Heuristics for a two-stage assembly flowshop with bicriteria of maximum lateness and makespan

Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job. Originality/value Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

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“…Table VI Factor levels (Seidgar et al, 2016a;Torabzadeh and Zandieh, 2010;Al-Anzi and Allahverdi, 2009;Seidgar et al, 2017a) Factor Levels…”

Section: Computational Resultsmentioning

confidence: 99%

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Tadayonirad

Seidgar

Fazlollahtabar

et al. 2019

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“…Although this method is a classical method, it is a simple and effective one for scheduling problems (Liao and Cheng 2007;Allahverdi and Al-Anzi 2008;Al-Anzi and Allahverdi 2009). In view of different requirements, decisions can be made by tuning the weights of different objectives.…”

Section: Problem Formulationmentioning

confidence: 99%

Meta-heuristics for the distributed two-stage assembly scheduling problem with bi-criteria of makespan and mean completion time

Xiong

Xing

2014

International Journal of Production Research

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This paper focuses on the distributed two-stage assembly flowshop scheduling problem for minimising a weighted sum of makespan and mean completion time. This problem involves two inter-dependent decision sub-problems: (1) how to allocate jobs among factories and (2) how to schedule the assigned jobs at each factory. A mathematical model is formulated for solving the small-sized instances of the problem. Since the NP-hardness of the problem, we also proposed a variable neighbourhood search (VNS) algorithm and a hybrid genetic algorithm combined with reduced variable neighbourhood search (GA-RVNS) to solve the distributed two-stage assembly flowshop scheduling problems and approximately optimise makespan and mean completion time simultaneously. Computational experiments have been conducted to compare the performances of the model and proposed algorithms. For a set of small-sized instances, both the model and the proposed algorithms are effective. The proposed algorithms are further evaluated on a set of large-sized instances. The results statistically show that both GA-RVNS and VNS obtain much better performances than the GA without RVNS-based local search step (GA-NOV). For the instances with small numbers of jobs, VNS achieves better performances than GA-RVNS. However, for the instances with large numbers of jobs, GA-RVNS yields better performances than the VNS. It is also shown that the overall performances of VNS are very close to GA-RVNS with different numbers of factories, weights given to makespan and numbers of machines at the first stage.

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“…Tozkapan et al (2003) developed a branch and bound procedure with an objective of minimizing the total weighted flowtime. The problems with respect to maximum lateness, mean completion time and weighted sum of makespan are addressed by Allahverdi and Fawaz (2006;2007;2008;2009). Torabzadeh and Zandeih (2010) found cloud theory based simulated annealing algorithm performing better than simulated annealing algorithm of Allahverdi and Fawaz (2008).…”

Section: Introductionmentioning

confidence: 99%

Heuristics for production scheduling problem with machining and assembly operations

Bhongade¹,

Khodke²

2012

ijiec

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This work deals with production scheduling problem in an assembly flow shop, having parts machining followed by their subsequent assembly operations. Limited heuristics available on the problem, are based on unrealistic assumption that every part is processed on all machines. In this paper, two heuristics NEH_BB and Disjunctive are proposed to solve assembly flow shop scheduling problem where every part may not be processed on each machine. Exhaustive computational experiments are conducted with 60 trials each. The methods are found to be applicable to large size problems. The objective functions used for comparison are makespan and computational time. Disjunctive method takes very less computational time as compared to NEH_BB and hence claimed to be the better among available approaches for finding solution in assembly flow shop problems.

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Heuristics for a two-stage assembly flowshop with bicriteria of maximum lateness and makespan

Cited by 43 publications

References 25 publications

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Meta-heuristics for the distributed two-stage assembly scheduling problem with bi-criteria of makespan and mean completion time

Heuristics for production scheduling problem with machining and assembly operations

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