Multiobjective approach to the vehicle routing problem with demand responsive transport

The Vehicle Routing Problem (VRP) has been largely studied over the last years, since problems involving the transport of persons and/or goods have great practical application. This paper addresses the Vehicles Routing Problem with Demand Responsive Transport (VRPDRT), a type of transport which enables customers to be taken to your destination like a taxi or minibus in order to reduce operating costs and to meet customer needs. A multiobjective approach is proposed to VRPDRT in which five different objective functions are used. Using an iterative methodology, known as aggregation tree, the objective functions are used to construct a bi-objective version for the problem. The proposed bi-objective optimization problem is solved via NSGA-II and SPEA2 and the algorithm performances are compared using S-Metric. Through a statistical test, the results shows with 95% of confidence that the NSGA-II presents better convergence when compared with SPEA2.

[1]  Antonio LaTorre,et al.  A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city , 2013, Expert Syst. Appl..

[2]  Patrizia Beraldi,et al.  Designing robust routes for demand-responsive transport systems , 2014 .

[3]  Carlos A. Brizuela,et al.  A survey on multi-objective evolutionary algorithms for many-objective problems , 2014, Computational Optimization and Applications.

[4]  J. D. Schaffer,et al.  Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition) , 1984 .

[5]  Ramasamy Panneerselvam,et al.  A Time-Dependent Vehicle Routing Problem with Time Windows for E-Commerce Supplier Site Pickups Using Genetic Algorithm , 2015 .

[6]  Khaled Ghédira,et al.  A multi-objective simulated annealing for the multi-criteria dial a ride problem , 2010, IFAC HMS.

[7]  Frederico G. Guimarães,et al.  Aggregation Trees for visualization and dimension reduction in many-objective optimization , 2015, Inf. Sci..

[8]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[9]  J. K. Lenstra,et al.  Complexity of vehicle routing and scheduling problems , 1981, Networks.

[10]  Puca Huachi Vaz Penna,et al.  Um Algoritmo Heurístico Baseado em Iterated Local Search para Problemas de Roteamento de Veículos , 2013 .

[11]  George B. Dantzig,et al.  The Truck Dispatching Problem , 1959 .

[12]  Mohamed Reghioui,et al.  Heuristics and memetic algorithm for the two-dimensional loading capacitated vehicle routing problem with time windows , 2013, Central Eur. J. Oper. Res..

[13]  Anand Subramanian,et al.  Branch-cut-and-price for the vehicle routing problem with simultaneous pickup and delivery , 2013, Optim. Lett..

[14]  Michaël Schyns,et al.  An ant colony system for responsive dynamic vehicle routing , 2015, Eur. J. Oper. Res..

[15]  Mariano Frutos,et al.  A New Approach to the Optimization of the CVRP through Genetic Algorithms , 2012 .

[16]  Arnaud Liefooghe,et al.  Solving a Dial-a-Ride Problem with a Hybrid Multi-objective Evolutionary Approach: Application to Demand Responsive Transport , 2012 .

[17]  Giovanni Rinaldi,et al.  Computational results with a branch and cut code for the capacitated vehicle routing problem , 1998 .

[18]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[19]  Emanuel Ey,et al.  Solutions in Under 10 Seconds for Vehicle Routing Problems with Time Windows Using Commodity Computers , 2015, EMO.

[20]  Bo Guo,et al.  The capacitated vehicle routing problem with stochastic demands and time windows , 2011, Comput. Oper. Res..

[21]  R. Rosenberg.  Simulation of genetic populations with biochemical properties : technical report , 1967 .

[22]  G. Laporte,et al.  Exact Algorithms for the Vehicle Routing Problem , 1987 .

[23]  José Reginaldo Hughes Carvalho,et al.  Genetic Algorithm Approach for a Class of Multi-Criteria, Multi-Vehicle Planner of UAVs , 2015, EMO.

[24]  Renato F. Werneck,et al.  Robust Branch-and-Cut-and-Price for the Capacitated Vehicle Routing Problem , 2006, Math. Program..

[25]  Roberto Roberti,et al.  Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints , 2012, Eur. J. Oper. Res..

[26]  Mingyuan Chen,et al.  Solving reverse logistics vehicle routing problems with time windows , 2013 .

[27]  Mitsuo Gen,et al.  A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries , 2010 .

[28]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[29]  Voratas Kachitvichyanukul,et al.  Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem , 2009, Comput. Ind. Eng..

[30]  W. Y. Szeto,et al.  An artificial bee colony algorithm for the capacitated vehicle routing problem , 2011, Eur. J. Oper. Res..

[31]  Shangyao Yan,et al.  A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem , 2012, Comput. Ind. Eng..