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

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Title: Integrated Intelligent Method for Solving Multi-objective MPM Job Shop Scheduling Problem
Authors: Tselios, Dimitrios
Savvas, Ilias K.
Kechadi, Tahar
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Date: 8-Jul-2015
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 Total Weighted 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.
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2015 IEEE
Keywords: Machine learningStatisticsProject schedulingJob shop schedulingRecurrent neural networkMulti-objectiveGenetic algorithmAdaptive learning
DOI: 10.1109/IISA.2015.7388093
Language: en
Status of Item: Peer reviewed
Conference Details: 2015 6th International IEEE Conference on Information, Intelligence, Systems and Applications (IISA 2015), Corfu, Greece, 6 - 8 July 2015
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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