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

Kristina Yancey Spencer (), Pavel V. Tsvetkov () and Joshua J. Jarrell ()
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Kristina Yancey Spencer: Texas A&M University, 3133 TAMU
Pavel V. Tsvetkov: Texas A&M University, 3133 TAMU
Joshua J. Jarrell: Oak Ridge National Laboratory

Journal of Heuristics, 2019, vol. 25, issue 1, No 1, 45 pages

Abstract: Abstract 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.

Keywords: Dynamic bin packing problem; Multiobjective combinatorial optimization; Metaheuristics; Memetic algorithms (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10732-018-9382-0

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