Minimizing Makespan in Permutation Flow Shop Scheduling with Proportional Deterioration

2015 ◽  
Vol 32 (06) ◽  
pp. 1550050 ◽  
Author(s):  
Na Yin ◽  
Liying Kang

The [Formula: see text]-job and [Formula: see text]-machine permutation flow shop scheduling problem with a proportional deterioration is considered in which all machines process the jobs in the same order, i.e., a permutation schedule. A proportional deterioration means that the job deterioration as an increasing function that is proportional to a linear function of time. The objective is to minimize the makespan, i.e., the maximum completion time. When some dominant relationships between [Formula: see text] machines can be satisfied, we show that some special cases of the problem can be polynomial solvable. For the general case, we also propose a heuristic algorithm and give the computational experiments.

2013 ◽  
Vol 30 (06) ◽  
pp. 1350022 ◽  
Author(s):  
JI-BO WANG ◽  
MING-ZHENG WANG

In this study, we consider a permutation flow shop scheduling problem on a three-machine with deteriorating jobs (a deteriorating job means that the job's processing time is an increasing function of its starting time) so as to minimize the makespan. We model job deterioration as a function that is proportional to a linear function of time. For some special cases, we prove that the problem can be solved in polynomial time. We develop branch-and-bound and heuristic procedures for the general case. Computational experiments for the branch-and-bound algorithm and heuristic algorithm are presented.


2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.


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