Comput Ind Eng 47(4):431–450Ītashpaz-Gargari E, Lucas C (2008) Imperialist competitive algorithm: an algorithm for optimisation inspired by imperialistic competition. Int Transl Oper Res 5:317–324Īllaoui H, Artiba A (2004) Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints. Comput Oper Res 36(10):2740–2747Īllahverdi A, Mittenthal J (1998) Dual criteria scheduling on a two-machine flow shop subject to random breakdowns. Int J Adv Manuf Technol 37(1):166–177Īllahverdi A, Al-Anzi FS (2009) The two-stage assembly flowshop scheduling problem to minimise total completion time with setup times. Int J Prod Res 44(22):4713–4735Īllahverdi A, Al-Anzi FS (2007) The two-stage assembly flowshop scheduling problem with bicriteria of makespan and mean completion time. Comput Oper Res 23:909–916Īllahverdi A, Al-Anzi FS (2006) Evolutionary heuristics and an algorithm for the two-stage assembly scheduling problem to minimise makespan with setup times. J Oper Res Soc 46:896–904Īllahverdi A (1996) Two-machine proportionate flow shop scheduling with breakdowns to minimize maximum lateness. Int J Prod Econ 132:279–291Īllahverdi A (1995) Two-stage production scheduling with separated set-up times and stochastic breakdown. Eur J Oper Res 182:80–94ĪL-Hinai N, Elmekkawy TY (2011) Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. Int J Comput Appl 26(4):207–211Īl-Anzi FS, Allahverdi A (2007) A self-adaptive differential evolution heuristic for two- stage assembly scheduling problem to minimize maximum lateness with setup times. Int J Prod Res 53(6):1680–1711Īl-Anzi FS, Allahverdi A (2004) Hybrid simulated annealing heuristic for multimedia object requests scheduling problem. Neighborhood search with global exchange LLHs:Ībedi M, Seidgar H, Fazlollahtabar H, Bijani R (2015) Bi-objective optimization for scheduling the identical parallel batch-processing machines with arbitrary job sizes, unequal job release times and capacity limits. Hybrid imperialist competitive algorithm NSG: New self-adapted differential evolutionary SA:īacktracking search hyper-heuristic HICA: Self-adapted differential evolutionary NSDE: Multi-objective distributed permutation flow shop scheduling problem GA:Ĭloud theory-based simulated annealing DE: Hybrid flow shop scheduling problem MODPFSP: Two-stage assembly flow shop problem HFSP: The computational results show which proposed NSDE statistically is better than other proposed meta-heuristics algorithms according two important indicators: quality of solution and computational time. Also, we suggest Taguchi method as one the most important adjusting approaches for analyzing the effect of input parameters in each algorithm. We apply artificial neural network as a tuning tool for predicting the input parameters of each proposed meta-heuristics algorithms in uncertain condition. Eventually, since the proposed problem has both types of complexities (algorithm complexity and structural complexity), simulation is integrated into the proposed meta-heuristic approaches to handle the complexities. In this regard, to overcome this form of complexity, simulation techniques are typically employed. Machine breakdown and dynamic nature of the problem, the structural complexity increases markedly. Owning to its problem complexity and since the problem belongs to NP-hard class, use of meta-heuristic algorithms is justified to tackle the potential complexity of the problem considered, and hence, we proposed four meta-heuristics algorithms entitled: genetic algorithm, imperialist competitive algorithm, cloud theory-based simulated annealing and new self-adapted differential evolutionary (NSDE) to solve it. The goal is to minimize the expected the weighted sum of makespan and mean of completion time. In this paper, machines in manufacturing and assembly stages are not always available due to random machines breakdowns which occur during processing of each operation.
![makespan with plant simulation makespan with plant simulation](http://i1.hdslb.com/bfs/archive/a9e4dba46606d423e4d574f38d3c5eecdb283a4f.jpg)
![makespan with plant simulation makespan with plant simulation](https://i.ytimg.com/vi/JXovFobXTIE/maxresdefault.jpg)
In practical manufacturing environment, disruptions and unforeseen incidents occur, so a schedule being built based on deterministic information is not practical and may lead to poor performance. This paper takes random machines breakdowns and the two-stage assembly flow shop problem into consideration as a realistic assumption in industrial environments.