A Bicriteria Scheduling Problem with a Learning Effect: Total Completion Time and Total Tardiness

2007 ◽  
Vol 45 (2) ◽  
pp. 75-81 ◽  
Author(s):  
Tamer Eren ◽  
Ertan Güner
Keyword(s):  
Completion Time ◽  
Learning Effect ◽  
Total Tardiness ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Bicriteria Scheduling

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.

scholarly journals Scheduling Jobs Families with Learning Effect on the Setup

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sergio Fichera ◽  
Antonio Costa ◽  
Fulvio Cappadonna
Keyword(s):  
Completion Time ◽  
Learning Effect ◽  
Flow Shop ◽  
Group Scheduling ◽  
Scheduling Problem ◽  
Learning Effects ◽  
Anova Analysis ◽  
Hybrid Metaheuristic ◽  
Set Up ◽  
Over Time

The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.

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