DOCSLIB.ORG

Vehicle Routing Problem

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Vehicle Routing Problem Vehicle Routing Problem Vehicle Routing Problem Edited by Tonci Caric and Hrvoje Gold I-Tech IV Published by In-Teh In-Teh is Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria. Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 In-teh www.in-teh.org Additional copies can be obtained from: [email protected] First published September 2008 Printed in Croatia A catalogue record for this book is available from the University Library Rijeka under no. 111225011 Vehicle Routing Problem, Edited by Tonci Caric and Hrvoje Gold p. cm. ISBN 978-953-7619-09-1 1. Vehicle Routing Problem. Tonci Caric and Hrvoje Gold Preface The Vehicle Routing Problem (VRP) dates back to the end of the fifties of the last century when Dantzig and Ramser set the mathematical programming formulation and algorithmic approach to solve the problem of delivering gasoline to service stations. Since then the interest in VRP evolved from a small group of mathematicians to the broad range of researchers and practitioners, from different disciplines, involved in this field today. The VRP definition states that m vehicles initially located at a depot are to deliver discrete quantities of goods to n customers. Determining the optimal route used by a group of vehicles when serving a group of users represents a VRP problem. The objective is to minimize the overall transportation cost. The solution of the classical VRP problem is a set of routes which all begin and end in the depot, and which satisfies the constraint that all the customers are served only once. The transportation cost can be improved by reducing the total travelled distance and by reducing the number of the required vehicles. The majority of the real world problems are often much more complex than the classical VRP. Therefore in practice, the classical VRP problem is augmented by constraints, such as vehicle capacity or time interval in which each customer has to be served, revealing the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW), respectively. In the last fifty years many real-world problems have required extended formulation that resulted in the multiple depot VRP, periodic VRP, split delivery VRP, stochastic VRP, VRP with backhauls, VRP with pickup and delivering and many others. VRP is NP hard combinatorial optimization problem that can be exactly solved only for small instances of the problem. Although the heuristic approach does not guarantee optimality, it yields best results in practice. In the last twenty years the meta-heuristics has emerged as the most promising direction of research for the VRP family of problems. This book consists of nine chapters. In the first four chapters the authors use innovative combinations of methods to solve the classically formulated VRP. The next two chapters study the VRP model as a tool for formalizing and solving problems not often found in the literature. The solving procedures in the next two chapters use the evolutionary multi- criteria optimization approach. The last chapter is focused on multi-resource scheduling problem. A brief outline of chapters is as follows: Chapter 1 “Scatter Search for Vehicle Routing Problem with Time Windows and Split Deliveries” considers the scatter search as a framework which provides a means to combine solutions, diversify, and intensify the meta-heuristic search process. The initial solution is an extension of Solomon’s sequential insertion heuristic I1. The experiments are carried out on Solomon’s problems with augmented demands which allow more than one delivery per customer. Chapter 2 “A Modelling and Optimization Framework for Real-World Vehicle Routing Problems” presents an integrated modelling and optimization framework for solving VI complex and practical VRP. The modular structure of the framework, a script based modelling language, a library of VRP related algorithms and a graphical user interface give the user both reusable components and high flexibility for rapid prototyping of complex VRP. The algorithm and the performance measure protocol is explained and implemented on the standard benchmark and on practical VRPTW problems. Chapter 3 “An Effective Search Framework Combining Meta-Heuristics to Solve the Vehicle Routing Problems with Time Windows” mainly considers combining of the two well-known methods (guided local search and tabu search) for solving VRPTW where the choice of the search method in each iteration is controlled by simulated annealing. The initial solution is obtained by push-forward insertion heuristics and the virtual vehicle heuristics. The approach is verified on the standard Solomon’s benchmark. Chapter 4 “A Hybrid Ant Colony System Approach for the Capacitated Vehicle Routing Problem and the Capacitated Vehicle Routing Problem with Time Windows” deals with hybrid ant colony with local search as the mechanism to solve CVRP and CVRPTW problems. 