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Improved population-based learning algorithm for solvingvehicle routing problem with soft time windows


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Improved population-based learning algorithm for solvingvehicle routing problem with soft time windows
Xie YongHu RongQian BinChen ShaofengZhang GuilianZhang Xiaodi
Faculty of Information Engineering and Automation,Kunming University ofScience and Technology,Kunming 650500,China
population-based incremental learning algorithm vehicle routing problem with soft time windows model of probability high quality solution region in the solution space total transportation cost effectiveness global search
An improved population-based incremental learning algorithm,short for IPBIL,is proposed to solve the vehicle routing problem with soft time windows(VRPSTW)for optimizing the total transportation cost.A new three-dimensional population incremental learning model is proposed to lead the algorithm to perform global search,find high quality solution region in the solution space.An exchange operation,which is based on the client distance and the penalty cost correlation degree,is designed to further improve the quality of solutions.An insertion and reverse operation based on the character of the time windows problem is proposed for detailed search in high-quality solution space.Through the simulation experiment and the comparison of algorithms,the proposed IPBIL is verified effectively.


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Last Update: 2016-02-29