Development of algorithms for solving routing problems in the people and parcel transportation = nghiên cứu và phát triển các thuật toán giải quyết các bài toán tối ưu trong giao thông vận tải người và hàng hóa

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: ˆ This work was done as a part of requirements for the degree of PhD at Hanoi University of Science and Technology ˆ This thesis has not previously been submitted for any degree ˆ 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, May, 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 scientic 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 signicant 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, May, 2023 Ph.D Student NGUYEN VAN SON 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 11 1.2.1 Capacitated Vehicle Routing Problem 11 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 Rich Vehicle Routing Problem 16 1.2.5 Static Routing Scenario 17 1.2.6 Dynamic Routing Scenario 18 Solution Methodologies for VRP problems 18 1.3.1 Exact Methods 19 1.3.2 Approximate Methods 20 1.3.2.1 Classic Heuristics 21 1.3.2.2 Metaheuristics 23 1.3 MODELLING AND SOLVING A NEW VARIANT OF STATIC VEHICLE ROUTING PROBLEM 28 2.1 Introduction 28 2.2 Problem description and formulation 30 2.2.1 Problem description 30 2.2.2 Notations and denitions 32 2.2.3 Model formulation 34 The solution methods 37 2.3.1 Notations for heuristic algorithms and solution evaluation 37 2.3.2 Analysis of the challenges of the new capacity constraints in the 2.3 MTDLC-VR problem iii 38 2.3.2.1 A review of construction heuristics 2.3.2.2 The challenges of the capacity constraints on construction heuristics 40 Splitting procedure 41 2.3.3 Adapted construction algorithms with splitting procedure 43 2.3.4 An adapted ALNS with splitting procedure 47 2.3.4.1 Outline of A-ALNS algorithm 48 2.3.4.2 Choosing the operators 50 2.3.4.3 Removal operators 50 2.3.4.4 Insertion operators 52 Experiments 53 2.4.1 Instances and setting 53 2.4.2 Experiment 1: Mathematical formulation validation 56 2.4.3 Experiment 2: Comparison the eciency between construction 2.3.2.3 2.4 2.4.4 2.4.5 2.5 38 heuristics 61 Experiment 3: The eciency of the A-ALNS algorithm 63 2.4.4.1 Parameter tuning 63 2.4.4.2 The eciency of removal and insertion operators 64 2.4.4.3 Robustness of the A-ALNS strategy 65 Experiment 4: Sensitivity analysis for the lower-bound capacity constraint 68 Chapter Summary 69 MODELLING AND SOLVING A NEW VARIANT OF DYNAMIC VEHICLE ROUTING PROBLEM 71 3.1 Introduction 71 3.2 Taxi-Share Routing Model 73 3.2.1 Problem Description 73 3.2.2 Problem Formulation 74 3.3 Online Taxi-Share Routing Problem Based on Predicted Information 3.3.1 3.3.2 76 Taxi Demand Prediction 76 3.3.1.1 Learning method with equal length subintervals 77 3.3.1.2 Learning framework with an adaptive binning method 78 Online Routing Algorithm 79 3.3.2.1 Route representation 81 3.3.2.2 Possible Positions for Insertion 81 3.3.2.3 Route Re-optimization 82 3.3.2.4 Route Establishment 82 3.3.2.5 Request Insertion 82 iv 3.3.2.6 Improvement Operator 83 3.3.2.7 Prediction-Based Idle Taxi Direction 84 Experiments 84 3.3.3.1 Data Description 84 3.3.3.2 Simulation design 85 3.3.3.3 Experimental results 85 Chapter Summary 91 3.3.3 3.4 CONCLUSIONS 92 PUBLICATIONS 94 Bibliography 95 v ABBREVIATIONS No Abbreviation Meaning ACS ALNS BnB BnC BnP CDF CF-RS CP CVRP 10 DARP 11 DP 12 DVRP 13 EDF 14 ERM 15 GA 16 GRASP 17 ICTP 18 KS 19 LP 20 LS 21 MDVRP 22 MMCVRP 23 MMVRP 24 MILP 25 MTVRP 26 NHPP 27 NP 28 OP 29 PDVRPTW Ant Colony System Adaptive Large Neighborhood Search Branch-and-Bound Branch-and-Cut Branch-and-Price Cumulative Distribution Function Cluster-First Route-Second Constraint Programming Capacitated Vehicle Routing Problem Dial-A-Ride Problem Dynamic Programming Dynamic Vehicle Routing Problem Empirical Distribution Function Empirical Risk Minimization Genetic Algorithm Greedy Randomised Adaptive Search Procedure Inland Container Transportation Problem Kolmogorov-Smirnov Linear Programming Local Search Multi-Depot Vehicle Routing Problem Min-Max Capacitate Vehicle Routing Problem MinMax Vehicle Routing Problem Mixed-Integer Linear Programming Multi-Trip Vehicle Routing Problem NonHomogeneous Poisson Process Non-deterministic Polynomial-time Optimization Problem Pickup-and-Delivery Vehicle Routing Problem with Time Window vi 30 PSO 31 RF-CS 32 RVRP 33 SA 34 SARP 35 SRM 36 SW 37 TSP 38 VRP 39 VRPB 40 VRPTW Particle Swarm Optimisation Route-First Cluster-Second Rich Vehicle Routing Problem Saving Algorithm Shared-A-Ride Problem Structural Risk Minimization Sweep Algorithm Travelling Salesman Problem Vehicle Routing Problem Vehicle Routing Problem with Backhauls Vehicle Routing Problem with Time Windows vii LIST OF TABLES A summary of the related papers 2.1 Sets and parameters 33 2.2 Modeling variables 34 2.3 Parameters of instance E21 − − − − − 55 2.4 Travel time matrix of instance E21 − − − − − 55 2.5 Parameters of instances RG − − − − − and RG − − − − − 56 2.6 Travel time matrix of instances RG−1−2−2−2−6 and RG−2−2−2−2−6 56 2.7 Comparison solutions of the MILP model with the found