Journal
MATHEMATICAL PROBLEMS IN ENGINEERING
Volume 2018, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2018/8574892
Keywords
-
Funding
- Training Foundation of Shandong Natural Science Foundation of China [ZR2016GP02]
- National Natural Science Foundation Project of China [61403180]
- Project of Shandong Province Higher Educational Science and Technology Program [J17KA199]
- Special Research and Promotion Program of Henan Province [182102210257]
- Project of Henan Province Higher Educational Key Research Program [16A120011]
- Talent Introduction Research Program of Ludong University [32860301]
Ask authors/readers for more resources
Workshop scheduling has mainly focused on the performances involving the production efficiency, such as times and quality, etc. In recent years, environmental metrics have attracted the attention of many researchers. In this study, an energy-efficient job shop scheduling problem is considered, and a grey wolf optimization algorithm with double-searching mode (DMGWO) is proposed with the objective of minimizing the total cost of energy-consumption and tardiness. Firstly, the algorithm starts with a discrete encoding mechanism, and then a heuristic algorithm and the random rule are employed to implement the population initialization. Secondly, a new framework with double-searching mode is developed for the GWO algorithm. In the proposed DMGWO algorithm, besides of the searching mode of the original GWO, a random seeking mode is added to enhance the global search ability. Furthermore, an adaptive selection operator of the two searching modes is also presented to coordinate the exploration and exploitation. In each searching mode, a discrete updating method of individuals is designed by considering the discrete characteristics of the scheduling solution, which can make the algorithm directly work in a discrete domain. In order to further improve the solution quality, a local search strategy is embedded into the algorithm. Finally, extensive simulations demonstrate the effectiveness of the proposed DMGWO algorithm for solving the energy-efficient job shop scheduling problem based on 43 benchmarks.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available