Advanced scheduling with genetic algorithms in supply networks

Purpose - The purpose of this research is to improve efficiency of the traditional scheduling methods and explore a more effective approach to solving the scheduling problem in supply networks with genetic algorithms (GAs).Design methodology approach - This paper develops two methods with GAs for de...

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Bibliographic Details
Published in:Journal of manufacturing technology management 2011-01, Vol.22 (6), p.748-769
Main Author: awrynowicz, Anna
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Chromosomes
Factories
Genetic algorithms
Heuristic
Job shops
Literature reviews
Manufacturing
Manufacturing engineering
Mathematical analysis
Networks
Operations management
Pareto optimum
Population
Production scheduling
Schedules
Scheduling
Studies
Supply chains
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Summary:Purpose - The purpose of this research is to improve efficiency of the traditional scheduling methods and explore a more effective approach to solving the scheduling problem in supply networks with genetic algorithms (GAs).Design methodology approach - This paper develops two methods with GAs for detailed production scheduling in supply networks. The first method adopts a GA to job shop scheduling in any node of the supply network. The second method is developed for collective scheduling in an industrial cluster using a modified GA (MGA). The objective is to minimize the total makespan. The proposed method was verified on some experiments.Findings - The suggested GAs can improve detailed production scheduling in supply networks. The results of the experiments show that the proposed MGA is a very efficient and effective algorithm. The MGA creates the manufacturing schedule for each factory and transport operation schedule very quickly.Research limitations implications - For future research, an expert system will be adopted as an intelligent interface between the MRPII or ERP and the MGA.Originality value - From the mathematical point of view, a supply network is a digraph, which has loops and therefore the proposed GAs take into account loops in supply networks. The MGA enables dividing jobs between factories. This algorithm is based on operation codes, where each chromosome is a set of four-positions genes. This encoding method includes both manufacture operations and long transport operations.
ISSN:1741-038X
1758-7786
DOI:10.1108/17410381111149620