2-opt and saving heuristics participate in hybridization. To improve the solution the algorithm introduces the simulated annealing in the phase of the pheromones updating. Hybrid Ant Colony System is tested on the classical CVRP and VRPTW problems. Chapter 5 “Dynamic Vehicle Routing for Relief Logistics in Natural Disasters” examines the dynamic vehicle routing problem for relief logistics (DVRP-RL) in natural disasters as a support to the relief management. Two-phase heuristic, route construction and route improvement for DVRP-RL is proposed. The solution procedure resolves the current change of constraints generated by new events in the field in such a way which updates information in DVRP-RL and initiates route construction or route improvement. Differences between features of VRPTW and DVRP-RL are presented. Routing of vehicles in constrained environment of emergency is computed and analysed. Chapter 6 “Cumulative Vehicle Routing Problems” develops formulation of cumulative VRP, named CumVRP, which is based on capacitated VRP extended by the cost function defined as a product of the distance travelled and the flow on that arc. m-Travelling Repairman Problem, Energy Minimizing Vehicle Routing Problem and Average Distance- Minimizing School-bus Routing Problem can be formulated as the special cases of CumVRP. Numerical examples of CumVRP formulation focusing on the collection case of the Energy Minimizing VRP are provided. Chapter 7 “Enhancing Solution Similarity in Multi-Objective Vehicle Routing Problems with Different Demand Periods” describes the problem of multiple-objective VRP constituted of two periods with different demands. The objectives are minimization of the maximum routing time, minimization of the number of vehicles and maximization of the similarity of solutions. To obtain a similar set of solutions in the normal and high demand period two-fold evolutionary multi-criteria optimization algorithm is applied. The simulation result on a multi-objective VRP with two periods with different demands are presented. In Chapter 8 “A Multiobjectivization Approach for Vehicle Routing Problems” the single-objective optimization CVRP problem is translated into multi-objective optimization problem using the concept of multiobjectivization. On the translated problem the evolutionary multi-criteria optimization algorithm is applied. Experimental results indicate that multiobjectivization using additional objectives is more effective than using either objective alone. VII Chapter 9 “Resources Requirement and Routing in Courier Service” studies multi- resource scheduling in pickup and delivery operations occurring mostly in the courier service. The objective is to examine the possible cost savings and computational time required as delivery resources operate in some cooperative modes. Computational analysis based on the real-life and simulated data is carried out. This book presents recent improvements, innovative ideas and concepts regarding the vehicle routing problem. It will be of interest to students, researchers and practitioners with knowledge of the main methods for the solution of the combinatorial optimization problems. July 2008 Editor Tonči Carić Hrvoje Gold Uniaversity of Zagreb Faculty of Traffic and Transport Sciences Vukelićeva 4, HR-10000 Zagreb, Croatia Contents Preface V 1. Scatter Search for Vehicle Routing Problem with Time Windows 001 and Split Deliveries Patrícia Belfiore, Hugo Tsugunobu and Yoshida Yoshizaki 2. A Modelling and Optimization Framework for Real-World 015 Vehicle Routing Problems Tonči Carić1, Ante Galić, Juraj Fosin, Hrvoje Gold and Andreas Reinholz 3. An Effective Search Framework Combining Meta-Heuristics to Solve the 035 Vehicle Routing Problems with Time Windows Vincent Tam and K.T. Ma 4. A Hybrid Ant Colony System Approach for the Capacitated Vehicle 057 Routing Problem and the Capacitated Vehicle Routing Problem with Time Windows Amir Hajjam El Hassani,
Load more

Recommended publications
  • Guided Local Search
    GUIDED LOCAL SEARCH Christos Voudouris, Edward P. K. Tsang and Abdullah Alsheddy Abstract Combinatorial explosion problem is a well known phenomenon that pre- vents complete algorithms from solving many real-life combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter de- scribes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penalty-based meta-heuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighbourhood to improve the efficiency of local search. The chapter also provides guidance for implement- ing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLS- based method in each case. Key words: Heuristic Search, Meta-Heuristics, Penalty-based Methods, Guided Local Search, Tabu Search, Constraint Satisfaction. 1 INTRODUCTION Many practical problems are NP-hard in nature, which means complete, construc- tive search is unlikely to satisfy our computational demand. For example, suppose Christos Voudouris Group Chief Technology Office, BT plc, Orion Building, mlb1/pp12, Marthlesham Heath, Ipswich, IP5 3RE, United Kingdom, e-mail: [email protected] Edward P. K. Tsang Department of Computer Science, Univeristy of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom, e-mail: [email protected] Abdullah Alsheddy Department of Computer Science, Univeristy of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom, e-mail: [email protected] 1 2 Christos Voudouris, Edward P.
