Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand

Gholamreza Jamali, Shib Sankar Sana, Reza Moghdani

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)473-497
Number of pages25
JournalRAIRO - Operations Research
Volume52
Issue number2
DOIs
Publication statusPublished - 1 Apr 2018
Externally publishedYes

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