A novel hybrid multi-objective algorithm to solve the generalized cubic cell formation problem

Abstract

The Generalized Cubic Cell Formation Problem (GCCFP) is a Multi-Objective (MO) optimization problem in manufacturing systems. It aims to find the suitable grouping of machines, products, and workers and their best assignment to manufacturing cells. During the resolution of the MO optimization problems, the decision maker should take decisions about multiple variables in order to optimize several objectives, or to find a good trade-off between the concerned objectives. Depending on the moment when the decision about the suitable configuration is taken, the resolution methods may be devised into (i) priori methods, (ii) interactive methods, and (iii) posteriori methods. In this study, a novel posteriori efficient algorithm called Robust Simulated Annealing-AUGMented ɛ-CONstraint (SA-AUGMECON-R) algorithm is introduced in order to solve the GCCFP. The proposed algorithm combines AUGMECON-R exact method and the simulated annealing meta-heuristic. To evaluate the performance of the algorithm, a comparative study was conducted. Regarding the values of the metrics that are used to evaluate the performance of MO algorithms, SA-AUGMECON-R algorithm provides excellent results comparing with AUGMECON 2, AUGMECON-R and NSGA-II.