Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand
RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 2, pp. 473-497.

One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers’ service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an organization. In this study, a new formulation of an inventory system is analyzed under discrete Markov-modulated demand. We employ simulation-based optimization that combines simulated annealing pattern search and ranking selection (SAPS&RS) methods to approximate near-optimal solutions of this problem. After determining the values of demand, we employ novel approach to achieve minimum cost of total SCM (Supply Chain Management) network. In our proposed approach, hybrid improved cuckoo search algorithm (ICS) and genetic algorithm (GA) are presented as main platform to solve this problem. The computational results demonstrate the effectiveness and applicability of the proposed approach.

DOI : 10.1051/ro/2017076
Classification : 90B05, 91B74
Mots-clés : Improved cuckoo search algorithm, genetic algorithm, Markov chain Monte Carlo procedure, stochastic demand, inventory control
Jamali, Gholamreza 1 ; Sana, Shib Sankar 1 ; Moghdani, Reza 1

1
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     title = {Hybrid improved cuckoo search algorithm and genetic algorithm for solving {Markov-modulated} demand},
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Jamali, Gholamreza; Sana, Shib Sankar; Moghdani, Reza. Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand. RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 2, pp. 473-497. doi : 10.1051/ro/2017076. http://www.numdam.org/articles/10.1051/ro/2017076/

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