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Rich Vehicle Routing: from a Set of Tailored Methods to a Generic

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Rich Vehicle Routing: from a Set of Tailored Methods to a Generic Randomized Algorithms for Rich Vehicle Routing Problems: From a Specialized Approach to a Generic Methodology Jos´ede Jes´usC´aceresCruz Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya - Internet Interdisciplinary Institute (UOC-IN3) Directors: Dr. Angel A. Juan P. & Dr. Daniel Riera T. Commitee Member: Dr. Helena R. Louren¸co PhD. Thesis Information and Knowledge Society Doctoral Programme Network and Information Technologies July 2013 ii Abstract The Vehicle Routing Problem (VRP) is a well known domain in optimization research community. Its different basic variants have been widely explored in the literature. Some studies have considered specific combinations of real- life constraints to define the emerging Rich VRP scopes. This work deals with the integration of heuristics, biased probability, simulation, parallel & distributed computing techniques, and constraint programming. The pro- posed approaches are tested for solving some variants of VRPs, namely, first, the deterministic families: Heterogeneous VRP (HVRP), Heteroge- neous VRP with Variable cost (HVRP-V), Heterogeneous fleet VRP with Multi-trips (HVRPM), Asymmetric cost matrix VRP (AVRP), Heteroge- neous fleet with Asymmetric cost matrix VRP (HAVRP), VRP with Time Windows (VRPTW), and Distance-Constrained VRP (DCVRP); second, the stochastic nature families: VRP with Stochastic Demands (VRPSD), and Inventory Routing Problem with Stochastic Demands (IRPSD). An extensive literature review is performed for all these variants, focusing on the main contributions of each work. A first approach proposes a biased- randomization of classical heuristics for solving the deterministic problems addressed here. A second approach is centered on the combination of ran- domized heuristics with simulation (Simheuristics) to be applied on the commented stochastic problems. Finally, a third approach based on the joined work of randomized heuristics with constraint programming is pro- posed to solve several types of routing problems. The developed heuristic algorithms are tested in several benchmark instances |between these, two real-life case studies in Spain are considered| and the results obtained are, on average, highly promising and useful for decision makers. Keywords: Rich Vehicle Routing Problems, Biased Randomized Heuristics, Metaheuristics, Real-Life Applications, Optimization, Logistics. Resumen El Problema de Enrutamiento de Veh´ıculos(VRP) y sus diferentes variantes b´asicasson un dominio ampliamente estudiado en la comunidad cient´ıfica de optimizaci´on.Algunos estudios han utilizado combinaciones espec´ıficas de restricciones encontradas en la vida real para definir los emergentes VRP Enriquecidos. Este trabajo aborda la integraci´onde heur´ısticas,probabil- idad sesgada, simulaci´on,t´ecnicasde computaci´ondistribuida & parale- las, y programaci´oncon restricciones. Los enfoques propuestos han solu- cionado algunas variantes del VRP: en primer lugar, las familias determin- istas: VRP con flotas Heterog´eneas(HVRP), VRP con flotas Heterog´eneas y costo variable (HVRP-V), VRP con flota Heterog´eneay M´ultiplesviajes (HVRPM), VRP con matriz de costo Asim´etrica(AVRP), VRP con flota Heterog´eneay matriz de costo Asim´etrica(HAVRP), VRP con ventanas de Tiempo (VRPTW), y VRP Distancia limitada (DCVRP); en segundo lu- gar, las familias de naturaleza estoc´astica:VRP con Demandas estoc´asticas (VRPSD), y Problemas de Inventario y Enrutamiento de Veh´ıculoscon De- mandas estoc´asticas(IRPSD). Una extensa revisi´onbibliogr´aficase ha re- alizado para cada una de estas variantes. Un primer enfoque propone la combinaci´onde una aleatorizaci´onsesgada con heur´ısticascl´asicaspara la soluci´onde problemas deterministas. Un segundo enfoque se centra en la combinaci´onde heur´ısticasaleatorias con simulaci´on(Simheuristics) para ser aplicados sobre los problemas estoc´asticoscomentados. Por ´ultimo,se propone un tercer enfoque basado en el trabajo conjunto de heur´ısticas aleatorias con programaci´onde restricciones para resolver varios tipos de problemas de enrutamiento. Los algoritmos heur´ısticosdesarrollados han sido aplicados en varios casos de referencia |entre ellos, dos estudios de casos reales de distribuci´onen Espa~na|y los resultados obtenidos son, en general, prometedores y ´utilespara los decisores. P alabras claves: Problemas Enriquecidos de Enrutamiento de Veh´ıculos, Heur´ısticas Aleatorias y Sesgadas, Metaheur´ısticas, Aplicaciones Reales, Optimizaci´on,Log´ıstica. To my lovely family and mainly to next generations (Mia, Mathias and Jorge Alejandro) who would have more opportunities and the duty of making the right decisions. To all dreams of latino-american students. In memory of my father. Aimer; c0est agir (Victor Hugo). Acknowledgements Foremost, I would like to express my sincere gratitude to my advisors Dr. Angel Juan