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A comparative evaluation of heuristic line balancing techniques

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TLDR
A computational experiment designed to assess the efficacy of 26 heuristic decision rules which group work tasks into work stations along an assembly line such that the number of work stations required is minimized is minimized.
Abstract
In this paper, we report on a computational experiment designed to assess the efficacy of 26 heuristic decision rules which group work tasks into work stations along an assembly line such that the number of work stations required is minimized. The heuristic decision rules investigated vary from simple list processing procedures that consider a single attribute of each work task for assignment, to procedures which are optimal seeking, but which have had their search terminated through the imposition of a limit on the amount of computation time that can be devoted to each search. Also included are heuristic decision rules which backtrack in an attempt to locate an improved solution, and decision rules which probabilistically search for improved solutions. Our investigation differs from those reported previously, in that the objective in balancing each line is to determine the minimum number of work stations for a given limit on the time available for assembly at each work station the cycle time. Previous approaches have investigated the problem of determining the minimum cycle time for a given line length. We also compare the results obtained with the optimal solution for a subset of the problems investigated. Both problems which have appeared in the open literature and problems which have been solved for the first time are included. Because a portion of our results differ from those reported previously, we suggest why these differences have occurred. Guidelines are also given to those balancing industrial assembly lines on the choice of the heuristic decision rule to use whether one is attempting to obtain a minimum station balance given a limit on the time available for assembly at each work station, or whether one is attempting to minimize the time devoted to assembly at a work station given a limit on the number of work stations available.

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A taxonomy of line balancing problems and their solutionapproaches

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Biased random-key genetic algorithms for combinatorial optimization

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References
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Journal ArticleDOI

A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem

TL;DR: A recurring problem in managing project activity involves the allocation of scarce resources to the individual activities comprising the project Resource conflict resolution decisions must be made whenever the concurrent demand for resources by the competing activities of a project exceeds resource availability.
Journal ArticleDOI

A computer method of sequencing operations for assembly lines

TL;DR: In this article, the authors present a Monte-Carlo simulation of COMSOAL, an acronym for a computer Method of Sequencing Operations for Assembly Lines, which is a method of balancing large complex machine-paced assembly lines.
Journal ArticleDOI

A Computing Procedure for a Line Balancing Problem

TL;DR: The purpose of the present paper is to set forth an enumeration procedure for solving one of the problems concerned with setting up production lines and to clarify the assumptions in its statement.
Journal ArticleDOI

An Experimental Investigation and Comparative Evaluation of Production Line Balancing Techniques

TL;DR: There are significant differences among the effectiveness results achieved by the line balancing techniques but there are few changes in the relative effectiveness of the computing techniques over the range of line lengths, partial ordering strengths, and problem sizes that were investigated.
Journal ArticleDOI

Dynamic Programming Solution of Sequencing Problems with Precedence Constraints

TL;DR: These two results permit a very compact computer implementation of a dynamic programming algorithm for solving one-machine sequencing problems with precedence constraints and this algorithm appears to be much more efficient than previous ones for certain one- machine sequencing problems.
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