.png){kind=icon}
.png){kind=icon}
Return
Solving multi-objective production scheduling problems using metaheuristics
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2003.08.029.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Most of research in production scheduling is concerned with the optimization of a single criterion. However the analysis of the performance of a schedule often involves more than one aspect and therefore requires a multi-objective treatment. In this paper we first present (Section 1) the general context of multi-objective production scheduling, analyze briefly the different possible approaches and define the aim of this study i.e. to design a general method able to approximate the set of all the efficient schedules for a large set of scheduling models. Then we introduce (Section 2) the models we want to treat-one machine, parallel machines and permutation flow shops-and the corresponding notations. The method used-called multi-objective simulated annealing-is described in Section 3. Section 4 is devoted to extensive numerical experiments and their analysis. Conclusions and further directions of research are discussed in the last section. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
production scheduling
multi-objective optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
No organization information available