Multi-manned assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production lines, usually there are multimanned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones in order to meet the market demand. In this paper, such a class of assembly line balancing problems, named multi-manned assembly line balancing problems type II, has been addressed. Since the problem is NP-hard, a meta-heuristic approach based on a simulated annealing algorithm has been developed to solve the problem. The performance of the proposed algorithm has been tested on a set of test problems taken from the literature; the results show that the algorithm performs well.

A simulated annealing approach for multi-manned assembly line balancing problem type II

ROSHANI, ABDOLREZA;GIGLIO, DAVIDE
2015-01-01

Abstract

Multi-manned assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production lines, usually there are multimanned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones in order to meet the market demand. In this paper, such a class of assembly line balancing problems, named multi-manned assembly line balancing problems type II, has been addressed. Since the problem is NP-hard, a meta-heuristic approach based on a simulated annealing algorithm has been developed to solve the problem. The performance of the proposed algorithm has been tested on a set of test problems taken from the literature; the results show that the algorithm performs well.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/852998
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