Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Grouping genetic algorithms: an efficient method to solve the cell formation problem
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms
Modular design to support green life-cycle engineering
Expert Systems with Applications: An International Journal
Generation of new service concepts: A morphology analysis and genetic algorithm approach
Expert Systems with Applications: An International Journal
A hybrid grouping genetic algorithm for citywide ubiquitous WiFi access deployment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Green product design through product modularization using atomic theory
Robotics and Computer-Integrated Manufacturing
Modularizing services: A modified HoQ approach
Computers and Industrial Engineering
A new grouping genetic algorithm for clustering problems
Expert Systems with Applications: An International Journal
A particle swarm optimizer for grouping problems
Information Sciences: an International Journal
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Modular products are products that fulfill various functions through the combination of distinct modules. These detachable modules are constructed both according to the maximum physical and functional relations among components and maximizing the similarity of specifically modular driving forces. Accordingly, a non-linear programming is proposed to identify separable modules and simultaneously optimize the number of modules. This paper presents a systematic approach to accomplish modular product design in four major phases. Phase 1 is by means of functional and physical interaction analysis to format a component-to-component correlation matrix. Phase 2 is the exploration of design requirements to evaluate the relative importance of each modular driver. In phase 3, non-linear programming is used to formulate the objective function. In the final phase, a heuristic grouping genetic algorithm is adopted to search for the optimal or near-optimal modular architecture. This process and its application are illustrated by a real case of an electrical consumer product provided by an Original Design Manufacturer. The results demonstrate that the designer could direct a new approach to establish product modules according to the relative importance of modular drivers and the interaction among components.