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Integrated Intelligent Method for Solving Multi-objective MPM Job Shop Scheduling Problem

Authors: 

D.C. Tselios, I.K. Savvas, Tahar Kechadi

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
The project portfolio scheduling problem has become very popular in recent years. Current project oriented organisations have to design a plan in order to execute a set of projects sharing common resources such as personnel teams. These projects must, therefore, be handled concurrently. This problem can be seen as an extension of the job shop scheduling problem; the multi-purpose job shop scheduling problem. In this paper, we propose a hybrid approach to deal with a bi-objective optimisation problem; Makespan and TotalWeighted Tardiness. The approach consists of three phases; in the first phase we 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 we 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.
Conference Name: 
6th International IEEE Conference on Information, Intelligence, Systems and Applications (IISA2015)
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
06/07/2015
Conference Location: 
Greece
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No
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