A Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm for efficient selection and assignment of Material Handling Equipment

Authors:
S. Hamid L. Mirhosseyni;Phil Webb
Affiliations:
School of Mechanical, Materials and Manufacturing Engineering, The University of Nottingham, University Park, Nottingham NG7 2RD, UK;School of Mechanical, Materials and Manufacturing Engineering, The University of Nottingham, University Park, Nottingham NG7 2RD, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Material Handling (MH) is one of the key issues for every production site and has a great impact on manufacturing costs. The core concern in the design of a MH system is selecting the most suitable equipment for every MH operation and optimising them totally in order to attain an optimum solution. This paper presents a hybrid method for the selection and assignment of the most appropriate Material Handling Equipment (MHE). In the first phase, the system selects the most appropriate MHE types for every MH operation in a given application using a Fuzzy Knowledge-Based Expert System consisting of two sets of rules: Crisp Rules and Fuzzy Rules. In the second phase, a Genetic Algorithm (GA) searches throughout the feasible solution space, constituting of all possible combinations of the feasible equipment specified in the previous phase, in order to discover optimum solutions. The validity of the methodology developed in this paper is proved through the use of a real problem. Finally a comparison of the method with the other available publicised methods reveals the effectiveness of this hybrid approach.