An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops

Abstract

The problem of scheduling in permutation flowshops with the objective of minimizing the completion-time variance of jobs is considered and solved by making use of ant-colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, which can be applied to solve combinatorial optimization problems. A new ant-colony algorithm (NACO) has been developed in this paper to solve the flowshop scheduling problem. The objective is to minimize the completion-time variance of jobs. Two existing ant-colony algorithms and the proposed ant-colony algorithm have been compared with an existing heuristic for scheduling with the objective of minimizing the completion-time variance of jobs. It is found that the proposed ant-colony algorithm gives promising and better results, on an average, as compared to those solutions given by the existing ant-colony algorithms and the existing heuristic for the permutation flowshop scheduling problem under study.