Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A heuristic approach to product design
Management Science
Heuristics for product-line design using conjoint analysis
Management Science
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A decision support system for vehicle fleet planning
Decision Support Systems
Triangulation in decision support systems: algorithms for product design
Decision Support Systems
Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Application of a hybrid genetic algorithm to airline crew scheduling
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms for product design
Management Science
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Development of hybrid genetic algorithms for product line designs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.01 |
This research builds on prior work on developing near optimal solutions to the product line design problems within the conjoint analysis framework. In this research, we investigate and compare different genetic algorithm operators; in particular, we examine systematically the impact of employing alternative population maintenance strategies and mutation techniques within our problem context. Two alternative population maintenance methods, that we term "Emigration" and "Malthusian" strategies, are deployed to govern how individual product lines in one generation are carried over to the next generation. We also allow for two different types of reproduction methods termed "Equal Opportunity" in which the parents to be paired for mating are selected with equal opportunity and a second based on always choosing the best string in the current generation as one of the parents which is referred to as the "Queen bee", while the other parent is randomly selected from the set of parent strings. We also look at the impact of integrating the artificial intelligence approach with a traditional optimization approach by seeding the GA with solutions obtained from a Dynamic Programming heuristic proposed by others. A detailed statistical analysis is also carried out to determine the impact of various problem and technique aspects on multiple measures of performance through means of a Monte Carlo simulation study. Our results indicate that such proposed procedures are able to provide multiple "good" solutions. This provides more flexibility for the decision makers as they now have the opportunity to select from a number of very good product lines. The results obtained using our approaches are encouraging, with statistically significant improvements averaging 5% or more, when compared to the traditional benchmark of the heuristic dynamic programming technique.