended size there are often great variations in size of the items. An example of this is the cutting of fish fillets into pieces.
  • the items are collected into portions of a specified size. In most cases it is only the portion weight (in grams) that is specified, but other specifications could be included. For many market areas, it is required that the weight of each portion is never under the specified target weight. This means that, because of the irregularities in item weights, there will always be some overweight.
  • the goal of the methods described here is to keep the overweight as low as possible with respect to the limits set by the required speed and the available information.
  • the items arrive sequentially on a conveyor belt and pass an electronic scale that records the item weight and sends it to a computer.
  • Overweight may preferably be expressed in terms of how much a package weight exceeds a given minimum target weight.
  • the problem in relation to the present invention differs from the above mentioned "Bounded-Space Online Dual Bin Packing" problem in the sense that in batching of items according to the present invention it is not possible - or at least not practical - to put items on hold which is a fundamental characteristic of the Packing methods. In these methods information relating to a large number of next arriving items is exploited to pack the items optimally which includes selection of the items in an order being different from the order they arrive in, i.e. the batching is not done sequentially.
  • a technical problem in connection with batching of items is therefore that the items must be assigned either to a bin or a rejection position as they arrive. No information is available for a large number of next arriving items, so that the assigning may only be based on one item - or at least a few numbers of next arriving items. This problem becomes even bigger when the assigning takes place as a step included in processing of food, as the items next arriving item may not be provided when an item is to be assigned.
  • the grader could be of a regular type adapted for conveying food items from an in-feed area to a selected batch, the food items being conveyed e.g. on a regular conveyor belt.
  • a measure is being determined and recorded for each food item.
  • the measure could be the weight of the food items, the size of the food items, a specific colour or any other characteristics of the food related to a criterion for batching.
  • the criterion for batching could as an example be to combine food items so that the weight of the batch of items are within a certain weight zone or so that the weight of the batch is as close as possible to a preferred weight zone.
  • the criteria could also be related to the quality of the food items, e.g.
  • the criteria could be that the food items of a certain criteria should be distributed as uniformly as possible, or that some batches should have all the items of a good quality while others should have all the items of a less good quality.
  • the criteria could also be related to the size of specific food items or even be related to the shape of specific food items.
  • the criterion may even be related to a number of dependent or independent measures of the batch or of the items included in the batch.
  • the criteria could be that the parts should have individual shapes and the total weight of the batch should be within a certain weight zone. This could be the case, e.g. for batching pieces from a chicken into a portion wherein the portion preferably should contain at least one of each part of a chicken i.e. at least one wing, one breast, one tender etc.
  • the imaginary portions are portions which are defined without the food items being moved to an actual location of a portion.
  • the imaginary portions may exist for a very short moment until they have been evaluated based on the criteria for the batches. If they do ⁇ not fulfil a certain criteria, new imaginary portions may be formed. When a satisfactory result has been reached the items can be located so as to form real batches according to the imaginary portions.
  • the items being used for forming the imaginary portions are taken from one pool of real items that have been measured and from another pool of imaginary items.
  • the pool of imaginary items is defined by generating measures for that pool of items.
  • the measures may be generated in various ways. As an example the measures may be generated based on knowledge of recorded measures of previous real items.
  • the known distribution of earlier weighed items could as an example include the last 100 recorded pieces or the last 1000 recorded pieces or even more than 10000 recorded pieces. As an example it may be chosen to generate a measure for a number N equal to 50 food items. To do so a known distribution including 10000 food items is divided into 50 2-percentile distributions and for each of the 50 2-percentile distributions a measure is defined as the average of the percentile.
  • the sample of items could be the latest 100, 1000 or even the latest 10000 recorded items.
  • the batches being filled may be short of a total of 20 kilo.
  • the average size of the latest 10000 recorded items was 0,5 kilo. Therefore at least 40 items should be included in the pool B of imaginary items.
  • the overhead may be necessary due to the fact that some of the batches will always be overfilled or filled with items witch are not 100 percent suited for the batch. As an example 10 batches may be short of 20 kilo of meat. Some of these batches will be filled with more meat than necessary, e.g. due to the nature of pieces of meat having different size and weight. Therefore it may be necessary to use a number which in average provides e.g. 21 kilo of meat.
