عنوان مقاله [English]
Line balancing is a fundamental concept for continuous production systems. Assembly lines are present in different industrial environments and usually have a great economic impact because of their high manpower levels. A simplified view of the assembly line balancing problem (ALBP) is defined as the grouping of the tasks required to assemble the final product to the workstations conforming the assembly line, which specifies the permissible orderings of the tasks. The main goal of the assembly line balancing problem is to assign the tasks to workstations such that the precedence relations are satisfied and some performance measure is optimized. The ALBPs are classified into two groups: simple assembly line balancing problems (SALBPs), which bear numerous simplifying assumptions and general assembly line balancing problems (GALBPs), which are closer to the reality due to the consideration of one or more realistic conditions, like sequence-dependent setups.
In this paper, we consider the problem of optimizing simultaneously the objectives of minimizing cycle time and minimizing the overall setups in a general assembly line balancing environment with the consideration of sequence-dependent setup times between tasks. The first objective, which is referred to as the type II problem, generally occurs when the organization wants to produce the optimum number of items using a fixed number of workstations without adding new machines. The minimization of the overall setup times is important mostly for the cases when setups impose maintenance costs on tools and the prolongation of setup times would increase maintenance costs and also brings more exhaustion to workers.
This paper is intended to introduce the objective of minimizing overall setups in the class of assembly line balancing problems and solve the problem of concurrently minimizing cycle time and the overall setups. Exact method was not efficient enough to solve the innovative problem with type II problem assumptions; thus, a Pareto simulated annealing (PSA) algorithm is developed to solve such an Np-hard problem and several quantitative metrics are defined for evaluating the proposed algorithm. Computational results verified the considerable efficiency of the PSA algorithm.