Luận Án development Of Algorithms For Solving Routing Problems In The People And Parcel Transportation.pdf

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN VAN SON DEVELOPMENT OF ALGORITHMS FOR SOLVING ROUTING PROBLEMS IN THE PEOPLE AND PARCEL TRANSPORTATION DOCTORAL DISSERTATION OF COMPUTER SCIENCE Hanoi−2023 MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN VAN SON DEVELOPMENT OF ALGORITHMS FOR SOLVING ROUTING PROBLEMS IN THE PEOPLE AND PARCEL TRANSPORTATION Major: Computer Science Code: 9480101 DOCTORAL DISSERTATION OF COMPUTER SCIENCE SUPERVISORS: Ph.D Pham Quang Dung Assoc Prof Nguyen Xuan Hoai Hanoi−2023 DECLARATION OF AUTHORSHIP I declare that my thesis titled "Development of algorithms for solving routing problems in the people and parcel transportation" has been entirely composed by myself, supervised by my cosupervisors, Ph.D Pham Quang Dung and Assoc Prof Nguyen Xuan Hoai I assure some statements as follows: • This work was done as a part of requirements for the degree of PhD at Hanoi University of Science and Technology • This thesis has not previously been submitted for any degree • The results in my thesis is my own independent work, except where works in the collaboration have been included Other appropriate acknowledgements are given within this thesis by explicit references Hanoi, February, 2023 Ph.D Student NGUYEN VAN SON SUPERVISORS Ph.D Pham Quang Dung Assoc Prof Nguyen Xuan Hoai i ACKNOWLEDGEMENT My thesis has been realized during my doctoral course at the School of Information Communication and Technology (SoICT), Hanoi University of Science and Technology (HUST) HUST is a really special place where I have accumulated immense knowledge in my PhD process A PhD process is not a one-man process Therefore, I am heartily thankful to my supervisors, Ph.D Pham Quang Dung and Assoc Prof Nguyen Xuan Hoai, whose encouragement, guidance and support from start to end enabled me to develop my research skills and understanding of the subject I have learned the countless amount of things from them This thesis would not have been possible without their precious support I would like to thank Prof Luc De Raedt and all members of Faculty of Computer Science, KU Leuven, Belgium for supporting me a lot in the research process A special thanks goes to Assoc Prof Mahito Sugiyama at National Institute of Informatics, Japan for valuable guidance helps me obtain many scientific experiences during the internship periods of the PhD Many thanks go also to Ph.D Anton Dries, Ph.D Behrouz Babaki, Ph.D Bui Quoc Trung, Msc Nguyen Thanh Hoang, Msc Phan Anh Tu for a positive research-partnership during many months made this research significant as well as realistic I would like to thank Executive Board and all members of Computer Science Department, SoICT as well as HUST for the frequent support in my PhD course I thank my colleagues at Academy of Cryptography Techniques for their help Last but not the least, I would like to thank my family: my parents, my wife and my friends, who support me spiritually throughout my life They were always there cheering me up and stood by me through the good and bad times Hanoi, February, 2023 Ph.D Student ii CONTENTS CONTENTS vi SYMBOLS vi LIST OF TABLES viii LIST OF FIGURES ix INTRODUCTION 1 BACKGROUND 10 1.1 Optimization Problem 10 1.2 Vehicle Routing Problem and Extensions 1.2.1 Capacitated Vehicle Routing Problem 11 11 1.3 1.2.2 Pickup-and-Delivery Vehicle Routing Problem with Time Windows 12 1.2.3 People and Parcel Sharing Taxi Routing Problem 14 1.2.4 1.2.5 Rich Vehicle Routing Problem Static Routing Scenario 16 17 1.2.6 Dynamic Routing Scenario 18 Solution Methodologies for VRP problems 18 1.3.1 1.3.2 Exact Methods Incomplete Methods 19 21 1.3.2.1 Classic Heuristics 21 1.3.2.2 Metaheuristics 23 MODELLING AND SOLVING A NEW VARIANT OF STATIC VEHICLE ROUTING PROBLEM 2.1 Introduction 27 27 2.2 Problem description and formulation 29 2.2.1 Problem description 29 2.2.2 2.2.3 Notations and definitions Model formulation 31 33 The solution methods 36 2.3.1 Notations for heuristic algorithms and solution evaluation 36 2.3.2 Analysis of the challenges of the new capacity constraints in the MTDLC-VR problem 37 2.3 iii 2.4 2.3.2.1 A review of construction heuristics 2.3.2.2 The challenges of the capacity constraints on construc- 2.3.2.3 tion heuristics Splitting procedure 39 40 2.3.3 Adapted construction algorithms with splitting procedure 43 2.3.4 An adapted ALNS with splitting procedure 47 2.3.4.1 2.3.4.2 Outline of A-ALNS algorithm Choosing the operators 48 49 2.3.4.3 Removal operators 50 2.3.4.4 Insertion operators 51 Experiments 2.4.1 Instances and