Master of Science in Computer Science May 2020 A New Approach for Solving the Disruption in Vehicle Routing Problem During the Delivery A Comparative Analysis of VRP Meta-Heuristics Sai Chandana Kaja Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science. The thesis is equivalent to 20 weeks of full-time studies. I declare that I am the sole author of this thesis and have not used any sources other than those listed in the bibliography and identified as references. I further declare that I have not submitted this thesis at any other institution to obtain a degree. Contact Information: Author(s): Sai Chandana Kaja E-mail: [email protected] University advisor: Dr. Julia Sidorova Department of Computer Science Blekinge Institute of Technology, Karlskrona, Sweden Faculty of Computing Internet : www.bth.se Blekinge Institute of Technology Phone : +46 455 38 50 00 SE–371 79 Karlskrona, Sweden Fax : +46 455 38 50 57 Abstract Context. The purpose of this research paper is to describe a new approach for solving the disruption in the vehicle routing problem (DVRP) which deals with the disturbance that will occur unexpectedly within the distribution area when executing the original VRP plan. The paper then focuses further on the foremost common and usual problem in real-time scenarios i.e., vehicle-breakdown part. Therefore, the research needs to be accomplished to deal with these major dis- ruption in routing problems in transportation. Objectives. The study first investigates to find suitable and efficient meta- heuristic techniques for solving real-time vehicle routing problems than an exper- iment is performed with the chosen algorithms which might produce near-optimal solutions. Evaluate the performance of those selected algorithms and compare the results among each other. Methods. To answer research questions, firstly, a literature review has been per- formed to search out suitable meta-heuristic techniques for solving vehicle routing problems. Then based on the findings an experiment is performed to evaluate the performance of selected meta-heuristic algorithms. Results. Results from the literature review showed that the meta-heuristic ap- proaches such as. Tabu Search, Ant Colony Optimization and Genetic Algorithm are suitable and efficient algorithms for solving real-time vehicle routing prob- lems. The performance of those algorithms has been calculated and compared with one another with standard benchmarks. Conclusions. The performance of a Tabu Search algorithm is best among the other algorithms, followed by Ant Colony Optimization and Genetic Algorithm. Therefore, it has been concluded that the Tabu Search is the best algorithm for solving real-time disruption problems in VRP. The results are similar to the per- formance comparison of the selected algorithms and standard benchmarks are presented within the research. Keywords: vehicle routing problem, disruption, vehicle breakdown, solution algorithms, meta-heuristics, Tabu Search. Acknowledgments It is a pleasure for me to express my special thanks and gratitude to my master thesis supervisor, Dr. Julia Sidorova for her expertise, remarkable supervision, and continuous support. I am thankful to her for making this research valuable. This thesis would not have been possible without her motivation and guidance. I must express my very profound gratitude to my parents Venkateshwar Rao & Surekha, for their unconditional support and continuous encouragement through- out my years of study. Lastly, I would also like to thank my friends Sai Krishna and Advaith for their guidance and support throughout the research. ii List of Abbreviations DVRP - Disruption in Vehicle Routing Problem VRP - Vehicle Routing Problem CVRP - Capacitated Vehicle Routing Problem TSP - Traveling Salesman Problem CVRPLIB - Capacitated Vehicle Routing Problem Library TS - Tabu Search ACO - Ant Colony Optimization GA - Genetic Algorithm GRASP - Greedy Randomize Adaptive Search Procedure OR - Operational Research CO - Combinatorial Optimization EV’s - Extra Vehicles ANOVA - Analysis of Variance RQ - Research Question iii List of Figures 1.1 Example of the occurrence of disruption at various distribution states [5] ................................ 3 2.1 The basic structure of classic VRP [10] ............... 7 2.2 Types of VRP variants ........................ 9 2.3 Solution algorithms and methods for solving routing problems . 10 2.4 Meta-Heuristic Algorithms ..................... 10 2.5 Tabu search example of neighbor selection [18] ........... 12 2.6 Examples of causing disruption problems .............. 18 4.1 Example format of benchmark instance by Fisher [38] ....... 