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.
Mots-clés : Improved cuckoo search algorithm, genetic algorithm, Markov chain Monte Carlo procedure, stochastic demand, inventory control
@article{RO_2018__52_2_473_0,
author = {Jamali, Gholamreza and Sana, Shib Sankar and Moghdani, Reza},
title = {Hybrid improved cuckoo search algorithm and genetic algorithm for solving {Markov-modulated} demand},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {473--497},
publisher = {EDP-Sciences},
volume = {52},
number = {2},
year = {2018},
doi = {10.1051/ro/2017076},
mrnumber = {3880539},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro/2017076/}
}
TY - JOUR AU - Jamali, Gholamreza AU - Sana, Shib Sankar AU - Moghdani, Reza TI - Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2018 SP - 473 EP - 497 VL - 52 IS - 2 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2017076/ DO - 10.1051/ro/2017076 LA - en ID - RO_2018__52_2_473_0 ER -
%0 Journal Article %A Jamali, Gholamreza %A Sana, Shib Sankar %A Moghdani, Reza %T Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand %J RAIRO - Operations Research - Recherche Opérationnelle %D 2018 %P 473-497 %V 52 %N 2 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro/2017076/ %R 10.1051/ro/2017076 %G en %F RO_2018__52_2_473_0
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://archive.numdam.org/articles/10.1051/ro/2017076/
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