Small profit margins and cost pressure in the road transportation business require an efficient planning of operations on the part of trucking companies. Complicating aspects such as legal restrictions, specific customer requirements, or the necessity of responding quickly to unforeseen events disqualify manual planning entirely, and even off-the-shelf route planning software can be inappropriate for setting up both feasible and cost-effective transportation plans in a certain business context. Vendors of sophisticated, customized decision support systems that provide support on different planning levels are confronted with the task of tailoring embedded vehicle routing algorithms to the specific demands and business rules of their clients. For such purposes we proposed the concept and design of a flexible metaheuristic framework to facilitate and accelerate the development of solution methods for a wide range of rich vehicle routing problems (VPR) arising in diverse industries. Part I provided an introduction into the field of VRPs and metaheuristic solution methods. Our framework is based on solution methods for the standard VRP and consequently, we started with defining this basic variant of the VRP class. Then, we presented an overview of real-world extensions to the VRP - the class of rich VRPs. Partly, this overview served as an illustration of the relevance of flexible and adaptable solution approaches, but it also served as the foundation of our requirements analysis in the second part of the thesis. We introduced common metaheuristics from the literature and explained those local search and large neighborhood search techniques in detail upon which our framework is based. Part II was dedicated to the framework itself. Reviewing existing publications on VRP frameworks we first documented that there is a lack of flexible approaches that explicitly address the solution of wide ranges of VRPs. We formulated requirements for a good framework, including the four attributes flexibility, simplicity, accuracy, and speed, as well as characteristics of rich VRPs that it should be able to take account of. Aiming to meet these requirements we presented the architecture and main concepts of our framework, mainly on a pseudocode level: we defined the five implemented standard VRP heuristics and the mechanisms available for adapting these heuristics to rich VRPs. The central aspect is the separation of standard VRP operations and specific VRP operations by user-definable checking and post-processing functions. Part III finally dealt with the evaluation of our framework, mainly regarding flexibility, accuracy, and speed, by developing customized metaheuristics for five rich VRPs.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A flexible metaheuristic framework for solving rich vehicle routing problems


    Contributors:


    Publication date :

    2011


    Size :

    185 Seiten, Bilder, Tabellen, 111 Quellen




    Type of media :

    Theses


    Type of material :

    Print


    Language :

    English




    General Metaheuristic Algorithm for a Set of Rich Vehicle Routing Problems

    Qi, Mingyao / Peng, Cheng / Huang, Xiaoyu | Transportation Research Record | 2016


    Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms

    Filip Taner / Ante Galić / Tonči Carić | DOAJ | 2012

    Free access

    Drone Arc Routing Problems and Metaheuristic Solution Approach

    Islam Altin / Aydin Sipahioglu | DOAJ | 2024

    Free access

    Rich Vehicle Routing Problems and Applications

    Wen, Min | BASE | 2010

    Free access