Efficient metaheuristic algorithm and re-formulations for the unrelated parallel machine scheduling problem with sequence and machine-dependent setup times

2014 ◽  
Vol 76 (9-12) ◽  
pp. 1705-1718 ◽  
Author(s):  
Oliver Avalos-Rosales ◽  
Francisco Angel-Bello ◽  
Ada Alvarez
Keyword(s):  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Setup Times ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Unrelated Parallel Machine ◽  
Unrelated Parallel Machine Scheduling ◽  
Parallel Machine Scheduling Problem ◽  
Dependent Setup Times

scholarly journals A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 768
Author(s):  
Dung-Ying Lin ◽  
Tzu-Yun Huang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problem ◽  
Unrelated Parallel Machine ◽  
Efficient Manner ◽  
Unrelated Parallel Machine Scheduling ◽  
Parallel Machine Scheduling Problem ◽  
Dependent Setup Times

The unrelated parallel machine scheduling problem aims to assign jobs to independent machines with sequence-dependent setup times so that the makespan is minimized. When many practical considerations are introduced, solving the resulting problem is challenging, especially when problems of realistic sizes are of interest. In this study, in addition to the conventional objective of minimizing the makespan, we further consider the burn-in (B/I) procedure that is required in practice; we need to ensure that the scheduling results satisfy the B/I ratio constrained by the equipment. To solve the resulting complicated problem, we propose a population-based simulated annealing algorithm embedded with a variable neighborhood descent technique. Empirical results show that the proposed solution strategy outperforms a commonly used commercial optimization package; it can obtain schedules that are better than the schedules used in practice, and it does so in a more efficient manner.

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