Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

•Four different MILP models based on four different modeling ideas are proposed.•A constraint programming (CP) model is designed.•MILP and CP models prove the optimality of 62 benchmark instances.•CP model obtains new best solutions for 11 benchmark instances. This paper intends to address the distr...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 142; p. 106347
Main Authors: Meng, Leilei, Zhang, Chaoyong, Ren, Yaping, Zhang, Biao, Lv, Chang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2020
Subjects:
Constraint programming
Distributed flexible job shop scheduling problem
Makespan
Mixed integer linear programming
Makespan
Mixed integer linear programming
Distributed flexible job shop scheduling problem
Constraint programming
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•Four different MILP models based on four different modeling ideas are proposed.•A constraint programming (CP) model is designed.•MILP and CP models prove the optimality of 62 benchmark instances.•CP model obtains new best solutions for 11 benchmark instances. This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixed integer linear programming (MILP) models as well as a constraint programming (CP) model, among which four MILP models are formulated based on four different modeling ideas. MILP models are effective in solving small-scaled problems to optimality. DFJSP is NP-hard, therefore, we propose an efficient constraint programming (CP) model based on interval decision variables and domain filtering algorithms. Numerical experiments are conducted to evaluate the performance of the proposed MILP models and CP model. The results show that the sequence-based MILP model is the most efficient one, and the proposed CP model is effective in finding good quality solutions for the both the small-sized and large-sized instances. The CP model incomparably outperforms the state-of-the-art algorithms and obtains new best solutions for 11 benchmark problems. Moreover, the best MILP model and CP model have proved the optimality of 62 best-known solutions.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2020.106347