A Modified Ant Colony Algorithm to the P| Prec| Cmax Scheduling Problem
| Autor: | Mohamed Messaoudi-Ouchene, Ali Derbala |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Statistics and Probability
Mathematical optimization Control and Optimization Job shop scheduling Computer science Ant colony optimization algorithms Ant colony Longest path problem Computer Science Applications Computational Mathematics Computational Theory and Mathematics Modeling and Simulation Simulated annealing Genetic algorithm Benchmark (computing) Decision Sciences (miscellaneous) Heuristics |
| Zdroj: | International Journal of Applied Metaheuristic Computing. 4:65-74 |
| ISSN: | 1947-8291 1947-8283 |
| DOI: | 10.4018/ijamc.2013070105 |
| Popis: | This paper investigates a comparative study which addresses the P/prec/Cmax scheduling problem, a notable NP-hard benchmark. MLP_SACS, a modified ant colony algorithm, is used to solve it. Its application provides us a better job allocation to machines. In front of each machine, the jobs are performed with three priority rules, the longest path (LP), a modified longest path (MLP) and a maximum between two values (MAX). With these three rules and with both static and dynamic information heuristics called “visibility”, six versions of this ant colony algorithm are obtained, studied and compared. The comparative study analyzes the following four meta-heuristics, simulated annealing, taboo search, genetic algorithm and MLP_SACS (a modified ant colony system), is performed. The solutions obtained by the MLP_SACS algorithm are shown to be the best. |
| Databáze: | OpenAIRE |
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