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Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem

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Abstract

Lot streaming is the technique of splitting a given job into sublots to allow the overlapping of successive operations in multi-stage manufacturing systems thereby reducing production makespan. Several research articles appeared in literature to solve this problem and most of these studies are limited to pure flowshop environments where there is only a single machine in each stage. On the other hand, because of the applicability of hybrid flowshops in different manufacturing settings, the scheduling of these types of shops is also extensively studied by several authors. However, the issue of lot streaming in hybrid flowshop environment is not well studied. In this paper, we aim to contribute in bridging the gap between the research efforts in flowshop lot streaming and hybrid flowshop scheduling. We propose a mathematical model and a genetic algorithm for the lot streaming problem of several jobs in multi-stage flowshops where at each stage there are unrelated parallel machines. The jobs may skip some of the stages, and therefore, the considered system is a complex generalized flowshop. The proposed genetic algorithm is executed on both sequential and parallel computing platforms. Numerical examples showed that the parallel implementation greatly improved the computational performance of the developed heuristic.

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Authors and Affiliations

  1. School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON, Canada, N1G 2W1

    Fantahun Melaku Defersha

  2. Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve W., Montreal, QC, Canada, H3G 1M8

    Mingyuan Chen

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  1. Fantahun Melaku Defersha
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  2. Mingyuan Chen
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Correspondence to Fantahun Melaku Defersha.

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Defersha, F.M., Chen, M. Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem. Int J Adv Manuf Technol 62, 249–265 (2012). https://doi.org/10.1007/s00170-011-3798-0

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  • DOI: https://doi.org/10.1007/s00170-011-3798-0

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