Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand
| Autor: | Reza Moghdani, Gholamreza Jamali, Shib Sankar Sana |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Inventory control
Mathematical optimization 021103 operations research Supply chain management Markov chain Computer science Population-based incremental learning 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Pattern search Computer Science Applications Theoretical Computer Science Service level Simulated annealing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Cuckoo search Algorithm |
| Zdroj: | RAIRO - Operations Research. 52:473-497 |
| ISSN: | 1290-3868 0399-0559 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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