Fastest Complete Vehicle Routing Problem Using Learning Multiple Ant Colony Algorithm

Si Yuan Wen; Ying Li

doi:10.4028/www.scientific.net/AMR.217-218.1044

Paper Titles

Design on the Testing System of the Towing Tank
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Prediction of Oilfield Cementing Quality Based on Levenberg-Marquardt BP Algorithm
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Calibration of Pipe Friction Factor Based on Improved Genetic Algorithm
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Suspended Load Prediction on Sucker Rod Suspension Load Based on Artificial Neural Network
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Fastest Complete Vehicle Routing Problem Using Learning Multiple Ant Colony Algorithm
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Effects of Plasma Nitriding and TiN Coating Duplex Treatment on Wear Resistance of Commercially Pure Titanium
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Practices and Research on Goal Setting of Software Performance Engineering
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Forward Displacement Analysis of a Parallel Manipulator
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Study on Glass Transition Temperature of Polyvinylamine Hydrochloride
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 217-218Fastest Complete Vehicle Routing Problem Using...

Fastest Complete Vehicle Routing Problem Using Learning Multiple Ant Colony Algorithm

Abstract:

The objective of fastest completed vehicle routing problem (FCVRP) is to minimum complete time, this kind VRP problem was applied in emergency management and quick response supply chain management. Multiple ant colony algorithm for this problem is studied in this paper. The conception of FCVRP is given and then vehicle allocation method and objective function computing method are given to convert FCVRP problem to optimal alignment problem. At same time one local search method of this problem is introduced. Then learning multiple ant colony algorithm is bring forward to solve FCVRP and one numerical example is solved by this algorithm at last.