  • the measure of the number N of items to include in the pool B of items could also be generated by:
  • the measure of an imaginary item could also be generated by stochastically simulating the measure by the use of an empirical distribution of the previously recorded items and assigning the simulated measure to the imaginary item of the pool B.
  • the selection of combinations may be done e.g. by Dynamic Programming, Enumeration - - just trying all combinations, Genetic Algorithm, Branch and Bounds Algorithms or by means of any Heuristic methods or by means of neural networks.
  • the selection criterion could as an example be selection of combinations with a minimum overweight or selection of batches closest possible to a target weight, selection of batches with most equally distributed sizes of the items etc.
  • the batches could be arranged from the most filled batch to the least filled batch. After the batches have been arranged the selection of combinations may start with the most filled batch towards the least filled batch.
  • the batches may be arranged from the least filled batch to the most filled batch.
  • the measure of the items is being recorded continuously as they are conveyed from the in-feet area to the grading area.
  • the assigning of combinations of the items of the imaginary item pool preferably comprises the step of: - moving the first assigned item to the batch it was assigned to and redoing the assigning of combinations each time the measure of a new piece has been recorded.
  • a post processing method may therefore preferably be adapted after the assigning of the combinations.
  • the invention relates to a method for assigning at least one item having at least one characteristic property to a bin comprised in a group of bins comprising at least one bin, wherein requirement(s) having been made as to an allowable batch size of each bin in terms of for instance a filling zone, a group of items comprising pretended and optionally also detected items having been provided and characteristic property/properties of at least one item to be test-assigned having been provided.
  • the requirements provided set for instance the maximum and minimum batch size of each bin and determine thereby the quality of the batching.
  • Other useable requirements may preferably be the number of items in each bin for instance combined with size (for instance in meters) and weight of the bin.
  • the platform further comprises a group of items, which mimic/represents characteristics of items to be assigned, on the basis of for instance earlier detected items, and atleast one item to be test assigned.
  • the item to be test- assigned is not necessarily divided from the group of item, but may just as be included into the group of items to be test-assigned.
  • the only crucial feature in this connection is that characteristic properties/property of at least one items to be test-assigned must be provided.
  • the item to be test-assigned is preferably the most recently detected item, which initially is test-assigned to a bin: i.e. the item is assigned to the bin in such a manner that the final assignment of the item is done in a later step.
  • more than one item is considered and in this case these items are considered as one item in the sense that for instance the characteristic properties are added together.
  • the assigning method comprises:
  • a collection of items may, of course, comprise only one item
  • Batch shortage - The amount, for instance expressed in (kilo)grams, that should be placed in a bin for that bin to fulfil a predetermined batch size.
  • Batch size The amount, for instance expressed in kilograms, contained in a bin.
  • Test-assigning Fictively assigning an item to a bin.
  • the method may be viewed upon as a method combining a number of items into groups in such a way that the combination results in a total batch size of the groups, i.e. the sum of the characteristic property of each group, being the smallest possible.
  • the smallest possible batch size will, of course, depend at least on the order of selecting the bins to be test-fitted and on the order of selecting the items to be test-fitted in the bins selected.
  • the items to be test-fitted being selected from the group of items is selected in descending order with respect to the characteristic property/properties of the items, the first selected item is the one having the largest characteristic property/properties and the last item selected last is the item having the smallest characteristic property.
  • the items present in the group of items may both be arranged and selected in descending order with respect to the characteristic property/properties of the items, the first selected item to be test-fitted is the item having the highest characteristic property/properties and wherein the last selected item to be test-fitted is the item having the smallest characteristic property/properties.
  • test-fitting of items into a particular bin may be terminated when the batch size of that particular bin meets the requirements as the remaining items is known to be smaller than the items already test- fitted.
  • the method may further comprise the step of arranging the bins ascending, least batch shortage first, after test- assigning and wherein the bins selected for test-fitting are selected sequentially starting with the bin having the least batch shortage.
