Integrated Intelligent Method for Solving Multi-objective MPM Job Shop Scheduling Problem
|Title:||Integrated Intelligent Method for Solving Multi-objective MPM Job Shop Scheduling Problem||Authors:||Tselios, Dimitrios
Savvas, Ilias K.
|Permanent link:||http://hdl.handle.net/10197/7418||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 learning;Statistics;Project scheduling;Job shop scheduling;Recurrent neural network;Multi-objective;Genetic algorithm;Adaptive 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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