A Pareto approach for the multi-factory supply chain scheduling and distribution problem
| Autor: | Ali Gharaei, Fariborz Jolai |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Numerical Analysis Mathematical optimization 021103 operations research business.industry Computer science Strategy and Management Ant colony optimization algorithms Supply chain 0211 other engineering and technologies Pareto principle Computational intelligence 02 engineering and technology Management Science and Operations Research 020901 industrial engineering & automation Computational Theory and Mathematics Management of Technology and Innovation Modeling and Simulation Simulated annealing Factory (object-oriented programming) Local search (optimization) Statistics Probability and Uncertainty Routing (electronic design automation) business |
| Zdroj: | Operational Research. 21:2333-2364 |
| ISSN: | 1866-1505 1109-2858 |
| DOI: | 10.1007/s12351-019-00536-7 |
| Popis: | Integrated decisions in the supply chain are one of the most attractive topics for researchers. But to get closer to the real-world problems, other real assumptions should be considered. One of these assumptions is the multi-agent view in which several sets of customers or agents with their own objective compete with each other to acquire the supply chain resources. Here, an integrated supply chain scheduling problem along with the batch delivery consideration in a series multi-factory environment is investigated and the routing decisions among customers are considered. A mathematical model is presented for this problem. Due to the complexity, a novel ant colony optimization algorithm is developed to obtain Pareto solutions. Also, a simulated annealing based local search is used to improve the quality of solutions. The performance of the algorithm is compared with three well-known multi-objective algorithms. Results show the proper performance of the proposed algorithm compared to the other algorithms. |
| Databáze: | OpenAIRE |
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