Fast and meta-heuristics for common due-date assignment and scheduling on parallel machines
- Authors
- Kim, Jun-Gyu; Kim, Ji-Su; Lee, Dong-Ho
- Issue Date
- Oct-2012
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- parallel machine scheduling; common due-date assignment; fast heuristics; meta-heuristics
- Citation
- INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v.50, no.20, pp.6040 - 6057
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Volume
- 50
- Number
- 20
- Start Page
- 6040
- End Page
- 6057
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/36308
- DOI
- 10.1080/00207543.2011.644591
- ISSN
- 0020-7543
- Abstract
- This study considers common due-date assignment and scheduling on parallel machines. The problem has three decision variables: assigning the common-due-date, allocating jobs to parallel machines, and sequencing the jobs assigned to each machine. The objective is to minimise the sum of due-date assignment, earliness and tardiness penalties. A mathematical programming model is presented, and then two types of heuristics are suggested after characterising the optimal solution properties. The two types of heuristics are: (a) a fast two-stage heuristic with obtaining an initial solution and improvement; and (b) two meta-heuristics, tabu search and simulated annealing, with new neighbourhood generation methods. Computational experiments were conducted on a number of test instances, and the results show that each of the heuristic types outperforms the existing one. In particular, the meta-heuristics suggested in this study are significantly better than the existing genetic algorithm.
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