scholarly journals Unrelated parallel machine scheduling with past-sequence-dependent setup time and learning effects

2011 ◽  
Vol 35 (3) ◽  
pp. 1492-1496 ◽  
Author(s):  
Chou-Jung Hsu ◽  
Wen-Hung Kuo ◽  
Dar-Li Yang
2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hongtao Hu ◽  
K. K. H. Ng ◽  
Yichen Qin

A parallel machine scheduling problem in plastic production is studied in this paper. In this problem, the processing time and arrival time are uncertain but lie in their respective intervals. In addition, each job must be processed together with a mold while jobs which belong to one family can share the same mold. Therefore, time changing mold is required for two consecutive jobs that belong to different families, which is known as sequence-dependent setup time. This paper aims to identify a robust schedule by min–max regret criterion. It is proved that the scenario incurring maximal regret for each feasible solution lies in finite extreme scenarios. A mixed integer linear programming formulation and an exact algorithm are proposed to solve the problem. Moreover, a modified artificial bee colony algorithm is developed to solve large-scale problems. The performance of the presented algorithm is evaluated through extensive computational experiments and the results show that the proposed algorithm surpasses the exact method in terms of objective value and computational time.


2011 ◽  
Vol 467-469 ◽  
pp. 1967-1972 ◽  
Author(s):  
Qun Niu ◽  
Fang Zhou ◽  
Tai Jin Zhou

This paper proposes an adaptive clonal selection algorithm (CSA) to solve the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup time constraints. The objective is to find the sequence which minimizes the makesepan. CSA is a newly discovered population-based evolutionary algorithm based on the clonal selection principle and the immune system. In order to improve the performance of CSA, a local search operation is adopted to strengthen the search ability. In addition, an adaptive clonal factor and a stage mutation operation are introduced to enhance the exploration and exploitation of the algorithm. The performance of the proposed adaptive clonal selection algorithm is compared with genetic algorithm (GA), Simulated Annealing (SA) and basic CSA on 320 randomly generated instances. The results demonstrate the superiority of the proposed method and confirm its potential to solve the UPMSP with sequence-dependent setup time constraints especially when the scale of the instances is very large.


Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1574
Author(s):  
Deming Lei ◽  
Tian Yi

Unrelated parallel machine scheduling problems (UPMSP) with various processing constraints have been considered fully; however, a UPMSP with deteriorating preventive maintenance (PM) and sequence-dependent setup time (SDST) is seldom considered. In this study, a new differentiated shuffled frog-leaping algorithm (DSFLA) is presented to solve the problem with makespan minimization. The whole search procedure consists of two phases. In the second phase, quality evaluation is done on each memeplex, then the differentiated search processes are implemented between good memeplexes and other ones, and a new population shuffling is proposed. We conducted a number of experiments. The computational results show that the main strategies of DSFLA were effective and reasonable and DSFLA was very competitive at solving UPMSP with deteriorating PM and SDST.


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