  • This step is in a presently most preferred embodiment of the present invention, included together with the arranging/selecting step of the items in descending order.
  • This arranging/ordering provides a very advantageous method in which the items are combined into the bins so that the combination of items giving the smallest total batch size may be detected.
  • the items of the group of items being pretended must be provided.
  • the assigning method is typically applied in connection with a stream of items arriving sequentially to a detection station for detection and record of a number of characteristic properties detected is preferably being kept
  • the pretended items of the group of items are being provided by a retrospective method.
  • the pretended items are being provided so that the pretended items of the group of items have substantially the same characteristic property/properties as the items assigned most recently.
  • the pretended items of the group of items are being provided by a simulation method.
  • the characteristic properties/property is/are being simulated according to an empirical distribution of the characteristic property/properties of the items assigned most recently.
  • the pretended items of the group of items are being provided by a scenario method, wherein, in a preferred embodiment thereof, the items are being provided so that the histogram of the pretended items is substantially the same as the histogram/empirical of the distribution of the characteristic property/properties of the items assigned most recently.
  • the empirical histogram is recalculated with respect to the desired number of imaginary items that we want to assign weights to. Then we round the number to nearest integer. And then the imaginary items have the weights equal to the intervals.
  • the assigning method according to the present invention is in a presently most preferred embodiment of the present invention applied for assigning items being food stuff such as fish, meat or the like.
  • the method is applied for grading a stream of items into a group of bins comprising at least one bin according to one or more characteristic property of the item (such as weight, volume, size, colour and/or number) by applying the assigning method according to the present invention.
  • the method comprises the steps of:
  • ⁇ conveying the item(s) from a first to a second location, the second position being either a bin or a rejection position;
  • the items to be graded are in a preferred embodiment of the present invention preferably food stuff items such as fish, meat or the like.
  • the present invention relates to an apparatus for batching food items into batches, the apparatus is being adapted to perform the method steps of forming combination and assigning those combinations to batches described above.
  • the present invention rely on the idea of establishing a measure for the ability of being able to fill a group of bins when only a small number - and in some cases - only one item is detected in a stream of items.
  • a measure is according to the present invention established for a group of bins of for instance two bins.
  • the group of bins is test- fitted with items from a group of items comprising generated and known items unless special requirements state different.
  • group of bins comprises two bins, bin A and B.
  • bins A and B are then filled, using a given batching or assigning method, with items comprised in the group of items until an upper limit for the weight of the bin is reached and the total overweight, i.e.:
  • W A ⁇ A refers to the overweight of bin A with known item in bin A and W A B refers to the overweight of bin B with known item in bin A).
  • the known item is test-assigned into bin B and the total overweight, i.e.:
  • FIG. 1 shows schematically a conveying system, in which the method according to the present invention has been incorporated for assigning items to bins,
  • Fig. 2 shows schematically a preferred embodiment of the method according to the present invention
  • Fig. 3 shows a functional diagram of a preferred method for batching items
  • Fig. 4 shows a functional diagram of post processing of the assignment of items to batches
  • Fig. 5 shows a functional diagram of another way of assignment items to batches.
  • the method is based on solving a knapsack problem. Added to the knapsack problem is the requisition that follow the arrangement already described that is the items come in a sequence and discards are not permitted.
  • Martello and Toth were used and adapted to the problem.
  • the adoption consisted mainly of converting the maximums into minimums. Martello ' s and Toth ' s algorithm can briefly be described by refer to the three lowest layers in the table below,
  • each layer calls on the next lower layer.
  • the fourth and the top layer explains when the items arrive in a simple sequence to the grader after having been weighted as described above, wherein said method calls on the adapted algorithm of Martellos and Toth.
  • Select items from set of free items that are marked i sack move selected items from set of free items to set of it fixed items
  • reject package ( select reject package( a reject package is a package that has the least fill) (Take an item from the sequence and put into reject package)
  • the fourth layer can be altered in such a way that it takes all selected items from the front of the sequence all the way to an item that is not selected. This can be done by adding one step 5a):
  • step 5a If the first item in sequence is selected and the last item did not go into a reject package then goes to step 4).
