Optimal Routing and Scheduling in Transportation: Using Genetic
General solution approaches for multi-attribute vehicle routing and scheduling problems
2
4OR-Q J Oper Res DOI 10.1007/s10288-013-0240-5 PHD THESIS General solution approaches for multi-attribute vehicle routing and scheduling problems Thibaut Vidal Received: 3 April 2013 / Revised: 11 April 2013 © Springer-Verlag Berlin Heidelberg 2013 This is a short abstract of the author’s PhD thesis entitled “Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d’horaires”, conducted jointly in LOSI, Université de Technologie de Troyes, France, and CIRRELT, Université de Montréal, Canada. The thesis has been supervised by Teodor Gabriel Crainic, Michel Gendreau and Christian Prins. The defense took place in Troyes on December 10th, 2012. The full text is available at http://w1.cirrelt. ca/~vidalt/ and by mail request to [email protected]. The thesis introduction and conclusion are written in French, and the chapters are in English. An additional English introduction is provided on the website. This work introduces new general- purpose methods to efficiently deal with a wide class of vehicle routing and scheduling problems. The Vehicle Routing Problem (VRP) involves designing least cost delivery routes to service a geographically-dispersed set of customers while taking into account vehicle- capacity constraints. This NP-hard combinatorial optimization problem is linked with multiple applications in logistics, telecommunications, robotics, crisis management in military and humanitarian frameworks, among others. Yet, practical routing applica- tions are usually quite distinct from the academic definition of the problem, encompass- ing additional sets of specific constraints, objectives and decisions, called attributes, which breed further new problem variants. These attributes aim to capture a higher level of system detail or decision choices, including but not limited to richer system struc- tures (e.g., several depots, vehicle fleets, and commodities), customer requirements (time windows for deliveries) and vehicle operation rules (e.g., load placement, route restrictions and driver work rules). The resulting “Multi-Attribute” Vehicle Routing Problems (MAVRP) are the support of a vast literature which, however, lacks unified methods capable of addressing multiple MAVRP. In addition, some rich VRPs, i.e. those that involve several attributes, can be difficult to address because of the wide array of combined and possibly antagonistic decisions they require. This thesis contributes to address these challenges by means of problem structure analysis, new metaheuristics and unified method developments. The “winning strate- gies” of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a Published online: 05 May 2013 123
Transcript of General solution approaches for multi-attribute vehicle routing and scheduling problems
4OR-Q J Oper ResDOI 10.1007/s10288-013-0240-5
PHD THESIS
General solution approaches for multi-attribute vehiclerouting and scheduling problems
Thibaut Vidal
Received: 3 April 2013 / Revised: 11 April 2013© Springer-Verlag Berlin Heidelberg 2013
This is a short abstract of the author’s PhD thesis entitled “Approches générales derésolution pour les problèmes multi-attributs de tournées de véhicules et confectiond’horaires”, conducted jointly in LOSI, Université de Technologie de Troyes, France,and CIRRELT, Université de Montréal, Canada. The thesis has been supervised byTeodor Gabriel Crainic, Michel Gendreau and Christian Prins. The defense took placein Troyes on December 10th, 2012. The full text is available at http://w1.cirrelt.ca/~vidalt/ and by mail request to [email protected]. The thesis introductionand conclusion are written in French, and the chapters are in English. An additionalEnglish introduction is provided on the website. This work introduces new general-purpose methods to efficiently deal with a wide class of vehicle routing and schedulingproblems.
The Vehicle Routing Problem (VRP) involves designing least cost delivery routes toservice a geographically-dispersed set of customers while taking into account vehicle-capacity constraints. This NP-hard combinatorial optimization problem is linked withmultiple applications in logistics, telecommunications, robotics, crisis management inmilitary and humanitarian frameworks, among others. Yet, practical routing applica-tions are usually quite distinct from the academic definition of the problem, encompass-ing additional sets of specific constraints, objectives and decisions, called attributes,which breed further new problem variants. These attributes aim to capture a higher levelof system detail or decision choices, including but not limited to richer system struc-tures (e.g., several depots, vehicle fleets, and commodities), customer requirements(time windows for deliveries) and vehicle operation rules (e.g., load placement, routerestrictions and driver work rules). The resulting “Multi-Attribute” Vehicle RoutingProblems (MAVRP) are the support of a vast literature which, however, lacks unifiedmethods capable of addressing multiple MAVRP. In addition, some rich VRPs, i.e.those that involve several attributes, can be difficult to address because of the widearray of combined and possibly antagonistic decisions they require.
This thesis contributes to address these challenges by means of problem structureanalysis, new metaheuristics and unified method developments. The “winning strate-gies” of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a
Published online: 05 May 2013 123
T. Vidal
unifying review. Another analysis is targeted on “timing” problems and algorithms foradjusting the execution dates of a given sequence of tasks. Such methods, indepen-dently studied in different research domains related to routing, scheduling, resourceallocation, and even isotonic regression are here surveyed in a multidisciplinary review.
A Hybrid Genetic Search with Advanced Diversity Control (HGSADC) is thenintroduced, which combines the exploration breadth of population-based evolutionarysearch, the aggressive-improvement capabilities of neighborhood-based metaheuris-tics, and a bi-criteria evaluation of solutions based on cost and diversity measures.Results of remarkable quality are achieved on classic benchmark instances of thecapacitated VRP, the multi-depot VRP, and the periodic VRP. Further extensions ofthe method to VRP variants with constraints on time windows, limited route duration,and truck drivers’ statutory pauses are also proposed. New route and neighborhoodevaluation procedures are introduced to manage penalized infeasible solutions w.r.t.to time-window and duration constraints. Tree-search procedures are used for drivers’rest scheduling, as well as advanced search limitation strategies, memories and decom-position phases. A dynamic programming-based neighborhood search is introducedto optimally select the depot, vehicle type, and first customer visited in the route dur-ing local searches. The notable contribution of these new methodological elements isassessed within two different metaheuristic frameworks.
To further advance general-purpose MAVRP methods, we introduce a component-based heuristic resolution framework and a Unified Hybrid Genetic Search (UHGS),which relies on modular self-adaptive components for addressing problem specifics.Computational experiments demonstrate the groundbreaking performance of UHGS.With a single implementation, unique parameter setting and termination criterion, thisalgorithm matches or outperforms all current problem-tailored methods from morethan 180 articles, on 26 vehicle routing variants and 39 benchmark sets. To addressrich problems, UHGS was included in a new parallel cooperative solution frameworkcalled “Integrative Cooperative Search (ICS)”, based on problem decompositions,partial solutions integration, and global search guidance.
This compendium of results provides a novel view on a wide range of MAVRPand timing problems, on efficient heuristic searches, and on general-purpose solutionmethods for combinatorial optimization problems.
123
