A multi-objective optimization for preemptive identical parallel machines scheduling problem

This research investigates a new notion in just-in-time philosophy on the identical parallel machines considering allowable job preemption with respect to a bi-objective approach. The work-in-process (WIP) is also allowed, since minimization of WIP is desirable in many industrial applications specif...

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Bibliographic Details
Published inComputational & applied mathematics Vol. 36; no. 3; pp. 1367 - 1387
Main Authors Aalaei, Amin, Kayvanfar, Vahid, Davoudpour, Hamid
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.09.2017
Springer Nature B.V
Subjects
Applications of Mathematics
Applied physics
Classification
Computational mathematics
Computational Mathematics and Numerical Analysis
Genetic algorithms
Heuristic methods
Industrial applications
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematics
Mathematics and Statistics
Multiple objective analysis
Philosophy
Preempting
Production scheduling
Scheduling
Sorting algorithms
Workflow
Preemption
Secondary 68M20
Identical parallel machines
Primary 90B35
Just-in-time
Non-dominated sorting genetic algorithm
Earliness and tardiness
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Summary:This research investigates a new notion in just-in-time philosophy on the identical parallel machines considering allowable job preemption with respect to a bi-objective approach. The work-in-process (WIP) is also allowed, since minimization of WIP is desirable in many industrial applications specifically those including perishable items. In this new notion, a new model is defined in which the earliness costs depend on the start times of the jobs. The goal of this study is to minimize two objectives simultaneously: (1) total weighted earliness and tardiness as well as holding cost of all jobs which are waiting to be processed as WIP costs and (2) number of jobs interruptions. In this context, two multi-objective meta-heuristic algorithms, i.e., the non-dominated sorting genetic algorithm II (NSGAII) and non-dominated ranking genetic algorithm (NRGA) are employed to solve such bi-objective problems. Three measurement factors are then employed to evaluate the algorithms performances. Computational results demonstrate that NRGA outperforms NSGAII in all small- and medium-to-large-sized sample-generated problems; however, intangibly.
Bibliography:ObjectType-Article-1
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ISSN:0101-8205
2238-3603
1807-0302
DOI:10.1007/s40314-015-0298-0