Evolutionary computation with linear physical programming for solving a disassembly-to-order system

Surendra M. Gupta, Northeastern University
Prasit Imtanavanich, Northeastern University

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Originally published in the Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing VI, Boston, Massachusetts, pp. 30-41, October 1-3, 2006

Abstract

We solve the disassembly-to-order (DTO) problem by using Evolutionary Computation. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL products for the DTO system which satisfy the desirable criteria of the system. One of the most widely used forms of Evolutionary Computation is Genetic Algorithm (GA). GA, which has the capability to improve a set of solutions over evolutionary steps, is used to generate optimal number of take-back EOL products. Moreover, linear physical programming (LPP), which has key features to entirely remove the decision maker (DM) from the process of choosing weights and to handle the vagueness of aspiration levels, is used to calculate fitness values in the GA process. A numerical example is considered to illustrate the methodology.