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Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem

Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem

Dimitrios C. Tselios, Ilias K. Savvas, M-Tahar Kechadi
Copyright: © 2016 |Volume: 4 |Issue: 1 |Pages: 20
ISSN: 2166-7241|EISSN: 2166-725X|EISBN13: 9781466693784|DOI: 10.4018/IJMSTR.2016010104
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MLA

Tselios, Dimitrios C., et al. "Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem." IJMSTR vol.4, no.1 2016: pp.42-61. http://doi.org/10.4018/IJMSTR.2016010104

APA

Tselios, D. C., Savvas, I. K., & Kechadi, M. (2016). Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 4(1), 42-61. http://doi.org/10.4018/IJMSTR.2016010104

Chicago

Tselios, Dimitrios C., Ilias K. Savvas, and M-Tahar Kechadi. "Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem," International Journal of Monitoring and Surveillance Technologies Research (IJMSTR) 4, no.1: 42-61. http://doi.org/10.4018/IJMSTR.2016010104

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Abstract

The project portfolio scheduling problem has become very popular in recent years since many modern organizations operate in multi-project and multi-objective environment. Current project oriented organizations have to design a plan in order to execute a set of projects sharing common resources such as personnel teams. This problem can be seen as an extension of the job shop scheduling problem; the multi-purpose job shop scheduling problem. In this paper, the authors propose a hybrid approach to deal with a bi-objective optimisation problem; Makespan and Total Weighted Tardiness. The approach consists of three phases; in the first phase they utilise a Genetic Algorithm (GA) to generate a set of initial solutions, which are used as inputs to recurrent neural networks (RNNs) in the second phase. In the third phase the authors apply adaptive learning rate and a Tabu Search like algorithm with the view to improve the solutions returned by the RNNs. The proposed hybrid approach is evaluated on some well-known benchmarks and the experimental results are very promising.

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