setting 53 53 2.4.2 Experiment 1: Mathematical formulation validation 56 2.4.3 Experiment 2: Comparison the efficiency between construction 2.4.4 heuristics Experiment 3: The efficiency of the A-ALNS algorithm 59 62 2.4.4.1 Parameter tuning 62 2.4.4.2 The efficiency of removal and insertion operators 63 2.4.4.3 Robustness of the A-ALNS strategy Experiment 4: Sensitivity analysis for the lower-bound capacity 64 constraint 67 Chapter Summary 68 2.4.5 2.5 37 MODELLING AND SOLVING A NEW VARIANT OF DYNAMIC VEHICLE ROUTING PROBLEM 3.1 Introduction 70 70 3.2 Taxi-Share Routing Model 72 3.2.1 Problem Description 72 3.2.2 Problem Formulation Online Taxi-Share Routing Problem Based on Predicted Information 72 75 3.3.1 Taxi Demand Prediction 75 3.3.1.1 Learning method with equal length subintervals 75 3.3.1.2 Learning framework with an adaptive binning method Online Routing Algorithm 76 81 3.3.2.1 Route representation 81 3.3.2.2 Possible Positions for Insertion 82 3.3.2.3 3.3.2.4 Route Re-optimization Route Establishment 83 83 3.3.2.5 Request Insertion 83 3.3 3.3.2 iv 3.3.2.6 Improvement Operator 84 3.3.2.7 Prediction-Based Idle Taxi Direction 84 Experiments 3.3.3.1 Data Description 85 85 3.3.3.2 Simulation design 86 3.3.3.3 Experimental results 87 Chapter Summary 92 3.3.3 3.4 CONCLUSIONS 93 PUBLICATIONS 95 Bibliography 97 v ABBREVIATIONS No Abbreviation Meaning ACS Ant Colony System ALNS Adaptive Large Neighborhood Search BnB Branch-and-Bound BnC Branch-and-Cut BnP Branch-and-Price CDF Cumulative Distribution Function CF-RS Cluster-First Route-Second CP Constraint Programming CVRP Capacitated Vehicle Routing Problem 10 DARP Dial-A-Ride Problem 11 DP Dynamic Programming 12 DVRP Dynamic Vehicle Routing Problem 13 EDF Empirical Distribution Function 14 ERM Empirical Risk Minimization 15 GA Genetic Algorithm 16 GRASP Greedy Randomised Adaptive Search Procedure 17 ICTP Inland Container Transportation Problem 18 KS Kolmogorov-Smirnov 19 LP Linear Programming 20 LS Local Search 21 MDVRP Multi-Depot Vehicle Routing Problem 22 MMCVRP Min-Max Capacitate Vehicle Routing Problem 23 MMVRP MinMax Vehicle Routing Problem 24 MIP Mixed-Integer Programming 25 MTVRP Multi-Trip Vehicle Routing Problem 26 NHPP NonHomogeneous Poisson Process 27 NP Non-deterministic Polynomial-time 28 OP Optimization Problem 29 PDVRPTW Pickup-and-Delivery Vehicle Routing Problem with Time Window vi 30 PSO Particle Swarm Optimisation 31 RF-CS Route-First Cluster-Second 32 RVRP Rich Vehicle Routing Problem 33 SA Saving Algorithm 34 SARP Shared-A-Ride Problem 35 SRM Structural Risk Minimization 36 SW Sweep Algorithm 37 TSP Travelling Salesman Problem 38 VRP Vehicle Routing Problem 39 VRPB Vehicle Routing Problem with Backhauls 40 VRPTW Vehicle Routing Problem with Time Windows vii LIST OF TABLES A summary of the related papers 2.1 Sets and parameters 32 2.2 Modeling variables 33 2.3 Parameters of instance E21 − − − − − 54 2.4 2.5 Travel time matrix of instance E21 − − − − − 55 Parameters of instances RG − − − − − and RG − − − − − 55 2.6 Travel time matrix of instances RG−1−2−2−2−6 and RG−2−2−2−2−6 56 2.7 The detail of the found optimal solutions 57 2.8 2.9 Comparison between MILP model and the A-ALNS algorithm Comparison between MIP model and construction algorithms 58 59 2.10 The efficiency comparison between construction algorithms 61 2.11 Results of parameter tuning 63 2.12 The results of the A-ALNS algorithm 66 3.1 Parameter Setting 86 3.2 Taxi fare rate for calculating the profit introduced in [14] 87 3.3 3.4 The number of taxi requests need to be served in two scenarios The routing results of four algorithms in the first scenario 87 88 3.5 The routing results of four algorithms in the second scenario 89 3.6 The efficiency of the algorithm based on the predicted information 90 3.7 The profit of scheduling algorithm using our proposed learning method 92 viii CONCLUSION AND FUTURE WORKS Conclusion Transport of people and freight plays a particularly important role in the economy of each country Many models of people and good transportation have been built in practice Solving these problems is very hard and still an active research topic that attracts the attention of many computer scientists due to their impact on society and the economy This thesis has presented the contributions in the field of VRP problems Concretely, three main contributions have been shown in three chapters and summarized as follows: The first is designing a new model of the freight transportation, representing the static VRP problem class It is a realistic dairy product transportation in which vehicles must deliver dairy product units from multiple distribution