Abstract
Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers’ perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision.
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Acknowledgements
This research is partially supported by National Natural Science Foundation of China (72172022), Humanities and Social Sciences Foundation of the Chinese Ministry of Education (21YJC630016), Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJQN202200749), and Open Fund of Chongqing Intelligent Supply Chain Engineering and Technology Research Center (OFCISCETRC23203).
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Cui, L., Wang, J., Li, S. et al. A hybrid firefly particle swarm for low-temperature dairy items assignment considering shelf display space profit disparities and two-phase demand. Soft Comput 27, 17971–17989 (2023). https://doi.org/10.1007/s00500-023-09005-y
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DOI: https://doi.org/10.1007/s00500-023-09005-y