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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. [ABSTRACT FROM AUTHOR]

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. In Part 1 of this paper, for each problem area, a typical solution has been coded in terms of a GA structure, a suitable objective function has been constructed and experiments have been performed in order to identify suitable GA operators and operator parameter values. The potential suitability of GAs to solve these problem types has then been determined by comparing the best solution generated using a GA to a solution derived using a traditional solution technique. Part 2 of this paper focuses on the range of GA experiments undertaken to investigate the underlying relationships between problem characteristics and the performance of individual operator types and their parameter values. [ABSTRACT FROM AUTHOR]