  • reject package can be the package that has the most items. That has not been investigated in the presentation given here.
  • the properties of DSMKP was investigated for different input.
  • the main property of the excess weight are:
  • Number of items in a queue is variable.
  • the length of a queue is dependent how much space is still to fill the packages and the mean weight of the items.
  • the range size is:
  • the queue is longest when all the packages are empty and gets shorter a packages are filled. Also the queue becomes long if the items are small and the packages large. No investigation was made on how it altered with average length of queue (sequence) or how it altered with size of package or mean weight of items.
  • Knapsack problem is in principle parallel selection of items that is to say, it does not matter in what sequence the items are taken from the collection. The same is valid for multiple knapsack problems.
  • the environment and solution that have been described here is an alteration on these and goes in fact across the parallel selection because it requires the items to be selected in a defined sequence.
  • Knapsack problem kp is in principle parallel selection of items that is to say it does not matter in what order the items are taken from the collection. The same is valid for multiple knapsack problem mkp.
  • the environment and the solution described here is an alteration it goes athwart on the unbuild parallel selection of items by demanding that the items are selected in a defined sequence.
  • All genetic algorithms have a defined number of individuals ( ⁇ ) each describing one solution. Each individual is symbolises with a vector and each component in the vector is called gen. The individual is developed through a defined number of generation and in each generation there are cross-overs, mutations and selection of individuals of that generation. Cross-over is the main search index for genetic algorithm and is based on cross-over of parts of gens from two good individuals and it is tried to create a new one that is better. In solving this project it was considered suitable to use uniform cross-over with 40% probability of cross-over. Then there is 40% cross-over of units between the two individuals. Mutation is sometimes omitted when designing genetic algorithms but then the aim is to increase multiplicity of the individuals. Here mutation was used with the probability of 1/m where m is the number of known items and on the average one mutation is performed on each individual.
  • Selection is the most important part of genetic algorithm but it oversees selecting the individuals that are allowed to proceed and form parents for the next generation. It is important that the selection secures that convergence happens sufficiently fast as fast convergence could lead to stationary extreme values.
  • selection When selecting parents for the next generation the skill of all individuals are found and a defined selection method utilised.
  • Tournament selection is based on comparing the skill values of two individuals and select the better one.
  • each individual needs to describe the sorting of the items that are known into the bins of the grader. It was therefore decided that each element in the individual was an integer that indicated a bin in the grader. Location of the element describes what item of those that are known, should go to the bin. As it is necessary to sort the items in the order they arrive to the grader certain restrictions are introduced on the optimising.
  • the optimising is dynamic, as the premise of the optimising is mobile. As soon as the foremost item is sorted into one of the bins the situation is altered, because at the same time a subsequent item behind the known sequence enters into the known sequence of items for sorting. Taking into consideration that sorting the foremost item is to a large extend depend on what items arrive next after it, it is natural to keep their shorting unaltered.
  • the last generation of sorted items was used as the first generation for shorting item i+1. It proved though necessary to build another sequence for the backmost items to increase the variation in their shorting.
  • batching is performed this way it is possible to increase its speed by self-adaptation in a number of generations.
  • the capacity of genetic algorithm determines to a large extend by the number of generations that is the more generation the longer time it takes to optimise, but at the same time better results can be expected.
  • Self-adaptation in number of generations is used in such a way that at the beginning the number of generation is large but decreases in accordance to what generation gives the best solutions.
  • Tn is integer number of packages formed from known package
  • equation 1 will distribute the items evenly into the bins as the algorithm has no tendency to select one bin above another.
  • equation 1 will contribute to that the bin will be filled quickly as the nominator increases at the same time as a new package is formed.
  • the objective function is independent of the weight distribution of the items and can therefore function for different distributions.
  • the result of the batching will all the same be dependent on the distribution. This can easily be seen by taking on one hand uniform distribution and on the other hand normal distribution with low standard deviation where the mean normal distribution is divided in the packages size.
