Scheduling of assembly flow shop problem and machines with random breakdowns

Hany Seidgar, Sahar Tadayoni Rad and Rasoul Shafaei

International Journal of Operational Research, 2017, vol. 29, issue 2, 273-293

Abstract: We investigate two-stage assembly flow shop problems (TAFSP) with considering machines breakdown and minimisation of the expected the weighted sum of makespan and mean of completion time is as objective value. This problem is NP-hard, hence we presented genetic algorithm (GA) and new self adapted differential evolutionary (NSDE) for solving random generated test problems. Artificial neural network (ANN) is applied to set parameters of two proposed algorithms also Taguchi method is used for analysing the effect of parameters of problem. The computational results reveal that NSDE is better than GA and achieve to good solutions in a shorter time.

Keywords: two stage assembly flow shop; machine breakdowns; simulation approach; self adapted differential evolutionary algorithm; artificial neural network; ANN; Taguchi method. (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:29:y:2017:i:2:p:273-293

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