Distributed permutation flowshop scheduling problem with total completion time objective

OPSEARCH ◽  
2020 ◽  
Author(s):  
Arshad Ali ◽  
Yuvraj Gajpal ◽  
Tarek Y. Elmekkawy
Keyword(s):  
Completion Time ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Flowshop Scheduling Problem

scholarly journals Two-machine flowshop scheduling problem to minimize makespan or total completion time with random and bounded setup times

2003 ◽  
Vol 2003 (39) ◽  
pp. 2475-2486 ◽  
Author(s):  
Ali Allahverdi ◽  
Tariq Aldowaisan ◽  
Yuri N. Sotskov
Keyword(s):  
Completion Time ◽  
Setup Times ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Lower And Upper Bounds ◽  
Dominance Relations ◽  
Flowshop Scheduling Problem ◽  
Global And Local ◽  
Local Dominance

This paper addresses the two-machine flowshop scheduling problem with separate setup times to minimize makespan or total completion time (TCT). Setup times are relaxed to be random variables rather than deterministic as commonly used in the OR literature. Moreover, distribution-free setup times are used where only the lower and upper bounds are given. Global and local dominance relations are developed for the considered flowshops and an illustrative numerical example is given.

scholarly journals Three-Machine Flowshop Scheduling Problem to Minimize Total Completion Time with Bounded Setup and Processing Times

2007 ◽  
Vol 1 (2) ◽  
pp. 5-23 ◽  
Author(s):  
Ali Allahverdi
Keyword(s):  
Completion Time ◽  
Computational Experiments ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Dominance Relations ◽  
Processing Times ◽  
Flowshop Scheduling Problem ◽  
Global And Local ◽  
Local Dominance

The three-machine flowshop scheduling problem to minimize total completion time is studied where setup times are treated as separate from processing times. Setup and processing times of all jobs on all machines are unknown variables before the actual occurrence of these times. The lower and upper bounds for setup and processing times of each job on each machine is the only information that is available. In such a scheduling environment, there may not exist a unique schedule that remains optimal for all possible realizations of setup and processing times. Therefore, it is desired to obtain a set of dominating schedules (which dominate all other schedules) if possible. The objective for such a scheduling environment is to reduce the size of dominating schedule set. We obtain global and local dominance relations for a three-machine flowshop scheduling problem. Furthermore, we illustrate the use of dominance relations by numerical examples and conduct computational experiments on randomly generated problems to measure the effectiveness of the developed dominance relations. The computational experiments show that the developed dominance relations are quite helpful in reducing the size of dominating schedules.

scholarly journals An algorithm for a no-wait flowshop scheduling problem for minimizing total tardiness with a constraint on total completion time

2022 ◽  
Vol 13 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Ali Allahverdi ◽  
Harun Aydilek ◽  
Asiye Aydilek
Keyword(s):  
Completion Time ◽  
Performance Measure ◽  
Total Tardiness ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Significance Level ◽  
Test Of Hypothesis ◽  
No Wait ◽  
Flowshop Scheduling Problem

We consider a no-wait m-machine flowshop scheduling problem which is common in different manufacturing industries such as steel, pharmaceutical, and chemical. The objective is to minimize total tardiness since it minimizes penalty costs and loss of customer goodwill. We also consider the performance measure of total completion time which is significant in environments where reducing holding cost is important. We consider both performance measures with the objective of minimizing total tardiness subject to the constraint that total completion time is bounded. Given that the problem is NP-hard, we propose an algorithm. We conduct extensive computational experiments to compare the performance of the proposed algorithm with those of three well performing benchmark algorithms in the literature. Computational results indicate that the proposed algorithm reduces the error of the best existing benchmark algorithm by 88% under the same CPU times. The results are confirmed by extensive statistical analysis. Specifically, ANOVA analysis is conducted to justify the difference between the performances of the algorithms, and a test of hypothesis is performed to justify that the proposed algorithm is significantly better than the best existing benchmark algorithm with a significance level of 0.01.

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