An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives

Abstract

This research presents an application of the relatively new approach of ant colony optimization (ACO) to address a production-sequencing problem when two objectives are present — simulating the artificial intelligence agents of virtual ants to obtain desirable solutions to a manufacturing logistics problem. The two objectives are minimization of setups and optimization of stability of material usage rates. This type of problem is NP-hard, and therefore, attainment of IP/LP solutions, or solutions via complete enumeration is not a practical option. Because of such challenges, an approach is used here to obtain desirable solutions to this problem with a minimal computational effort. The solutions obtained via the ACO approach are compared against solutions obtained via other search heuristics, such as simulated annealing, tabu search, genetic algorithms and neural network approaches. Experimental results show that the ACO approach is competitive with these other approaches in terms of performance and CPU requirements.