    [Show full text]
  • (Stochastic) Local Search Algorithms
    DM841 DISCRETE OPTIMIZATION Part 2 – Heuristics (Stochastic) Local Search Algorithms Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Local Search Algorithms Basic Algorithms Outline Local Search Revisited 1. Local Search Algorithms 2. Basic Algorithms 3. Local Search Revisited Components 2 Local Search Algorithms Basic Algorithms Outline Local Search Revisited 1. Local Search Algorithms 2. Basic Algorithms 3. Local Search Revisited Components 3 Local Search Algorithms Basic Algorithms Local Search Algorithms Local Search Revisited Given a (combinatorial) optimization problem Π and one of its instances π: 1. search space S(π) I specified by the definition of (finite domain, integer) variables and their values handling implicit constraints I all together they determine the representation of candidate solutions I common solution representations are discrete structures such as: sequences, permutations, partitions, graphs (e.g., for SAT: array, sequence of truth assignments to propositional variables) Note: solution set S 0(π) ⊆ S(π) (e.g., for SAT: models of given formula) 4 Local Search Algorithms Basic Algorithms Local Search Algorithms (cntd) Local Search Revisited 2. evaluation function fπ : S(π) ! R I it handles the soft constraints and the objective function (e.g., for SAT: number of false clauses) S(π) 3. neighborhood function, Nπ : S ! 2 I defines for each solution s 2 S(π) a set of solutions N(s) ⊆ S(π) that are in some sense close to s. (e.g., for SAT: neighboring variable assignments differ in the truth value of exactly one variable) 5 Local Search Algorithms Basic Algorithms Local Search Algorithms (cntd) Local Search Revisited Further components [according to [HS]] 4.
    [Show full text]
  • Leveraging Machine Learning to Solve the Vehicle Routing Problem with Time Windows Julie Poullet
    Leveraging Machine Learning to Solve the Vehicle Routing Problem with Time Windows by Julie Poullet M.Eng, Ecole Polytechnique (2018) Submitted to the Sloan School of Management in partial fulfillment of the requirements for the degree of Master of Science in Operations Research at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020 ○c Massachusetts Institute of Technology 2020. All rights reserved. Author...................................................................... Sloan School of Management April 30th, 2020 Certified by. Matthias Winkenbach Research Scientist Thesis Supervisor Accepted by................................................................. Patrick Jaillet Dugald C. Jackson Professor, Department of Electrical Engineering and Computer Science Co-director, Operations Research Center 2 Leveraging Machine Learning to Solve the Vehicle Routing Problem with Time Windows by Julie Poullet Submitted to the Sloan School of Management on April 30th, 2020, in partial fulfillment of the requirements for the degree of Master of Science in Operations Research Abstract The Vehicle Routing Problem with Time Windows (VRPTW) has been widely studied in the Operations Research (OR) literature given its increasingly widespread applications, rang- ing from school bus scheduling to packages delivery. In the last decades, and in large part due to the surge in e-commerce and shortened promised lead times, the scale of the highly constrained VRPTW instances encountered in real-world applications has significantly in- creased. Simultaneously, various Machine Learning (ML) methods have been developed to tackle combinatorial problems and to leverage complex data structure, but little research has been done on applying these techniques to the VRPTW. In light of this research gap, our thesis develops a process to solve large-scale VRPTW without classical OR routing by proposing a two-stage algorithm.