25 4.2 An overview of the experiment procedure .............. 26 5.1 Annual cost solution of algorithms for the dataset "A-n37-k6". 30 5.2 Shortest routes of each vehicle traveled - ACO solution. ...... 30 5.3 Shortest routes of each vehicle traveled - GA solution. ....... 31 5.4 Shortest routes of each vehicle traveled - TS solution. ....... 31 6.1 Bar representation of results with Augerat et al. Set A ...... 36 6.2 Bar representation of results with Augerat et al. Set B ...... 36 6.3 Bar representation of results with Christofides et al. Set E .... 37 6.4 Bar representation of results with Feng et al. Set A ........ 37 6.5 Bar representation of results with Feng et al. Set B ........ 38 6.6 Bar representation of results with Korayem et al. Set A ...... 38 6.7 Bar representation of results with Korayem et al. Set B ...... 39 6.8 Bar representation of results with Ganesh et al. Set A ....... 39 iv List of Tables 2.1 Advantages and disadvantages of selected algorithms [25]. ..... 17 4.1 Hardware Environment ........................ 24 5.1 (i) Results of a Literature Review. .................. 28 5.2 (ii) Results of a Literature Review. ................. 29 5.3 Comparison of results with solutions by Augerat et al. - set A. 32 5.4 Comparison of results with solutions by Augerat et al. - set B. 32 5.5 Comparison of results with solutions by Christofides et al. - set E. 33 5.6 Comparison of results with solutions by Feng et al. - Set A & B. 33 5.7 Comparison of results with solutions by Korayem et al. - set A & B. 34 5.8 Comparison of results with solutions by Ganesh et al. - set A. 34 6.1 Statistical Analysis of results with Augerat et al. Set A. ..... 41 6.2 Statistical Analysis of results with Augerat et al. Set B. ...... 42 6.3 Statistical Analysis of results with Christofides et al. Set E. 43 6.4 Statistical Analysis of results with Feng et al. Set A and B. 44 6.5 Statistical Analysis of results with Korayem et al. Set A and B. 45 6.6 Statistical Analysis of results with Ganesh et al. Set A. ...... 46 v Contents Abstract i Acknowledgments ii List of Abbreviations iii 1 Introduction 1 1.1 Context and Motivation ....................... 1 1.2 Disruption in Vehicle Routing Problem (DVRP) .......... 2 1.3 Problem Statement .......................... 3 1.4 Aim and Objectives .......................... 4 1.5 Research Questions .......................... 4 1.6 Thesis Outline ............................. 5 2 Background 6 2.1 Operational Research ......................... 6 2.2 Optimization ............................. 6 2.3 Vehicle Routing Problem ....................... 7 2.3.1 What is VRP? ........................ 7 2.3.2 Types of VRP Variants .................... 8 2.3.3 Solution Algorithms for Solving VRP ............ 9 2.4 Meta-Heuristics ............................ 10 2.4.1 Tabu Search (TS) ....................... 11 2.4.2 Ant Colony Optimization (ACO) .............. 15 2.4.3 Genetic Algorithm (GA) ................... 15 2.4.4 Advantages and Disadvantages ............... 16 2.5 Disruption in VRP - Causes and Factors .............. 17 3 Related Work 20 4 Research Methodology 22 4.1 Literature Review ........................... 22 4.1.1 Search process ......................... 22 4.1.2 Inclusion and Exclusion Criteria .............. 23 vi 4.2 Experiment .............................. 23 4.2.1 Independent and Dependent Variables ........... 23 4.2.2 Initial setup .......................... 23 4.2.3 Experimental Process ..................... 25 4.2.4 Experimental Design ..................... 26 5 Results 27 5.1 Literature Review Results ...................... 27 5.2 Experiment Results .......................... 27 5.2.1 Comparison of all obtained results .............. 31 6 Analysis and Discussion 35 6.1 Analysis of Literature Review .................... 35 6.2 Analysis and Discussion of Experimental results .......... 35 6.3 Statistical Analysis .......................... 39 6.4 Answers to Research Questions ................... 47 6.5 Validity Threats ............................ 48 6.5.1 Internal Validity ....................... 48 6.5.2 External Validity ....................... 48 6.5.3 Conclusion Validity ...................... 49 7 Conclusions and Future Work 50 vii Chapter 1 Introduction 1.1 Context and Motivation The future of the world is dedicated to automobiles and artificial intelligence. So, we can use them together to deal with minimizing the usage of scarce and valu- able resources in the transport or logistics industry. The main goal of logistics is optimization. Optimization is performed by solving complex computing prob- lems, routing, and many other instances related to problem-solving [1]. There are many ways to solve