Multi-objective Evolutionary Approaches for Solving the Hybrid Flowshop Scheduling Problem
| Autor: | Daniel Mendoza-Casseres, Miguel Rojas-Santiago, Fabricio Niebles-Atencio |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Schedule Mathematical optimization Job shop scheduling Computer science Ant colony optimization algorithms Tardiness Sorting 02 engineering and technology Multi-objective optimization Set (abstract data type) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Metaheuristic |
| Zdroj: | Computer Aided Systems Theory – EUROCAST 2019 ISBN: 9783030450922 EUROCAST (1) |
| DOI: | 10.1007/978-3-030-45093-9_54 |
| Popis: | In this paper we schedule a set of jobs on a production system with more than one stage and several machines in parallel per stage, considering multiple objectives to be optimized. This problem is known as the Flexible or Hybrid Flowshop Scheduling problem (HFSP), which is NP-hard even for the case of a system with only two processing stages where one stage contains two machines and the other stage contains a single machine. In that sense, it is possible to find an optimal solution for this problem with low computing resources, only for small instances which, in general, do not reflect the industrial reality. For that reason, the use of meta-heuristics as an alternative approach it is proposed with the aim to determine, within a computational reasonable time, the best assignation of the jobs in order to minimize the makespan, total tardiness and the number of tardy jobs simultaneously. In this regard, a Multi-Objective Ant Colony Optimization algorithm (MOACO) and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are used for solving this combinatorial optimization problem. Results show the effectiveness of the approaches proposed. |
| Databáze: | OpenAIRE |
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