and Dr. Daniel Riera for their continuous support of my re- search, for his patience, motivation, enthusiasm, and knowledge. I could not have imagined having a better mentors for my Ph.D study. Besides my di- rectors, I would like to thank my external advisor: Dr. Helena R. Louren¸co for her encouragement, insightful comments, and challenging remarks. To the Doctoral Grant Programme of UOC-IN3 and all people inside of this institution: Josep Llados, Jordi Ferran, Yolanda Gonz´alez,Lin Win, Isabel Egea, Monserrat Mir, Felisa Cabeza, Andrea, Alejandra P´erez, etc. Thanks to all PhD candidates of the programme (new and old generations) to share the good and hard moments of this path: Jordi, Denise, Debora, Nadia, Guillem, Sergio, Ola, Marc, Cecilia, etc. Particularly, thanks to Enosha and Barry for helping me with patience to improve my english. Also thanks to all scientific collaborators who help me to develop the content of this thesis, specially to Daniel Guimarans and Pol Arias to show me the path of constraint programming. The work in this thesis has been also part of the MICINN project with reference TRA2010-21644-C03-02 (\Algor´ıtmosy software distribuido para el Dise~node Rutas Optimas´ en PYMES") of my research group DPCS for the \Plan Nacional I+D+i 2011-2013", the CYTED-HAROSA Net- work (CYTED2010-511RT0419), in the context of the IN3-ICSO program and the IN3-HAROSA Knowledge Community (http://dpcs.uoc.edu). Also thanks to contacted people in distribution companies involved on the studies of this thesis for their time and cooperation. Gracias a toda mi familia quienes siempre me han brindado incondicional- mente su apoyo y cari~nopara recorrer este exigente camino de la academia: mi madre Margarita; tias Thialy, Mima, Noris y Thamara; tios Carlos, Roque, Goyo y Luis; primos Jesus Enrique, Lucho y Carlos; y primas Dayana y Oriana; gracias por siempre estar ah´ıy ense~narmeque el esp´ıritu del venezolano es luchador y generoso por naturaleza propia. Gracias en particular a la familia Reyes-Zuloaga (en especial a Marcos) por todo el apoyo brindado durante el desarrollo del doctorado. Gracias a todos mis amigos esparcidos entre Europa y Am´erica. A esos amigos venezolanos que me han ayudado a extender el concepto de familia a nuevos l´ımites:Victor, Roselyn, Alejandro y Aleana. A los catalanes por recibirme en su tierra (Albert). En especial a mis compa~nerodel voleyball en Panteres Grogues de Barcelona quienes con su dinamismo y esp´ıritu entusiasta me han concedido un espacio lo suficientemente abierto y diverso como para mantener mi mente y cuerpo en sano equilibrio durante mi es- tad´ıaen la ciudad de las grandes obras de Gaud´ı.F´elix,gracias por dise~nar la portada. Merci ´ames cher amis en France: Edwin, Gloria, Alfonso, Quentin et Jan, pour toujours me donner de l'´energieet de la espoir pour suivre ce long parcours. x Contents List of Figures xv List of Tables xix 1 Introduction 1 1.1 Structure of this Thesis . .4 1.2 Relevance of this Topic . .5 1.3 Objectives . .8 1.4 Chapter Conclusions . 10 2 Capacitated VRP 11 2.1 Definition . 11 2.2 VRP Variants . 14 2.3 Chapter Conclusions . 16 3 VRP Methodologies 17 3.1 Exact Methods . 18 3.2 Approximate Methods . 19 3.3 Chapter Conclusions . 21 4 Rich VRPs 23 4.1 Definition . 24 4.2 Literature Review . 27 4.3 Classification of Rich VRP papers . 34 4.4 Chapter Conclusions . 41 xi CONTENTS 5 Biased Randomization of Heuristics 43 5.1 Background . 44 5.2 Applying a Biased Randomization . 48 5.3 Benefits . 51 5.4 Chapter Conclusions . 52 6 Heterogeneous VRPs 55 6.1 Definition . 56 6.2 Literature Review . 57 6.3 Proposed Approach . 59 6.4 Computational Results . 66 6.4.1 HVRP with Variable Costs . 70 6.5 Real Case I: HVRP with Multiple Trips . 70 6.5.1 Computational Results . 73 6.5.2 Sub-case: HVRPM with Real Cost Function . 77 6.6 HVRP Sensibility Analysis . 81 6.6.1 Proposed Approach . 81 6.6.2 Experimental Design . 84 6.6.3 Computational Results . 85 6.7 Chapter Conclusions . 92 7 Heterogeneous and Asymmetric VRPs 95 7.1 Literature Review . 96 7.2 Proposed Approach . 97 7.2.1 Asymmetric Local Searches . 100 7.3 Computational Results . 101 7.3.1 AVRP . 103 7.3.2 HAVRP . 105 7.4 Real Case II: HAVRP with Extra Constraints . 110 7.4.1 Proposed Approach . 111 7.4.2 Computational Results . 112 7.4.3 Sub-case: New Extra Constraints . 115 7.5 Chapter Conclusions . 120 xii CONTENTS 8 VRPs with Time Windows 121 8.1 Definition . 121 8.2 Literature Review . 122 8.3 Proposed Approach . 123 8.4 Computational Results . 125 8.5 Future lines . 126 8.6 Chapter Conclusions . 128 9 Simheuristics 129 9.1 Background . 130 9.2 Building a Simheuristic . 133 9.3 Benefits . 135 9.4 Cooperative and Distributed Approaches . 136 9.4.1 Related work of PDCS for VRP . 139 9.5 Chapter Conclusions . 145 10 VRPs with Stochastic Demands 147 10.1 Definition . 148 10.2 Literature review . 149 10.3 Proposed Approach . 151 10.4 Computational Results . 156 10.5 Chapter Conclusions . 164 11 Inventory Routing Problem with Stochastic Demands 165 11.1 Definition .
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