A fuzzy genetic algorithm for real-world job shop scheduling

Authors:
Carole Fayad;Sanja Petrovic
Affiliations:
School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK;School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

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Abstract

In this paper, a multi-objective genetic algorithm is proposed to deal with a real-world fuzzy job shop scheduling problem. Fuzzy sets are used to model uncertain due dates and processing times of jobs. The objectives considered are average tardiness and the number of tardy jobs. Fuzzy sets are used to represent satisfaction grades for the objectives taking into consideration the preferences of the decision maker. A genetic algorithm is developed to search for the solution with maximum satisfaction grades for the objectives. The developed algorithm is tested on real-world data from a printing company. The experiments include different aggregation operators for combining the objectives.