Multi-objective Evolutionary Optimisation for Product Design by Kalyanmoy Deb (auth.), Lihui Wang, Amos H. C. Ng, Kalyanmoy

By Kalyanmoy Deb (auth.), Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb (eds.)

With the expanding complexity and dynamism in today’s product layout and production, extra optimum, powerful and useful techniques and platforms are had to help product layout and production actions. Multi-objective Evolutionary Optimisation for Product layout and Manufacturing provides a concentrated choice of caliber chapters on state of the art learn efforts in multi-objective evolutionary optimisation, in addition to their useful functions to built-in product layout and production.

Multi-objective Evolutionary Optimisation for Product layout and Manufacturing contains significant sections. the 1st offers a broad-based evaluate of the major parts of study in multi-objective evolutionary optimisation. the second one provides in-depth remedies of chosen methodologies and platforms in clever layout and built-in manufacturing.

Recent advancements and concepts in multi-objective evolutionary optimisation make Multi-objective Evolutionary Optimisation for Product layout and Manufacturing an invaluable textual content for a large readership, from educational researchers to training engineers.

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Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

With the expanding complexity and dynamism in today’s product layout and production, extra optimum, powerful and sensible ways and structures are had to aid product layout and production actions. Multi-objective Evolutionary Optimisation for Product layout and production offers a targeted selection of caliber chapters on cutting-edge examine efforts in multi-objective evolutionary optimisation, in addition to their sensible functions to built-in product layout and production.

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Chichester, UK: Wiley. 2. Goldberg, D. E. (1989). Genetic algorithms for search, optimisation, and machine learning. Reading, MA: Addison-Wesley. 3. , Reddy, A. , & Singh, G. (2003). Optimal scheduling of casting sequence using genetic algorithms. Journal of Materials and Manufacturing Processes 18(3):409–432. 4. Deb, K. (1999). An introduction to genetic algorithms. S"adhan"a. 24(4):293–315 5. , & Agrawal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems 9(2):115–148 1 Multi-objective Optimisation Using Evolutionary Algorithms 31 6.

2, we present a comprehensive literature review of MOO for SCM, which is summarised in Sect. 3. In Sect. 4, a case study in which MOO is applied for to investigate the behaviour of the bullwhip effect in a supply chain is presented. 2 Literature Review: MOO for SCM Multi-objective optimisation (MOO) is a discipline that has been studied since 1970s, and its application areas range widely from resource allocation, transportation, investment decision to mechanical engineering, chemical engineering, automation applications, to name a few.

Offline and online objective reduction in evolutionary multiobjective optimisation based on objective conflicts (p. 269). ETH Zürich: Institut für Technische Informatik und Kommunikationsnetze. 58. , & Amato, P. (2004). A fuzzy definition of optimality for many criteria optimisation problems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34(3):315–326. 59. Branke, J. (2001). Evolutionary optimisation in dynamic environments. Heidelberg, Germany: Springer. 60. , Rao, U.

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