    [Show full text]
  • Reinforcement Learning for Solving the Vehicle Routing Problem
    Industrial and Systems Engineering Reinforcement Learning for Solving the Vehicle Routing Problem Mohammadreza Nazari1, Afshin Oroojlooy1, Lawrence V. Snyder1, and Martin Taka´cˇ1 1Lehigh University ISE Technical Report 19T-002 Reinforcement Learning for Solving the Vehicle Routing Problem Mohammadreza Nazari Afshin Oroojlooy Lawrence V. Snyder Martin Takácˇ Department of Industrial and Systems Engineering Lehigh University, Bethlehem, PA 18015 {mon314,afo214,lvs2,takac}@lehigh.edu Abstract We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. In this approach, we train a single policy model that finds near-optimal solutions for a broad range of problem instances of similar size, only by observing the reward signals and following feasibility rules. We consider a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance. On capacitated VRP, our approach outperforms classical heuristics and Google’s OR-Tools on medium-sized instances in solution quality with comparable computation time (after training). We demonstrate how our approach can handle problems with split delivery and explore the effect of such deliveries on the solution quality. Our proposed framework can be applied to other variants of the VRP such as the stochastic VRP, and has the potential to be applied more generally to combinatorial optimization problems. 1 Introduction The Vehicle Routing Problem (VRP) is a combinatorial optimization problem that has been studied in applied mathematics and computer science for decades.
    [Show full text]
  • Exploring High-Order Neighborhoods by Pattern Mining and Injection
    PILS: Exploring high-order neighborhoods by pattern mining and injection Florian Arnolda, ´Italo Santanab, Kenneth S¨orensena, Thibaut Vidalb∗ a University of Antwerp, Department of Engineering Management, Belgium fflorian.arnold,[email protected] bDepartamento de Inform´atica,Pontif´ıciaUniversidade Cat´olicado Rio de Janeiro (PUC-Rio) fisantana,[email protected] Abstract. We introduce pattern injection local search (PILS), an optimization strategy that uses pattern mining to explore high-order local-search neighborhoods, and illustrate its application on the vehicle routing problem. PILS operates by storing a limited number of frequent patterns from elite solutions. During the local search, each pattern is used to define one move in which 1) incompatible edges are disconnected, 2) the edges defined by the pattern are reconnected, and 3) the remaining solution fragments are optimally reconnected. Each such move is accepted only in case of solution improvement. As visible in our experiments, this strategy results in a new paradigm of local search, which complements and enhances classical search approaches in a controllable amount of computational time. We demonstrate that PILS identifies useful high-order moves (e.g., 9-opt and 10-opt) which would otherwise not be found by enumeration, and that it significantly improves the performance of state-of-the-art population-based and neighborhood-centered metaheuristics. Keywords. Local search, Pattern mining, Combinatorial optimization, Vehicle routing problem ∗ Corresponding author 1. Introduction Since the \no free lunch" theorem [46], it is known that no method can | on average | perform arXiv:1912.11462v1 [cs.AI] 24 Dec 2019 better than any other method on all possible problems.
    [Show full text]
  • On the Design of (Meta)Heuristics
    On the design of (meta)heuristics Tony Wauters CODeS Research group, KU Leuven, Belgium There are two types of people • Those who use heuristics • And those who don’t ☺ 2 Tony Wauters, Department of Computer Science, CODeS research group Heuristics Origin: Ancient Greek: εὑρίσκω, "find" or "discover" Oxford Dictionary: Proceeding to a solution by trial and error or by rules that are only loosely defined. Tony Wauters, Department of Computer Science, CODeS research group 3 Properties of heuristics + Fast* + Scalable* + High quality solutions* + Solve any type of problem (complex constraints, non-linear, …) - Problem specific (but usually transferable to other problems) - Cannot guarantee optimallity * if well designed 4 Tony Wauters, Department of Computer Science, CODeS research group Heuristic types • Constructive heuristics • Metaheuristics • Hybrid heuristics • Matheuristics: hybrid of Metaheuristics and Mathematical Programming • Other hybrids: Machine Learning, Constraint Programming,… • Hyperheuristics 5 Tony Wauters, Department of Computer Science, CODeS research group Constructive heuristics • Incrementally construct a solution from scratch. • Easy to understand and implement • Usually very fast • Reasonably good solutions 6 Tony Wauters, Department of Computer Science, CODeS research group Metaheuristics A Metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Instead of reinventing the wheel when developing a heuristic
    [Show full text]
  • A Novel LP-Based Local Search Technique – Fast and Quite Good –
