A Mutualism Quantum Genetic Algorithm to Optimize the Flow Shop Scheduling with Pickup and Delivery ConsiderationsReport as inadecuate




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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 387082, 17 pages -

Research Article

School of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China

School of Mathematics, Shanghai University of Finance and Economics, Shanghai 200433, China

Research Institute of Automation, East China University of Science and Technology, Shanghai 200237, China

Received 5 June 2014; Revised 3 November 2014; Accepted 11 November 2014

Academic Editor: Kang Li

Copyright © 2015 Jinwei Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A mutualism quantum genetic algorithm MQGA is proposed for an integrated supply chain scheduling with the materials pickup, flow shop scheduling, and the finished products delivery. The objective is to minimize the makespan, that is, the arrival time of the last finished product to the customer. In MQGA, a new symbiosis strategy named mutualism is proposed to adjust the size of each population dynamically by regarding the mutual influence relation of the two subpopulations. A hybrid Q-bit coding method and a local speeding-up method are designed to increase the diversity of genes, and a checking routine is carried out to ensure the feasibility of each solution; that is, the total physical space of each delivery batch could not exceed the capacity of the vehicle. Compared with the modified genetic algorithm MGA and the quantum-inspired genetic algorithm QGA, the effectiveness and efficiency of the MQGA are validated by numerical experiments.





Author: Jinwei Gu, Manzhan Gu, and Xingsheng Gu

Source: https://www.hindawi.com/



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