Thesis (M.S.)--Kutztown University of Pennsylvania, 2001. / Source: Masters Abstracts International, Volume: 45-06, page: 3171. Typescript. Abstract precedes thesis as preliminary leaves[1-3]. Includes bibliographical references (leaves 91-92).

Thesis (Ph. D.)--Georgia Institute of Technology, 2005. / Includes bibliographical references (p. 108-110). Also available online via the Georgia Institute of Technology, website (http://etd.gatech.edu/).

Chen, Jian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 81-85). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Literature Review --- p.4 / Chapter 1.2.1 --- Vehicle Routing Problem with Stochastic Demands --- p.5 / Chapter 1.2.2 --- Vehicle Routing Problem with Stochastic Travel Times --- p.8 / Chapter 1.3 --- The Vehicle Routing Problem with Time Windows and Stochastic Travel Times --- p.10 / Chapter 2 --- Notations and Formulations --- p.12 / Chapter 2.1 --- Problem Definitions --- p.12 / Chapter 2.2 --- A Two-Index Stochastic Programming Model --- p.14 / Chapter 2.3 --- The Second Stage Problem --- p.17 / Chapter 3 --- The Scheduling Problem --- p.20 / Chapter 3.1 --- The Overtime Cost Problem --- p.22 / Chapter 3.2 --- The Waiting and Late Cost Problem --- p.27 / Chapter 3.3 --- The Algorithm --- p.37 / Chapter 4 --- The Integer L-Shaped Method --- p.40 / Chapter 4.1 --- Linearization of the Objective Function --- p.41 / Chapter 4.2 --- Handling the Constraints --- p.42 / Chapter 4.3 --- Branching --- p.44 / Chapter 4.4 --- The Algorithm --- p.44 / Chapter 5 --- Feasibility Cuts --- p.47 / Chapter 5.1 --- Connected Component Methods --- p.48 / Chapter 5.2 --- Shrinking Method --- p.49 / Chapter 6 --- Optimality Cuts --- p.52 / Chapter 6.1 --- Lower Bound I for the EOT Cost --- p.53 / Chapter 6.2 --- Lower Bounds II and III for the EOT Cost --- p.56 / Chapter 6.3 --- Lower Bound IV for the EWL Cost --- p.57 / Chapter 6.4 --- Lower Bound V for Partial Routes --- p.61 / Chapter 6.5 --- Adding Optimality Cuts --- p.66 / Chapter 7 --- Numerical Experiments --- p.70 / Chapter 7.1 --- Effectiveness in Separating the Rounded Capacity Inequalities --- p.71 / Chapter 7.2 --- Effectiveness of the Lower Bounds --- p.72 / Chapter 7.3 --- Performance of the L-shaped Method --- p.74 / Chapter 8 --- Conclusion and Future Research --- p.79 / Bibliography --- p.81 / Chapter A --- Generation of Test Instances --- p.86

Thesis (M.S.)--University of North Carolina at Wilmington, 2005. / Includes bibliographical references (leaves: [26]-[27])

This dissertation is submitted in fulfillment of the requirements for the degree of Master of Science, University of the Witwatersrand, Johannesburg, August 2018 / Abstract. The vehicle routing problem is a common combinatorial optimization, which is modelled to determine the best set routes to deploy a fleet of vehicles to customers, in order to deliver or collect goods efficiently. The vehicle routing problem has rich applications in design and management of distribution systems. Many combinatorial optimization algorithms which have been developed, were inspired through the study of vehicle routing problems. Despite the literature on vehicle routing problems, the existing techniques fail to perform well when n (the number of variables defining the problem) is very large, i.e., when n > 50. In this dissertation, we survey exact and inexact methods to solve large problems. Our attention is on the capacitated vehicle routing problem. For exact methods, we investigate only the Cutting Planes method which has recently been used in conjunction with other combinatorial optimization problem algorithms (like the Branch and Bound method) to solve large problems. In this investigation, we study the polyhedral structure of the capacitated vehicle routing problem. We compare two metaheuristics, viz., the Genetic Algorithm and the Ant Colony Optimization. In the genetic algorithm, we study the effect of four different crossover operators. Numerical results are presented and conclusion are drawn, based on our findings. / XL2019

No description available.

