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
DOI: 10.4018/978-1-7998-0414-7.ch083
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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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2. System Model

A globally accepted notation for theoretical study of scheduling problems was proposed by Graham et al. Graham R. L. et al. (1979). According to this classification αβγ, the generalised version of our problem can be expressed as follows:

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