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An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs

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Abstract

The problem of scheduling in flowshops with sequence-dependent setup times 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, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.

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Authors and Affiliations

  1. Department of Management Studies, Indian Institute of Technology Madras, 600 036, Chennai, India

    Yuvraj Gajpal & Chandrasekharan Rajendran

  2. Faculty of Business Administration and Economics, Department of Operations, Production and Logistics Management, University of Passau, 94032, Passau, Germany

    Hans Ziegler

Authors
  1. Yuvraj Gajpal
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  2. Chandrasekharan Rajendran
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  3. Hans Ziegler
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Correspondence to Chandrasekharan Rajendran.

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Gajpal, Y., Rajendran, C. & Ziegler, H. An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs. Int J Adv Manuf Technol 30, 416–424 (2006). https://doi.org/10.1007/s00170-005-0093-y

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  • DOI: https://doi.org/10.1007/s00170-005-0093-y

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