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Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables
Authors:Stelios Tsafarakis  Charalampos Saridakis  George Baltas  Nikolaos Matsatsinis
Institution:1. Technical University of Crete, Department of Production Engineering and Management, Agiou Titou square, Chania 73132 Greece;2. University of Leeds, Leeds University Business School, Maurice Keyworth Building, Leeds LS2 9JT UK;3. Athens University of Economics and Business, Department of Marketing and Communication, 76 Patission Avenue, Athens 10434 Greece;4. Technical University of Crete, Department of Production Engineering and Management, Chania 73100 Greece
Abstract:This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.
Keywords:Product line design  Business-to-business marketing  Particle swarm optimization  Hybridization  Mutation operator
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