    A Novel LP-based Local Search Technique – Fast and Quite Good – Vom Fachbereich Informatik der Technische Universit¨at Darmstadt zur Erlangung des akademischen Grades eines Dr. rer. nat. genehmigte Dissertation von Herrn Alaubek Avdil (M.Sc.) aus der Ulaanbaatar, Mongolei Referent: Professor Dr. Karsten Weihe Korreferent: Professor Dr. Matthias M¨uller-Hannemann Tag der Einreichung: 04. Juni 2009 Tag der m¨undlichen Pr¨ufung: 17. Juli 2009 Darmstadt 2009 D 17 ii Acknowledgments I want to express my deep and sincere gratitude to my supervisor Prof. Karsten Weihe for enabling my doctoral study, inspiring with scientific work and motivating and providing me pleasant research and work atmosphere. Without his trust and encouragement my research would not be done and this theses would not exist. I am extremely grateful to Prof. Matthias M¨uller-Hannemann for his invaluable support and advice on my research, excellent and critical review to improve the presentation of my work, and for his friendship, and making it possible for my young family be close with me during my doctoral study in Germany. I would like to express my warm and sincere thanks to the colleagues and members of Algorithmics Group at the Department of Computer Science of Technische Universit¨at Darmstadt for their support and valuable comments on my research. My special ackno- wledgment goes to Dr. Roland Martin for his unforgettable support and encouragement during my doctoral study, and his detailed review and constructive criticism during the writing process of my dissertation. I wish to extend my warmest thanks to all people, who have helped me with my work at the Department of Computer Science at Technische Universit¨at Darmstadt.
    [Show full text]
  • Coupling Ant Colony System with Local Search
    UNIVERSITE´ LIBRE DE BRUXELLES Ecole´ Polytechnique de Bruxelles IRIDIA - Institut de Recherches Interdisciplinaires et de D´eveloppements en Intelligence Artificielle Coupling Ant Colony System with Local Search Luca Maria Gambardella Promoteur de Th`ese: Prof. Marco DORIGO Th`esepr´esent´eeen vue de l'obtention du titre de Docteur en Sciences de l'Ing´enieur Ann´eeacad´emique2014-2015 2 Summary In the last decades there has been a lot of interest in computational models and algorithms inspired by the observation of natural systems. Nature seems to be very good at inventing processes that have many of the qualities we would like to be able to put into artificial systems. Consider for example genetic algorithms and evolutionary computation (Holland 1975): by reproducing in a simplified way the approach taken by Nature to develop living beings, researchers were able to develop both control systems for learning autonomous agents, and robust optimiza- tion algorithms for function optimization or combinatorial optimization. We can also think of work in reinforcement learning (Kaelbling et al. 1996): again, the observation of how animals learn to accomplish some simple tasks has suggested a useful and productive set of reinforcement- based learning algorithms. There are many such examples, some already very widespread, like simulated annealing (Kirkpatrick et al. 1983), ge- netic algorithms (Holland 1975), neural networks (Rumelhart et al. 1986), and others that still exist mostly in research labs, like immune networks (Bersini & F.Varela 1993) and classifier systems (Holland & Reitman 1978). In this thesis we present another ethological approach to combinatorial optimization, which has been inspired by the observation of ant colonies: the Ant Colony System (ACS, Gambardella & Dorigo 1996; Dorigo & Gambardella 1997).
    [Show full text]
  • Clever Algorithms Nature-Inspired Programming Recipes Ii
    Jason Brownlee Clever Algorithms Nature-Inspired Programming Recipes ii Jason Brownlee, PhD Jason Brownlee studied Applied Science at Swinburne University in Melbourne, Australia, going on to complete a Masters in Information Technology focusing on Niching Genetic Algorithms, and a PhD in the field of Artificial Immune Systems. Jason has worked for a number of years as a Consultant and Software Engineer for a range of Corporate and Government organizations. When not writing books, Jason likes to compete in Machine Learning competitions. Cover Image © Copyright 2011 Jason Brownlee. All Reserved. Clever Algorithms: Nature-Inspired Programming Recipes © Copyright 2011 Jason Brownlee. Some Rights Reserved. Revision 2. 16 June 2012 ISBN: 978-1-4467-8506-5 This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License. The full terms of the license are located online at http://creativecommons.org/licenses/by-nc-sa/2.5/au/legalcode Webpage Source code and additional resources can be downloaded from the books companion website online at http://www.CleverAlgorithms.com Contents Foreword vii Preface ix I Background1 1 Introduction3 1.1 What is AI.........................3 1.2 Problem Domains...................... 10 1.3 Unconventional Optimization............... 13 1.4 Book Organization..................... 17 1.5 How to Read this Book.................. 20 1.6 Further Reading...................... 21 1.7 Bibliography........................ 22 II Algorithms 27 2 Stochastic Algorithms 29 2.1 Overview.......................... 29 2.2 Random Search....................... 30 2.3 Adaptive Random Search................. 34 2.4 Stochastic Hill Climbing.................. 40 2.5 Iterated Local Search.................... 44 2.6 Guided Local Search.................... 50 2.7 Variable Neighborhood Search..............
