Method for storing batch objects through batch containers

An object and container technology, applied in the field of computer optimization, can solve problems such as box waste, reduce the number of boxes, improve the utilization rate of boxes, and improve the effect of boxing

Active Publication Date: 2021-10-08
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AI-Extracted Technical Summary

Problems solved by technology

But since the size of the item is randomly generated, when t i (No more than ) of type a i When packing, if there is no K-t i K-t i For items of type -1, a i Can only fit into B with Next Fit 2 , but apparently a i with K-t i Items of type -1 have a utilization ...
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Method used

The advantage that the present invention implements: in order to promote the boxing effect under uniform distribution, reduce the number of boxes, improve the utilization rate of boxing, in combination with the advantages and defects of sub-assembly boxes, a more practical and more detailed sub-assembly is proposed Box rules, making more flexible settings for the types of items that can be adapted and the number of items in the adaptation box. On the premise of ensuring the linearity of the algorithm, the binning effect of the algorithm is improved, which is helpful for the practical application and theoretical research of the binning algorithm. Aiming at the bin packing problem where the size of the items obeys the uniform distribution, a linear time algorithm for binning two boxes and items in pairs is proposed. By controlling the number of i...
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The invention discloses a method for storing batch objects through batch containers. The method comprises the following steps: acquiring information of to-be-stored objects and information of to-be-stored containers; grouping a plurality of containers, performing interval division on the capacity of a single container, and defining the type of a to-be-stored object in combination with the information of the to-be-stored object; obtaining the type number of containers which can be adapted to the object; obtaining the maximum number of objects which can be placed in a single container; constructing an adaptation algorithm of the to-be-stored objects and the containers on the basis of the type number of the containers which can be adapted to the objects and the maximum number of the objects which can be placed in a single container, and storing the to-be-stored objects into the adapted containers after the to-be-stored objects are adapted to the containers in a selected container group; and storing the unadapted to-be-stored objects in the containers in the non-selected container group by using the adaptation algorithm. The operation time and effect of the algorithm can be balanced, and a storage result with better comprehensive performance is obtained.

Application Domain

LogisticsComplex mathematical operations

Technology Topic

Process engineeringManufacturing engineering +1


  • Method for storing batch objects through batch containers
  • Method for storing batch objects through batch containers
  • Method for storing batch objects through batch containers


  • Experimental program(2)
  • Effect test(1)

Example Embodiment

[0107] Embodiment one
[0108] like figure 1 As shown, an online binning method OnGPA, that is, the algorithm presses a 1 ,...,a n Pack each item in the order of , and in the pair a 1 When packing a 1 ,…,a i-1 The boxing position of w cannot be changed, w i+1 ,...,w n Sequence unknown. It is stipulated here that K is an odd number. The method mainly includes the following steps:
[0109] S101, K=K+1-K%2, set the iteration variable i=1.
[0110] S102. When i≤n, execute S103 for a i Carry out packing, otherwise execute S110.
[0111] S103. Calculate a i of type t if Execute S104, otherwise execute S105.
[0112] S104, open an empty box b, a i put into b, add b to B 1 end of , assuming b is B 1In the p-th box, add p to the sequence Q t in, that is, Q t Recorded B 1 The subscript of the box whose type is t, go to S109.
[0113] S105, record y=K-t, Follow Q y ,...,Q z Sequentially find the first non-empty set Q x.
[0114] S106. If there is x that satisfies the condition, execute S107; otherwise, execute S108.
[0115] S107, delete Q x In the first element p, a i put in B 1,p , and add p to , that is, according to the present B 1,p type, add its serial number to the new Q sequence, and execute S109.
[0116] S108. Using the Next Fit strategy to a i load B 2 In the sequence box, execute S109.
[0117] S109, i=i+1, execute S102.
[0118] S110,|B 1 |+|B 2 | is the packing number of the algorithm.
[0119] In OnGPA, h∈(1, 10] is a definite input, specifically each type is The items can fit y-z+1 types of boxes, corresponding to step S105. The parameter m=+∞, that is, the adapted box will be re-added to the new Q sequence, and can still accommodate other elements, which makes B 1 Any number of items can be placed in the box in , which corresponds to step S107. It can be seen from the above process that the time complexity of the algorithm is O(hn).

Example Embodiment

[0120] Embodiment two
[0121] like figure 2 As shown, an offline box packing method OffGPA, that is, before loading items, the sequence w 1 ,...,w n It is known.
[0122] according to a 1 ,...,a n Each item is boxed in order of . I x The definition of (1≤x≤K), for all items a with 1≤i≤n i type of t i. The method mainly includes the following steps:
[0123] S101. For all 1≤i≤n, calculate a i type of t i.
[0124] S102. Command set S x ={i|t i =x}, representing the set of subscripts of items of type x. will satisfy |S x |>0 and The x is inserted into a balanced binary tree KTree, where |S x | represents the set S x the number of elements. The tree records those types that are small (no more than ) and a collection of non-empty types.
[0125] S103. Pack items of type K, that is, for all i∈S K , will a i Pack into an empty box b, put b into B 1 tail of , and close b.
[0126] S104. Let the iteration variable t=K-1.
[0127] S105, when , execute S106 for S t The elements in the box are boxed, otherwise go to S114.
[0128] S106. If|S t |>0, go to S107, otherwise go to S113.
[0129] S107, delete S t In an element i, a i Pack into an empty box b. At this time, the type of items that box b can fit is e=K-t(b), and the number of items in b is f=1.
[0130] S108. When f≤m, execute S109; otherwise, execute S106.
[0131] S109. Find the maximum value y not exceeding e in the KTree.
[0132] S110. If y is found, execute S111; otherwise, execute S106.
[0133] S111, from S y delete an element j in the j Pack into box b. if|S y |=0, delete y from KTree.
[0134] S112, e=K-t(b), f=f+1, go to S108.
[0135] S113, t=t-1, execute S105.
[0136] S114. If t≥1, execute S115, otherwise execute S117.
[0137] S115. For each j∈S t , using First Fit to place a j load B 2 middle.
[0138] S116, t=t-1, execute S114.
[0139] S117,|B 1 |+|B 2 | is the packing number of the algorithm.
[0140] In OffGPA, h=+∞, that is, there is no requirement for the lower limit of the fit between the item and the box, and the type is The box of can accommodate items of type K—t, . . . , 1, corresponding to step S109. Another parameter m ∈ [2, 100] is specified, that is, the number of items in the adapted box does not exceed m, corresponding to step S108. It can be seen from the above process that the time complexity of the algorithm is O((mlogK)n).
[0141] The following are the numerical experimental results of OnGPA and OffGPA. This part is mainly composed of three parts, namely the determination of the input parameters h and m, the comparison of the experimental results of boxing, and the determination of the parameter K. The implementation language is C++, and the test machine is IntelCeleron 1005M 1.9GHz, 12GB RAM. By default, each experiment is done 10 times, and the recorded data is the average value.


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