A hybrid genetic algorithm approach on multi-objective of assembly planning problem

Abstract

In practice, modeling an assembly system often requires assigning a set of operations to a set of workstations. The aim is to optimize some performance indices of an assembly line. This assignation is usually a tedious design procedure so a significant amount of manpower is required to obtain a good work plan. Poor assembly planning may significantly increase the cost of products and reduce productivity. However, these optimization problems fall into the class of NP-hard problems. Finding an optimal solution in an acceptable time is difficult, even using a powerful computer. This study presents a hybrid genetic algorithm approach to the problems of assembly planning with various objectives, including minimizing cycle time, maximizing workload smoothness, minimizing the frequency of tool change, minimizing the number of tools and machines used, and minimizing the complexity of assembly sequences. A self-tuning method was developed to correct infeasible chromosomes. Several examples were employed to illustrate the proposed approach. Experimental results indicated that the proposed method can efficiently yield many alternative assembly plans to support the design and operation of a flexible assembly system.

Simple assembly line balancing problem under task deterioration

“…The assembly-line balancing problems, among the combinational optimization problems, are also categorized as NP-Hard (Karp, 1972). In recent years, most researchers have used genetic algorithm to solve such problems (Kim et al 2009, Chen et al 2002. In genetic algorithm, the initial population of solutions, which appear as a set of feasible solutions (chromosomes), are selected randomly.…”

Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences

“…Limitations of AI based optimal ASP methods in terms of assembly predicate consideration, premature convergence and high computational time are listed in Table 1. (Kashkoush & ElMaraghy, 2015) Computational time C C NC (Chen & Liu, 2001) GA Computational time C C NC (Chen et al, 2002) Computational time C C NC (Sinanoglu & Riza Börklü, 2005) NN Computational time C C NC PSO Computational time C C NC (Wang & Liu, 2010) Computational time C C C (Nayak et al, 2015) SA Computational time C C NC (Lee & Gemmill, 2001) Assembly cost C C NC ) AIS…”

A multi-objective method for solving assembly line balancing problem

“…Assembly lines originally were developed for a mass-production of standardized products, designed to explore a high specialization of labor (Salveson, 1955;Gutjahr & Nemhauser, 1964). Ponnambalam et al (2007) presented a multiobjective genetic algorithm like Chen et al (2002) to solve assembly line balancing problems by considering the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer and using genetic algorithm the problem was solved using different benchmarks ). Kottas and Lau (1973) proposed a heuristic for balancing paced assembly lines involving tasks which represent substantial time variations and by explicitly considering labor and incompletion costs in grouping tasks into work assignments to reach a near optimal solution.…”