Multiobjective Assembly Line Resource Assignment and Balancing Problem of Type 2

The lively field of assembly line problem often has a significant impact on the performance of manufacturing systems. In this context, Assembly Line Balancing Problems (ALBPs) are widely cited in the literature. The ALBP is one of the most important problems among the other problems of assembly lines like designing and managing. The fundamental ALB Problem (ALBP) is the way of getting an optimal assignment of tasks to the stations respecting well-determined constraints and reaching certain objectives. In the previous research, there are many studies on developing methods for solving Simple Assembly Line Balancing Problems (SALBP) and their various extensions. Each extension is motivated by several real-life applications. This paper presents a new extension of SALBP-2, so-called Multiobjective Assembly Line Resource Assignment and Balancing Problem of type 2 (MOALRABP-2). The problem is a tri-criteria one, which aims to minimize simultaneously the following objectives: the cycle time, the mean absolute deviation and the cost per time unit (hour) of a line for a fixed number of stations to satisfy the constraints of precedence between tasks and compatibility between resources. A new version of Multiobjective Evolutionary Algorithm (MOEA) named Hybrid MOEA (HMOEA) is elaborated to seek a set of diverse optimal solutions. In addition, the MOEA parameters are optimized using the Taguchi method. The effectiveness of the HMOEA was assessed through a set of problems. The results comparisons show a quite promising higher performance for the HMOEA.

[1]  Sungsoo Park,et al.  A heuristic for an assembly line balancing problem with incompatibility, range, and partial precedence constraints , 1997 .

[2]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[3]  Saeed Ghahramani,et al.  Balance equations for general tandem queues , 1991 .

[4]  Eckart Zitzler,et al.  Evolutionary multi-objective optimization , 2007, Eur. J. Oper. Res..

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Armin Scholl,et al.  A survey on problems and methods in generalized assembly line balancing , 2006, Eur. J. Oper. Res..

[7]  Maghsud Solimanpur,et al.  Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing , 2013, Math. Comput. Model..

[8]  Ahmed Mellouli,et al.  A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2) , 2017, J. Intell. Manuf..

[9]  Ilker Baybars,et al.  A survey of exact algorithms for the simple assembly line balancing , 1986 .

[10]  Nima Hamta,et al.  Bi-criteria assembly line balancing by considering flexible operation times , 2011 .

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  George L. Nemhauser,et al.  An Algorithm for the Line Balancing Problem , 1964 .

[13]  Ram Rachamadugu,et al.  Improving the equality of workload assignments in assembly lines , 1991 .

[14]  Z. H. Che,et al.  A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB , 2012, Expert Syst. Appl..

[15]  Gunhan Mirac Bayhan,et al.  A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints , 2011, Eng. Appl. Artif. Intell..

[16]  Michal Tzur,et al.  Design of flexible assembly line to minimize equipment cost , 2000 .