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Solving the C sum Permutation Flowshop Scheduling Problem by Genetic Local Search
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yamada, Takeshi Reeves, Robert |
| Copyright Year | 1998 |
| Abstract | In this paper a new metaheuristic method is proposed to solve the classical permutation flowshop scheduling problem with the objective of minimizing sum of completion times. The representative neighbourhood combines the stochastic sampling method mainly used in Simulated Annealing and the best descent method elaborated in Tabu Search and integrates them naturally into a single method. The method is further extended into the Genetic Local Search framework by using a population and a special crossover operator called multi-step crossover fusion. Computational experiments using benchmark problems demonstrate the effectiveness of the proposed method. Keywords— flowshop scheduling, genetic algorithms, tabu search, stochastic sampling, path relinking |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.kecl.ntt.co.jp/as/members/yamada/csum.ps.gz |
| Alternate Webpage(s) | http://www.kecl.ntt.co.jp/as/members/yamada/csum.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Benchmark (computing) Computation Experiment Genetic algorithm Local search (constraint satisfaction) Local search (optimization) Metaheuristic Numerous Pseudorandom permutation Sampling (signal processing) Sampling - Surgical action Scheduling (computing) Scheduling - HL7 Publishing Domain Simulated annealing Tabu search Unified Framework |
| Content Type | Text |
| Resource Type | Article |
