An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption

2020 ◽  
Vol 145 ◽  
pp. 106431 ◽  
Author(s):  
Damla Yüksel ◽  
M. Fatih Taşgetiren ◽  
Levent Kandiller ◽  
Liang Gao
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Efficient ◽  
Total Tardiness ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Total Energy Consumption ◽  
Permutation Flowshop ◽  
No Wait ◽  
Flowshop Scheduling Problem

An energy-efficient permutation flowshop scheduling problem

2020 ◽  
Vol 150 ◽  
pp. 113279 ◽  
Author(s):  
Hande Öztop ◽  
M. Fatih Tasgetiren ◽  
Deniz Türsel Eliiyi ◽  
Quan-Ke Pan ◽  
Levent Kandiller
Keyword(s):  
Energy Efficient ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Flowshop Scheduling Problem

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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