centers to customers and operate multiple trips and the total weight of products transported in each trip must be within a given range depending on the capacity of the operating vehicle We proposed a mixed-integer linear programming model and an adapted ALNS algorithm for solving the considered problem in large instances Experimental results state that the proposed algorithm can generate suitable results in a short computational time and the performance of the proposed algorithm should be acceptable in the application as one of the most appropriate algorithms for solving a large number of requests The other proposed model represents a people transport model in which people and parcels can share a ride and the routing system needs to recommend the best route to the driver of a taxi without load so that the chance of receiving a new transportation demand is high when the taxi is still available Our model alleviates the deficiencies of models in [14, 9] by adding some real-world factors We developed new algorithms for solving the considered model in the dynamic scenario Especially, a new anticipatory algorithm for routing taxis exploiting the predicted future requests in the dynamic scenario is proposed The algorithms are experimented on real data sets and shown to be competitive with the one in [14] Finally, this thesis proposed a statistical learning method for learning the NHPP process to predict VRP requests that help minimize the vehicle’s idle distance The experimental results show that the total idle travel distance of our algorithms is less than that of the existing share-a-ride method about 9.64% to 12.76% each day by applying the learning method This study linked transportation problems with machine learning that improves the overall travel distance 93 Future works The thesis has obtained some significant results However, there are rooms for improvement In our future works, we would like to explore the following research directions listed as follows: We focus on other metaheuristics algorithms for comparison with our proposed algorithms to improve current solution quality We will make these problems more flexible/realistic with a stochastic environment (e.g., consider the uncertainty in travel times, fluctuation of speed, etc.) We also explore some statistical techniques for improving the learning quality of request information Finally, we will analyze the successive and failure cases of prediction more deeply and different approaches for exploiting prediction information in VRP problems 94 PUBLICATIONS Publications related to the thesis Van Son NGUYEN, Quang Dung PHAM, Quoc Trung BUI, Thanh Hoang NGUYEN (2022), Modelling and Solving a Real-world Multi-trip Multi-distribution center Vehicle Routing Problem with Lower-bound Capacity, Computers and Industrial Engineering, vol.172 (A), 108597 Doi: 10.1016/j.cie.2022.108597 (IF: 7.18) Van Son NGUYEN, Babaki Behrouz, Dries Anton, Quang Dung PHAM, Xuan Hoai NGUYEN(2017), Prediction-based optimization for online People and Parcels share a ride taxis, 9th International Conference on Knowledge and Systems Engineering (KSE), pp 42-47 Doi: 10.1109/KSE.2017.8119432 (Best paper award) Van Son NGUYEN, Quang Dung Pham, Van Hieu Nguyen Exploiting Demand Prediction to Reduce Idling Travel Distance for Online Taxi Scheduling Problem In Proceedings of the 4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences MCO 2021, Hanoi, Vietnam, 13-14 December 2021 Volume 363 of Lecture Notes in Networks and Systems, pages 51-62, Springer, 2021 (Scopus Indexed) Van Son NGUYEN, Thi Hong Nhan VU, Quang Dung PHAM, Xuan Hoai NGUYEN, Behrouz BABAKI, Dries ANTON (2022), Novel Online Routing Algorithms for Smart People-Parcel Taxi Sharing Services, ETRI journal, vol 44(2), pp 220-231 Doi: 10.4218/etrij.2021-0406 (IF: 1.622) Other publications Van Son NGUYEN, Quang Dung PHAM, Quoc Trung BUI, Thanh Hoang NGUYEN (2017), Solving Min-Max Capacitated Vehicle Routing Problem by Local Search Journal of Computer Science and Cybernetics, vol 33(1), pp 3-18 Doi:10.15625/1813-9663/33/1/8846 Van Son NGUYEN, Quang Dung PHAM, Anh Tu PHAN (2019), An Adaptive Large Neighborhood Search Solving Large People and Parcel Share-a-Ride Problem, 6th NAFOSTED Conference on Information and Computer Science (NICS), pp 303-308 Doi: 10.1109/NICS48868.2019.9023893 95 Van Son NGUYEN, Quang Dung PHAM (2019), A New Variant of Truck Scheduling for Transporting Container Problem Proceedings of the Tenth International Symposium on Information and Communication Technology - SoICT 2019, pp 139-146 Doi:10.1145/3368926.3369721 Van Son NGUYEN, Quang 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