optimal solutions 58 2.8 Comparison between MILP model and the A-ALNS algorithm 58 2.9 Comparison between MILP model and construction algorithms 60 2.10 The eciency comparison between construction algorithms 62 2.11 Results of parameter tuning 64 2.12 The comparison of solution approaches 67 3.1 Parameter Setting 86 3.2 Taxi fare rate for calculating the prot introduced in [14] 86 3.3 The number of taxi requests need to be served in two scenarios 86 3.4 The routing results of four algorithms in the rst scenario 87 3.5 The routing results of four algorithms in the second scenario 88 3.6 The eciency of the algorithm based on the predicted information 89 3.7 The prot of scheduling algorithm using our proposed learning method 91 viii Table 3.7: The prot of scheduling algorithm using our proposed learning method OTSF-DP (Scenario 1) Ins D1 D2 D3 D4 D5 D6 D7 D8 D9 OTSF-DP (Scenario 2) p1 p2 ρ(%) p1 p2 ρ(%) 74.97 67.09 78.61 78.89 85 94.36 92.47 63.5 49.31 79.12 70.37 85.02 81.74 90.21 101.68 98.62 66.2 53.85 5.54 4.89 8.15 3.61 6.13 7.76 6.65 4.25 9.21 27.92 30.89 32.67 29.62 29.8 29.24 29.62 28.56 10.16 29.14 32.11 33.98 31.03 31.61 30.15 31.17 29.72 10.85 4.37 3.95 4.1 4.76 6.07 3.11 5.23 4.06 6.79 3.4 Chapter Summary In this work, we solved the problem of routing a shared ride of people and parcels in an online manner using the taxi network First, we formulate the people-parcel taxi sharing model in [9] using a constraint-based programming model with the ability to predict requests based on historical data To achieve this aim, we derive a learning model and a data-driven method for predicting the areas of the road network at which requests are likely to be sent for some time period of a day by utilizing spatio-temporal Poisson process Next, we proposed the algorithm OTSF-DP for the online routing problem Usually, conventional navigation systems would provide the driver with the shortest path to the nearest parking location upon the completion of all the requests In contrast, we suggest the driver follow the route with a high probability of receiving a new request on the route to the specied parking location by using prediction information However, if there is no such an opportunity, a new parking location is recommended to the driver with the hope that during staying there the taxi can receive a new request soon Therefore, the taxi's idle time is minimized Finally, we evaluated the proposed algorithms and the previous ones on the adapted real datasets Experimental results showed that our algorithms are better than the existing methods in terms of total benets prot The idle travel distance is reduced as well Research results in this Chapter have been published at [2, 3, 4, 6] in PUBLICATIONS list 91 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 eld of VRP problems Concretely, three main contributions have been shown in three chapters and summarized as follows: The rst 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 deciencies 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 92 Future works The thesis has obtained some signicant results However, there are rooms for improvement In our future works, we would like to explore the following research directions listed as follows: For the proposed MTDLC-VR problem, we will investigate an online scenario in which requests are not known beforehand and are revealed online during the execution of the schedule Moreover, a challenging area for future research is to extend the problem more exibly and realistically with a stochastic environment (e.g., stochastic demands and stochastic travel time) In practice, real-time requests are dynamic and each vehicle's status and route must be continuously monitored by the system A dynamic instance requires solving a series of static instances composed of currently pending requests Hence, an algorithm for solving a dynamic instance must make good solutions in a short execution time For the proposed problem of the dynamic share-a-ride taxi VRP, we aim to make this problem more exible and realistic We will investigate other algorithms as well as dierent approaches for reducing both the number of rejected requests and total cost Furthermore, we would like to focus on other prediction methods and dierent approaches for exploiting prediction information in VRP problems We will also analyze more deeply the successful and failure cases of prediction 93 PUBLICATIONS Van Son NGUYEN, Quang Dung PHAM, Quoc Trung BUI, Thanh Hoang Modelling and Solving a Real-world Multi-trip Multi-distribution center Vehicle Routing Problem with Lower-bound Capacity, Computers and InNGUYEN (2022), dustrial 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 Novel Online Routing Algorithms for Smart People-Parcel Taxi Sharing Services, ETRI journal, vol 44(2), NGUYEN, Behrouz BABAKI, Dries ANTON (2022), pp 220-231 Doi: 10.4218/etrij.2021-0406 (IF: 1.622) Van Son NGUYEN, Quang Dung PHAM, Quoc Trung BUI, Thanh Hoang NGUYEN (2017), Search Solving Min-Max Capacitated Vehicle Routing Problem by Local Journal of Computer Science and Cybernetics, vol 33(1), pp 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