Use of genetic algorithms in operations management: Part 2: Results

• Published in: Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture Vol. 218; no. 3; pp. 329 - 343
• Main Authors: Stockton, D J, Quinn, L, Khalil, R A
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.03.2004; Mechanical Engineering Publications; SAGE PUBLICATIONS, INC
• Subjects: Applied sciences; Exact sciences and technology; Genetic algorithms; Mechanical engineering. Machine design; Operations management; assembly line balancing; genetic algorithms; manufacturing system design; facilities layout; material requirements planning; assortment planning; aggregate planning; Decision support system; Decision making; Performance characteristic; System design; Experimental study; Genetic algorithm; Objective function; System identification; Planning; Aggregate
• ISSN: 0954-4054; 2041-2975
• DOI: 10.1243/095440504322984876

Abstract Research has been carried out to investigate the use of genetic algorithms (GAs) as a common solution technique for solving the range of problems that arise when designing and planning manufacturing operations. A variety of problem areas have been selected that are representative of the range of problem types found in manufacturing decision-making, i.e. assortment planning, aggregate planning, lot sizing within material requirements planning environments, line balancing and facilities layout. Part 1 of this paper reported how typical solutions for each problem area were coded in terms of a genetic algorithm structure and how suitable objective functions were constructed. In addition, comparisons of performance were carried out between GA solution methods and traditional solution methods. Part 2 of this paper now describes the GA experiments undertaken during the identification of suitable GA operators and operator parameter values. These experiments have enabled underlying relationships between problem characteristics and performance of individual operator types and parameter values to be identified. From this work a set of guidelines has been identified for selecting appropriate genetic algorithm structures for specific types of operations management decision area.