An archived multi-objective simulated annealing for a dynamic cellular manufacturing system

Authors

1 Department of Industrial Management, Qom Branch, Islamic Azad University, P.O. Box 3749113191, Qom, Iran

2 Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, P.O. Box 148, Firoozkooh, Iran

3 Department of Industrial Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran

4 School of Industrial Engineering and Engineering Optimization Research Group, College of Engineering, University of Tehran, P.O.Box 11155-4563, Tehran, Iran

Abstract

To design a group layout of a cellular manufacturing
system (CMS) in a dynamic environment, a
multi-objective mixed-integer non-linear programming
model is developed. The model integrates cell formation,
group layout and production planning (PP) as three interrelated
decisions involved in the design of a CMS. This
paper provides an extensive coverage of important manufacturing
features used in the design of CMSs and enhances
the flexibility of an existing model in handling the fluctuations
of part demands more economically by adding
machine depot and PP decisions. Two conflicting objectives
to be minimized are the total costs and the imbalance
of workload among cells. As the considered objectives in
this model are in conflict with each other, an archived
multi-objective simulated annealing (AMOSA) algorithm
is designed to find Pareto-optimal solutions. Matrix-based
solution representation, a heuristic procedure generating an
initial and feasible solution and efficient mutation operators
are the advantages of the designed AMOSA. To demonstrate
the efficiency of the proposed algorithm, the performance
of AMOSA is compared with an exact algorithm
(i.e., [-constraint method) solved by the GAMS software
and a well-known evolutionary algorithm, namely NSGAII
for some randomly generated problems based on some
comparison metrics. The obtained results show that the
designed AMOSA can obtain satisfactory solutions for the
multi-objective model.

Keywords