DOE-based enhanced genetic algorithm for unrelated parallel machine scheduling to minimize earliness/tardiness costs
Subject Areas : Business and Marketing
Parsa
Kianpour
1
(Senior Data Scientist, Cummins, USA)
Deepak
Gupta
2
(Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, USA)
Krishna
Krishnan
3
(Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, USA)
Bhaskaran
Gopalakrishnan
4
(Industrial and Management Systems Engineering, West Virginia University, USA)
Keywords: Heuristic, GA, earliness, Tardiness, DOE, Unrelated Parallel Machine,
Abstract :
This study presents an enhanced genetic algorithm (E-GA) to minimize earliness/tardiness costs in the job shop environment. It considers an unrelated parallel machine scheduling problem with a limit on maximum tardiness levels. This problem is motivated by the experience of one of the authors in a job shop supporting the local aircraft industry that requires strict control on delivery times. Current literature does not consider this critical restriction and unsuccessfully tries to deal with them using higher penalty costs. The proposed method uses the design of experiment (DOE) concept while optimizing the GA operators. Furthermore, it improves the initial solution using a hybrid dispatch rule through a strategic combination of construction and improvement heuristics. The model was applied to a local job shop. The results indicate that E-GA provides a schedule with lower cost and reduced computational time compared to existing dispatch rules in the literature and existing algorithms (OptQuest).
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