Multi-objective fuzzy assembly line balancing using genetic algorithms

Authors:
P. Th. Zacharia;Andreas C. Nearchou
Affiliations:
Department of Mechanical Engineering & Aeronautics, University of Patras, Rio, Greece 26 500;Department of Business Administration, University of Patras, Rio, Greece 26 500
Venue:
Journal of Intelligent Manufacturing
Year:
2012

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Abstract

This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-criteria fuzzy objectives: (a) Minimizing the fuzzy cycle time and the fuzzy smoothness index of the workload of the line. (b) Minimizing the fuzzy cycle time of the line and the fuzzy balance delay time of the workstations. A new multi-objective genetic algorithm is applied to solve the problem whose performance is studied and discussed over known test problems taken from the open literature.