  • a limiting function that is based only on mean excess weight of the packages is sensitive to the number of items. If for example 5 items are needed on the average to form a package and the number of bins are 6 it is clear that knowledge on the weight of next 10 items is only partly useful. In reality the knowledge is only useful for selecting the last item put in a package. When a bin is empty the algorithm gives no encouragement to select together items that contribute to filling a bin to low excess weight.
  • a trial to solve this problem is to calculate the expected weight of the items that is to be sorted. The 10 its that are known are sorted into the bins and "virtual items" used to fill the bins. The weight of the virtual items is the expected weight at each particular time. Weighted average of the real weight of the items and the mean excess weight that is formed in the bin that is filled up with virtual items then form the objective function. The problem proved to be to determine how high excess weight because of virtual items should be, that is selection of the constant :
  • S h is the number of virtual items needed to fill bin h
  • Tu is the number of unfilled bins
  • Vj is a predetermined weight and P(V,) the probability that weight Vj appears.
  • ⁇ is the expectancy coefficient
  • the expectancy coefficients ( ⁇ ) is dependant on when the package is formed but not if it is formed for example from the 5 first items. If they depended on the 5 first items the algorithm would try to sort them in such a way that they did not fill any bin and thus result in keeping the bins nearly filled.
  • Input Situation in bins, weight of next m known items, minimum size of package, individuals of a generation g.
  • Output Mean excess weight of package for each individual.
  • the result for 600g package shows that the mean excess weight decreased with increasing number of bins for GA 20 and GA 40. Identical results are obtained for other package sizes. If the packages are suitable for the weight distribution of the items the curves lowers for larger packages and become flatter, that is, less difference in excess weight for different number of bins. The result shows that perfect number of bins is primarily dependent on how many items are known. It is notable that mean excess weight lowers at the begin but then increases slightly when a defined number of bins are reached. The reason for this is the same as that for increase in mean excess weight when the packages became very large as effect of increased number bins is the same as the effect of larger packages. It can also be seen that the gain in increasing the number of bins decreases unfailingly until the ideal number of bins is obtained.
  • LPV decides on basis of the items weight and weight distribution.
  • GA knows on one hand the weight of next 20 items and GA 40 the next 4o items and can make a decision based on the items that still are to come.
  • DSMKP utilises also knowledge of the weight of items after the one that is being sorted and the difference of the DSMKP and GA method is small. The explanation may be that the DSMKP edition that the results are based on was not fully arranged and finished.
  • Simulation of batching with objective function 3 when expectancy coefficient is used to increase the relevance of the sorting of the foremost items gives no better results than the objective function 1.
  • the capacity of the method is mainly dependent upon the number of known items. The less items known, the faster is the method. This is explained by that in each round the results have to be optimised for the items that are known and the time needed to calculate increases with increased number of items. Also the number of individuals is increased with increased number of known items as it is necessary to increase the number of individuals as it necessary that is at least equal to the number of units in each individual to give a good solution.
  • Programming and simulation can preferably be performed Matlab 5.3 on Deltacomputer with 450 MHz Pentium III microprocessor and 128 Mb RAM.
  • %average excess weight medal sum(package-lagm)/length(package);
  • %D Sorting of item based on weight
  • %Weight(bin) total weight of items in a bin
  • histogr [(10:10:max dreif)*10)' sum(tidni,2)/length(tidni(1 ,:))];
  • Lamda m + 10;
  • medal sum(pakkning-lagm)/length(packning);
  • H length(vigt(1 ,:);
  • the method according to the present invention makes use of group of items and that group has to be generated.
  • the basic idea of the methods presented herein for few (N1) or only one known item is to "pretend” that we know the next incoming N2 items.
  • This item generation can be done in at least three ways:
  • N N1 + N2
  • N N1 + N2
  • zone Z This means that whenever an item fills a bin within the zone it is accepted for that bin.
  • the choice of Z depends on one hand on the demand or expectations of the user to the worst case packing results and on the other hand on the impact on the average overweight. Too small Z restricts the packing while too high Z would in some cases be unnecessarily pessimistic
  • An example of a method specification could be the following short notation: B-1 -20-10 meaning that we simulate the next (pretended) 20 items (N1 of these are actually detected so we simulate N2) and use heuristics for batching, accepting up to 10 grams overweight.
