A greedy memetic algorithm for a multiobjective dynamic bin packing problem for storing cooling objects

In this paper, a multiobjective dynamic bin packing problem for storing cooling objects is introduced along with a metaheuristic designed to work well in mixed-variable environments. The dynamic bin packing problem is based on cookie production at a bakery, where cookies arrive in batches at a cooli...

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Vydané v:Journal of heuristics Ročník 25; číslo 1; s. 1 - 45
Hlavní autori: Spencer, Kristina Yancey, Tsvetkov, Pavel V., Jarrell, Joshua J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.02.2019
Springer Nature B.V
Predmet:
Artificial Intelligence
Benchmarks
Boxes
Calculus of Variations and Optimal Control; Optimization
Cookies
Cooling
Greedy algorithms
Heuristic methods
Management Science
Mathematics
Mathematics and Statistics
Multiple objective analysis
Operations Research
Operations Research/Decision Theory
Packing problem
Memetic algorithms
Multiobjective combinatorial optimization
Dynamic bin packing problem
Metaheuristics
ISSN:1381-1231, 1572-9397
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Shrnutí:In this paper, a multiobjective dynamic bin packing problem for storing cooling objects is introduced along with a metaheuristic designed to work well in mixed-variable environments. The dynamic bin packing problem is based on cookie production at a bakery, where cookies arrive in batches at a cooling rack with limited capacity and are packed into boxes with three competing goals. The first is to minimize the number of boxes used. The second objective is to minimize the average initial heat of each box, and the third is to minimize the maximum time until the boxes can be moved to the storefront. The metaheuristic developed here incorporated greedy heuristics into an adaptive evolutionary framework with partial decomposition into clusters of solutions for the crossover operator. The new metaheuristic was applied to a variety benchmark bin packing problems and to a small and large version of the dynamic bin packing problem. It performed as well as other metaheuristics in the benchmark problems and produced more diverse solutions in the dynamic problems. It performed better overall in the small dynamic problem, but its performance could not be proven to be better or worse in the large dynamic problem.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-018-9382-0