Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique

Vikas Kumar, Tung Sun Chan

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

Increasing global competitiveness has forced manufacturers to develop and manage families of products to remain competitive in the market while simultaneously increasing their profitability and market share. Manufacturers achieve this goal by aiming to design a variety of consumer appealing products while maintaining the production cost at a minimum level. Therefore product design and development issues have generated a lot of curiosity among researchers in the last few decades. The product family design concept incorporates the challenges of designing, developing and coordinating the design of multiple products. At the same time it also aims to maximize the commonality across a set of products without compromising their individual performances. The design for supply chain (DFSC) helps in the selection of an appropriate product family. In this research, to address the DFSC issues, a product platform approach' has been proposed that integrates the component modularity as well as the function modularity in the product design. The optimization model proposed in this research for the product development and the supply chain design is based on a Generic Bill of Materials (GBOM) representation. Realizing the complexities associated with the design of supply chain this research uses a newly developed cellular particle swarm optimization (CPSO) technique to resolve the product development problem. This algorithm inherits the property of cellular automata (CA) and standard particle swarm optimization (PSO). The main objective of this research is to maximize the market share while taking into account the customer view during the new product development. The performance of the CPSOalgorithm has been compared with the GA and standard PSO on a case study and randomly generated data sets of increasing complexity are used to show the efficacy and robustness of the algorithm.
Original languageEnglish
Title of host publicationEvolutionary Computing in Advanced Manufacturing
PublisherJohn Wiley and Sons
Pages51-76
Number of pages26
ISBN (Print)9780470639245
DOIs
Publication statusPublished - 22 Aug 2011

Keywords

  • Cellular automata
  • GBOM
  • Particle swarm
  • Product development
  • Supply chain design

ASJC Scopus subject areas

  • Engineering(all)

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