  • B-1 -20-10 meaning that we simulate the next (pretended) 20 items (N1 of these are actually detected so we simulate N2) and use heuristics for batching, accepting up to 10 grams overweight.
  • C-1-N-Z which uses N pretended items and the zone is Z.
  • this method is described by use of the heuristic batching method. Furthermore, the method is described simplified in the sense that a the characteristic property of an item is considered to be the weight of an item, but as stated in the introduction to the invention the characteristic property of an item considered could just as well be the size, the shape, the colour etc. of the item and also combinations thereof.
  • the simulation method is based on the empirical weight distribution of the previous items (say 500 items). N2 items are chosen randomly from a collection of previous detected and recorded item properties such as weights. The probability of choosing a specific item weight is according to the frequency of this weight in the collection of all previous detected weights.
  • This collection of items should be recalculated periodically or at least when changes are detected in the empirical histogram.
  • the invention is directed to - but not limited to - food processing lines where items in the end of processing are collected into portions of a specified size.
  • the method is applied in connection with a conveyer system comprising a conveyer belt for conveying the item from a first position to a second position, see Fig. 1.
  • the first position is a cutting station where the raw materials for instance hole fish or meat bodies are cut into pieces, items, for instance by hand or by machine.
  • a detection station comprising an electronic scale and a computer for assigning items to bins situated at the second position.
  • the weight of the item is recorded and by use of the computer the item is either assigned to a specific bin or rejected.
  • the method Initially - i.e. the first time the assigning method is applied - the method generates a group of pretended elements from scratch. Such a generation may preferably be based on the retrospective method.
  • the first items detected, say 10, are assigned arbitrary to a bin and the detected characteristic of the items (the weights) are recorded by the computer.
  • a group of pretended items may be generated based on the first detected items and the method may now be fully applicable.
  • the number of items on which the retrospective item generation method is based may be expanded.
  • the item generation method may be shifted to for instance the simulation method or the scenario method if wanted.
  • the method when loaded into the computer may comprise a group of pretended items detected elsewhere or provided in another way, and this initial group of pretended items may then by exchanged as items are detected.
  • bins are to be filled with a large number of items and in this situation the assigning by the method according to the present invention may be by-passed in the beginning of the filling of bins. That means, when an empty bin is to be filled with items, items, which are detected but not processed, is assigned to the bin until the weight of the bin has reached a predetermined limit. The items are then assigned based on the method according to the present invention.
  • items may be passed both non-detected and non-processed to bins until the weights of the bins have reached a predetermined limit.
  • the bins must be weighed in order to register the point where they have reached the predetermined limit and for providing weight input to the method according to the invention.
  • Another method for dealing with bins with a large number of items is to fictively divide each bin into a number K of sub-bins and use the above-described method to fill the sub- bins sequentially.
  • the number K it is preferable to have the sizes of all the sub-bins, or if this is not possible at least the last (highest in the bin) sub-bins, close to the mean value of the weight distribution of items, given that this is a one-peak distribution. This will increase the probability of the bins being filled gradually up in a way so that there will be a great supply of last items that will fit the last sub-bin shortage with low overweight for the bin.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Control Of Non-Electrical Variables (AREA)
  • Polyesters Or Polycarbonates (AREA)

Abstract

Procédé et appareil de regroupement à gestion pondérale d'articles d'un poids non uniforme. Les articles sont affectés à un bac, sur la base d'une correspondance contrôlée d'articles réels avec une mesure pondérale connue combinée à une correspondance contrôlée d'articles imaginaires avec des mesures pondérales imaginées.
