Name
Abstract | ||
|---|---|---|
In this paper, a hybrid metaheuristic method for the job shop scheduling problem is proposed. The optimization criterion is the minimization of makespan and the solution method consists of three components: a Differential Evolution-based algorithm to generate a population of initial solutions, a Variable Neighbourhood Search method and a Genetic Algorithm to improve the population; the latter two are interconnected. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high quality solutions in short computational times using fixed parameter settings. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1057/palgrave.jors.2602534 | JORS |
Keywords | Field | DocType |
inventory,management science,information systems,reliability,operational research,forecasting,investment,production,communications technology,location,computer science,marketing,project management,scheduling,information technology,operations research,logistics | Search algorithm,Job shop scheduling,Evolutionary algorithm,Computer science,Job shop,Flow shop scheduling,Differential evolution,Genetic algorithm,Operations management,Metaheuristic | Journal |
Volume | Issue | ISSN |
60 | 2 | 0160-5682 |
Citations | PageRank | References |
9 | 0.54 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. I. Zobolas | 1 | 92 | 3.72 |
| Christos D. Tarantilis | 2 | 679 | 33.69 |
| George Ioannou | 3 | 106 | 11.99 |