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Open Final Thesis Chaitanya.Pdf The Pennsylvania State University The Graduate School College of Engineering AN AGENT BASED AND ANT COLONY METAHEURISTIC APPROACH TO THE LAST MILE LOGISTICS PROBLEM A Thesis in Industrial Engineering by Chaitanya Kaul © 2018 Chaitanya Kaul Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science August 2018 ii The thesis of Chaitanya Kaul was reviewed and approved * by the following: Terry Harrison Professor of Supply Chain and Information Systems Daniel Finke Assistant Research Professor Thesis Adviser Applied Research Laboratory, Pennsylvania State University Robert Voigt Professor of Industrial and Manufacturing Engineering Graduate Program Coordinator, Department of Industrial and Manufacturing Engineering *Signatures are on file in the Graduate School. iii ABSTRACT The last mile logistics refers to the last leg of delivery in a supply-chain transportation network. More transporters and delivery runs are required to fulfill this demand and hence make the last- mile logistic a highly inefficient and time-consuming segment of a supply chain. In this thesis, the formulation of the last mile logistics problem has been presented as an optimization problem and is solved by a modified meta-heuristic algorithm and an agent-based simulation technique. For the last-mile logistics problem, the widely studied vehicle routing problem with time windows transportation model was considered, as it abstracts the salient features of numerous logistics and transportation related real-world problems. The ant colony metaheuristic was then modified to find global minimum in case of the last mile problem. In this work, the last mile logistics problem was then also solved as an agent-based simulation model. Due to its efficient behavioral and communicative patterns, the agent-based systems provide a powerful alternative to traditional optimization techniques. In this work, we present a formulation of the last mile logistics problem as an ant colony metaheuristic and as a multi-agent optimization model. The modified ant colony algorithm was experimented on test problems and data, and successful results were obtained. A detailed experimental assessment of the agent based simulation model and our modified ant colony metaheuristic is presented, including the comparison to the traditional centralized algorithms. A detailed analysis of the solution approach is performed as well, generating future research opportunities. The outcomes of this thesis demonstrate that the methodologies return the best- known solutions accomplished by the state-of-the-art algorithms in most of our experimental cases, representing a significant improvement over the previous comparable agent based or metaheuristic studies. The core commitment of this thesis is the more profound and novel understanding of the implications of embracing agent-based and a heuristic approach to solve the last mile network problems and complex transportation-optimization problems. iv TABLE OF CONTENTS List of Figures .............................................................................................................. vi List of Tables ............................................................................................................... vii Acknowledgements ...................................................................................................... viii Chapter 1 Introduction ................................................................................................. 1 1.1 Thesis outline .................................................................................................. 2 Chapter 2 Problem definition ....................................................................................... 5 2.1 Conventional problems in last mile network .................................................. 5 Chapter 3 Literature review ......................................................................................... 11 3.1 Related work and contributions in last mile logistics ..................................... 11 3.2 Impact of inefficient routing on last mile logistics ......................................... 13 3.3 Popular optimization techniques used for last mile logistics .......................... 15 3.3.1 Solution techniques ................................................................................. 18 3.3.1.1 Exact algorithms ........................................................................... 18 3.3.1.2 Metaheuristics ............................................................................... 21 3.3.1.3 Evolutionary algorithms ............................................................... 22 3.3.1.4 Agent based approaches to optimization ...................................... 24 Chapter 4 Research methodology ................................................................................ 27 4.1 Overview ......................................................................................................... 27 4.2 Problem setup ................................................................................................. 28 4.3 Model formulation .......................................................................................... 29 4.3.1 Last mile logistics routing problem model ............................................. 29 4.3.2 Routing cost model ................................................................................. 32 4.4 Algorithms for problem .................................................................................. 33 4.4.1 Evolutionary algorithm: Ant colony ....................................................... 33 4.4.2 Optimization approach: Proposed modified ant colony metaheuristic .. 37 4.4.3 Optimization approach: Branch and bound algorithm ........................... 39 4.5 Optimization approach: Agent based simulation model ................................ 40 4.6 Benchmarking ................................................................................................ 41 Chapter 5 Implementation............................................................................................ 44 5.1 Proposed modified ant colony metaheuristic implementation ........................ 44 5.1.1 Parameter tuning ..................................................................................... 45 5.2 Proposed agent based simulation-optimization model implementation ......... 49 v 5.2.1 Development of the communicating agent network ............................... 50 5.2.2 Delivery process...................................................................................... 51 5.2.3 Model framework.................................................................................... 51 5.2.4 Agent development ................................................................................. 51 Chapter 6 Experimentation & analysis ....................................................................... 56 6.1 Experimentation .............................................................................................. 56 6.2 Conclusions from experiments ....................................................................... 65 Chapter 7 Conclusions ................................................................................................ 67 Bibliography ................................................................................................................ 70 Appendix: Ant colony pseudo code ........................................................................... 78 vi LIST OF FIGURES Figure 1: The ant-colony optimization solution method ........................................................................ 34 Figure 2: The ant colony optimization of the travelling salesperson problem ....................................... 35 Figure 3: The ant-colony optimization algorithm flowchart .................................................................. 36 Figure 4: The modified optimization algorithm flowchart .................................................................... 39 Figure 5: Relative importance of the alpha tuning for ACO .................................................................. 47 Figure 6: Evaporation rate tuning for ACO ........................................................................................... 48 Figure 7: Number of ants tuning for ACO ............................................................................................. 48 Figure 8: Relative importance of the beta tuning for ACO .................................................................... 49 Figure 9: Process flow of depot agent ................................................................................................... 52 Figure 10: Process flow of order agent .................................................................................................. 52 Figure 11: Process flow of vehicle agent ............................................................................................... 53 Figure 12: Process flow of customer agent ............................................................................................ 54 Figure 13: Solution quality analysis ...................................................................................................... 57 Figure 14: Computational time analysis ................................................................................................ 58 Figure 15: Optimal route selected through ‘gmplots’ ...........................................................................
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