    [Show full text]
  • A Guided Tabu Search for the Vehicle Routing Problem with Two-Dimensional Loading Constraints
    Available online at www.sciencedirect.com European Journal of Operational Research 195 (2009) 729–743 www.elsevier.com/locate/ejor A Guided Tabu Search for the Vehicle Routing Problem with two-dimensional loading constraints Emmanouil E. Zachariadis a, Christos D. Tarantilis b,*, Christos T. Kiranoudis a a Department of Process Analysis and Plant Design, Department of Chemical Engineering, National Technical University of Athens, Athens, Greece b Department of Management Science and Technology, Athens University of Economics and Business, Hydras Street 28, Athens 11362, Greece Received 12 January 2007; accepted 31 May 2007 Available online 21 November 2007 Abstract We present a metaheuristic methodology for the Capacitated Vehicle Routing Problem with two-dimensional loading constraints (2L- CVRP). 2L-CVRP is a generalisation of the Capacitated Vehicle Routing Problem, in which customer demand is formed by a set of two- dimensional, rectangular, weighted items. The purpose of this problem is to produce the minimum cost routes, starting and terminating at a central depot, to satisfy the customer demand. Furthermore, the transported items must be feasibly packed into the loading surfaces of the vehicles. We propose a metaheuristic algorithm which incorporates the rationale of Tabu Search and Guided Local Search. The loading aspects of the problem are tackled using a collection of packing heuristics. To accelerate the search process, we reduce the neigh- bourhoods explored, and employ a memory structure to record the loading feasibility information. Extensive experiments were con- ducted to calibrate the algorithmic parameters. The effectiveness of the proposed metaheuristic algorithm was tested on benchmark instances and led to several new best solutions.
    [Show full text]
  • Ant Colony Optimization Marco Dorigo and Thomas Stützle Ant Colony Optimization
    Ant Colony.qxd 6/9/04 12:15 PM Page 1 Ant Colony Optimization Marco Dorigo and Thomas Stützle Optimization Ant Colony The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic Ant Colony technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant Optimization colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioin- formatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends Marco Dorigo and Thomas Stützle with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
    [Show full text]
  • A Pointer Neural Network for the Vehicle Routing Problem with Task
    Information Technology and Control 2020/2/49 237 ITC 2/49 A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources Information Technology and Control Received 2019/11/13 Accepted after revision 2020/03/22 Vol. 49 / No. 2 / 2020 pp. 237-248 DOI 10.5755/j01.itc.49.2.24613 http://dx.doi.org/10.5755/j01.itc.49.2.24613 HOW TO CITE: Ma, H., Sheng, Y., Xia, W. (2020). A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources. Information Technology and Control, 49(1), 237-248. https://doi.org/10.5755/j01.itc.49.2.24613 A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources Huawei Ma, Yuxiang Sheng, Wei Xia School of Management, Hefei University of Technology, Hefei 230000, P.R. China, e-mails: [email protected], [email protected], [email protected] Corresponding author: [email protected] The vehicle routing problem with task priority and limited resources (VRPTPLR) is a generalized version of the vehicle routing problem (VRP) with multiple task priorities and insufficient vehicle capacities. The objec- tive of this problem is to maximize the total benefits. Compared to the traditional mathematical analysis meth- ods, the pointer neural network proposed in this paper continuously learns the mapping relationship between input nodes and output decision schemes based on the actual distribution conditions. In addition, a global at- tention mechanism is adopted in the neural network to improve the convergence rate and results. To verify the effectiveness of the method, we model the VRPTPLR and compare the results with those of genetic algorithm and differential evolution algorithm.
    [Show full text]