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

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

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Article ID: iaor20062065
Country: Netherlands
Volume: 101
Issue: 2
Start Page Number: 259
End Page Number: 272
Publication Date: Jan 2006
Journal: International Journal of Production Economics
Authors: ,
Keywords: ant system, flowshop
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 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.

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