Volume 52, Number 2, April–June 2018
|Page(s)||473 - 497|
|Published online||22 June 2018|
Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand
Department of Industrial Management, Persian Gulf University,
2 Department of Mathematics, Bhangar Mahavidyalaya, Bhangar 743502, South 24 Parganas, India
3 Department of Industrial Management, Persian Gulf University, Bushehr 7516913817, Iran
* Corresponding author: firstname.lastname@example.org
Accepted: 13 October 2017
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.
Mathematics Subject Classification: 90B05 / 91B74
Key words: Improved cuckoo search algorithm / genetic algorithm / Markov chain Monte Carlo procedure / stochastic demand / inventory control
© EDP Sciences, ROADEF, SMAI 2018
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