International Journal of Industrial Engineering & Production Research

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A three stage production system is considered in this paper. There are two stages to fabricate and ready the parts and an assembly stage to assembly the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and ready in the second stage and finally the parts are assembled in an assembly stage to produce the products. Two objective functions are considered that are: (1) to minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (∑_i▒(E_i∕T_i ) . Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that performance of the proposed algorithms is good in both efficiency and effectiveness criterions.

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Assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. In many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. When some changes related to market demand occur, it is worthwhile to re-balance an existing line rather than balancing a new one. Hence, in this paper we suggest a re-balancing model of an existing assembly line in which a new demand related cycle time (CT) is embedded to the traditional assembly line balancing problem (ALBP) as a new parameter. It does not focus on balancing a new line instead it considers a more realistic problem which is re-balancing an existing line. The objective is to re-schedule the tasks in order to reduce the current CT to the new required one such that two criteria are optimized: (i) minimization of the incurred costs and (ii) minimization of non-smoothing of reconfigured line. To solve the considered problem, an effective differential evolution algorithm is developed. Furthermore, to enhance the performance of algorithm, its parameters are optimized by the use of Taguchi method which is a conventional statistical technique for parameter design. The obtained results from computational experiments on benchmark instances show the effectiveness of suggested algorithm against other methods.

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Multi-manned assembly line balancing problems (MALBPs) can be usually found in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost-oriented version of MALBPs, namely, CMALBP, is addressed. This class of problems may arise in final assembly lines of products in which the manufacturing process is very labor-intensive. Since CMALBP is NP-Hard, a heuristic approach based on a tabu search algorithm is developed to solve the problem. The proposed algorithm uses two neighborhood generation mechanisms, namely swap and mutation, that effectively collaborate with each other to build new feasible solutions; moreover, two separate tabu lists (associated with the two generation mechanisms) are used to check if moving to a new generated neighbor solution is forbidden or allowed. To examine the efficiency of the proposed algorithm, some experimental instances are collected from the literature and solved. The obtained results show the effectiveness of the proposed tabu search approach.