Mousavi, S., Mahdavi, I., Rezaeian, J., Zandieh, M. (2018). Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times. Scientia Iranica, 25(4), 2233-2253. doi: 10.24200/sci.2017.4451
S.M. Mousavi; I. Mahdavi; J. Rezaeian; M. Zandieh. "Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times". Scientia Iranica, 25, 4, 2018, 2233-2253. doi: 10.24200/sci.2017.4451
Mousavi, S., Mahdavi, I., Rezaeian, J., Zandieh, M. (2018). 'Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times', Scientia Iranica, 25(4), pp. 2233-2253. doi: 10.24200/sci.2017.4451
Mousavi, S., Mahdavi, I., Rezaeian, J., Zandieh, M. Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times. Scientia Iranica, 2018; 25(4): 2233-2253. doi: 10.24200/sci.2017.4451
Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times
1Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
2Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G. C., Tehran, Iran
Abstract
The production scheduling problem in hybrid flow shops is a complex combinatorial optimization problem observed in many real-world applications. The standard hybrid flow shop problem involves often unrealistic assumptions. In order to address the realistic assumptions, four additional traits were added to the proposed problem. These include re-entrant line, setup times, position-dependent learning effects, and the consideration of maximum completion time together with total tardiness as objective function. Since the proposed problem is non-deterministic polynomial-time (NP)-hard, a meta-heuristic algorithm is proposed as the solution procedure. The solution procedure is categorized as an a priori approach. To show the efficiency and effectiveness of the proposed algorithm, computational experiments were done on various test problems. Computational results show that the proposed algorithm can obtain an effective and appropriate solution quality for our investigated problem
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