PCT/IS2000/000009 1999-09-30 2000-09-29 Procede et appareil de mise en lots d'articles WO2001027567A2 (fr)

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EP00962797A EP1222443A2 (fr) 1999-09-30 2000-09-29 Procede et appareil de mise en lots d'articles
AU74436/00A AU7443600A (en) 1999-09-30 2000-09-29 A method and an apparatus for batching items
IS6325A IS6325A (is) 1999-09-30 2002-03-26 Aðferð og búnaður til flokkunar hluta
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US7828639B2 (en) 2002-03-18 2010-11-09 Scanvaegt International A/S Method and system for monitoring the processing of items
WO2011138052A1 (fr) 2010-05-07 2011-11-10 Marel Hf Procédé et système de tri en fonction du rapport matière grass/chair
CN110935646A (zh) * 2019-11-19 2020-03-31 泰州职业技术学院 基于图像识别的全自动螃蟹分级系统
CN112016862A (zh) * 2019-05-29 2020-12-01 北京京东尚科信息技术有限公司 生成理货任务的方法和装置
US11259531B2 (en) 2015-03-02 2022-03-01 Valka Ehf Apparatus for processing and grading food articles and related methods
US11344036B2 (en) 2015-03-02 2022-05-31 Valka Ehf Apparatus for processing and grading food articles and related methods
US11357237B2 (en) 2015-03-02 2022-06-14 Valka Ehf Apparatus for processing and grading food articles and related methods
US11897703B2 (en) 2019-05-07 2024-02-13 Valka Ehf Conveyor system and method
US11948120B2 (en) 2022-01-31 2024-04-02 Walmart Apollo, Llc Systems and methods of merging retail products between containers to optimize storing capacity of retail storage facilities
US11954641B2 (en) 2022-01-31 2024-04-09 Walmart Apollo, Llc Systems and methods for optimizing space utilization of containers at retail storage facilities

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US4632254A (en) * 1984-02-28 1986-12-30 Pennwalt Corporation Method for adjusting weight breaks reactive to changes in distribution of object weight
WO1996008322A1 (fr) * 1994-09-15 1996-03-21 Scanvaegt A/S Procede et dispositif de division par pesee d'articles de poids non uniforme
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US7828639B2 (en) 2002-03-18 2010-11-09 Scanvaegt International A/S Method and system for monitoring the processing of items
US8091712B2 (en) 2005-09-23 2012-01-10 Marel Food Systems Hf Method for batching items
WO2007034512A1 (fr) * 2005-09-23 2007-03-29 Marel Hf. Procédé de groupage d’articles
EP3906783A1 (fr) 2010-05-07 2021-11-10 Marel HF. Procédé et système de classement de viande/graisse
WO2011138052A1 (fr) 2010-05-07 2011-11-10 Marel Hf Procédé et système de tri en fonction du rapport matière grass/chair
US8820534B2 (en) 2010-05-07 2014-09-02 Marel Hf Fat/meat grading method and system
US9066524B2 (en) * 2010-05-07 2015-06-30 Marel Hf Fat/meat grading method and system
US9439444B2 (en) 2010-05-07 2016-09-13 Marel Hf Fat/meat grading method and system
US11357237B2 (en) 2015-03-02 2022-06-14 Valka Ehf Apparatus for processing and grading food articles and related methods
US11259531B2 (en) 2015-03-02 2022-03-01 Valka Ehf Apparatus for processing and grading food articles and related methods
US11344036B2 (en) 2015-03-02 2022-05-31 Valka Ehf Apparatus for processing and grading food articles and related methods
US11897703B2 (en) 2019-05-07 2024-02-13 Valka Ehf Conveyor system and method
CN112016862A (zh) * 2019-05-29 2020-12-01 北京京东尚科信息技术有限公司 生成理货任务的方法和装置
CN110935646A (zh) * 2019-11-19 2020-03-31 泰州职业技术学院 基于图像识别的全自动螃蟹分级系统
US11948120B2 (en) 2022-01-31 2024-04-02 Walmart Apollo, Llc Systems and methods of merging retail products between containers to optimize storing capacity of retail storage facilities
US11954641B2 (en) 2022-01-31 2024-04-09 Walmart Apollo, Llc Systems and methods for optimizing space utilization of containers at retail storage facilities

Also Published As

Publication number Publication date
NO20021523L (no) 2002-04-15
EP1222443A2 (fr) 2002-07-17
AU7443600A (en) 2001-04-23
IS6325A (is) 2002-03-26
WO2001027567A3 (fr) 2001-09-07
NO20021523D0 (no) 2002-03-26

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