In this dissertation, we aim to investigate some delivery planning problems under different aspects of uncertain conditions in order to present some insights on how to develop more efficient and effective logistics solutions in the challenging, dynamic and competitive industry of express delivery nowadays. / Lastly, we introduce a travel time estimation method which can be considered as an upstream extension on the first delivery planning problem or any routing problems that require travel time in a road network as an input parameter. We realize that different trips may traverse on some links that are transited by other trips as well; and therefore linear and quadratic programming models can be developed to infer the travel time of any road segments which are traversed by more than one trip from the consideration of the common road segments of various trips. Bus routes information is proposed as the data source because of their coverage on the road network and their data available. Robustness of the proposed models is evaluated according to the deviation between the resultant inferences and the means of the segment travel times. It is found that the basic models suffer from a fundamental problem of underspecification issue if the number of observed trips (i.e. constraints) is fewer than the number of road segments (i.e. variables) to be estimated. Therefore, two additional types of constraints are introduced to address these issues. / The focus of the second delivery planning problem addressed in this dissertation is switched to the randomness of the demands at the delivery locations. We consider a single-depot problem in which a large volume of packages have to be delivered in a dense and small area with a lot of high-rise buildings within an extremely tight delivery commitment time window. At each vertex, the package volume (i.e. demand) is a highly stochastic variable and its service time is also dependent on the demand. This version of problem is typically faced by express service providers which offer premium express services to customers that are protected by the money back guarantee (MBG) condition. We name this version of problem as a time-constrained vehicle routing problem with stochastic demands and service times (SVRP-D). Since both the demand and service time of a vertex are stochastic, the vehicle capacity and the commitment-time (i.e. deadline) constraints may not be satisfied after the demands are realized, and recourse action is thus necessary to "rescue" the "infeasible" delivery plan through additional effort and cost. Three typical recourse actions are independently considered: (1) restocking; (2) outsourcing; and (3) reassignment. A two-stage stochastic integer programming is proposed such that the first-stage problem is to determine a set of a priori routes which minimize the vehicle travel cost and the cost for any known recourse actions. Dependent on a first-stage solution, the second-stage problem is to decide the optimal recourse plan with least cost according to the pre-determined recourse action. / Two delivery planning problems and one travel time estimation model under various aspects of randomness are addressed in this dissertation. The first delivery planning problem considers an extension on a well-known problem---the vehicle routing problem with time windows (VRPTW), where the travel time required between each delivery location is a stochastic variable, instead of a fixed value. We name this version of the VRPTW as a vehicle routing problem with time windows and stochastic travel times (VRPTWST). A two-stage stochastic integer programming with recourse model is developed and its objective is to minimize the vehicle travel cost together with the expected loss due to the violation of the delivery time windows. An exact branch-and-cut (B&C) algorithm is proposed. This algorithm is effective for small-size problems, e.g. instances with only 8 vertices. For larger-size problems, some modifications on the B&C algorithm are suggested to improve the solution time with only a small deviation from optimality. / Wong Chi Fat. / "May 2007." / Adviser: Janny Leung. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0596. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 256-270). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

This dissertation focuses on improvements in vehicle routing that can be gained by allowing multiple vehicles to service a common load. We explore how costs can be reduced through the elimination of the constraint that a load must be serviced by only one vehicle. Specifically, we look at the problem of routing vehicles to service loads that have distinct origins and destinations, with no constraint on the amount of a load that a vehicle may service. We call this the Pickup and Delivery Problem with Split Loads (PDPSL). We model this problem as a dynamic program and introduce structural results that can help practitioners implement the use of split loads, including the definition of an upper bound on the benefit of split loads. This bound indicates that the routing cost can be reduced by at most one half when split loads are allowed. Furthermore, the most benefit occurs when load sizes are just above one half of vehicle capacity. We develop a heuristic for the solution of large scale problems, and apply this heuristic to randomly generated data sets. Various load sizes are tested, with the experimental results supporting the finding that most benefit with split loads occurs for load sizes just above one half vehicle capacity. Also, the average benefit of split loads is found to range from 6 to 7% for most data sets. The heuristic was also tested on a real world example from the trucking industry. These tests reveal the benefit of both using split loads and allowing fleet sharing. The benefit for split loads is not as significant as with the random data, and the various business rules added for this case are tested to find those that have the most impact. It is found that an additional cost for every stop the vehicle makes strictly limits the potential for benefit from split loads. Finally, we present a simplified version of the PDPSL in which all origins are visited prior to any destination on a route, generalizing structural results from the Split Delivery Vehicle Routing Problem for this problem.

Thesis (Ph.D.)(Industrial Engineering)--University of Pretoria, 2006. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

Cheung, Kwan Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 115-124). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Contributions --- p.7 / Chapter 1.3 --- Findings --- p.8 / Chapter 1.4 --- Structure of the Thesis --- p.10 / Chapter 2 --- Beer Logistics in China --- p.12 / Chapter 2.1 --- The Logistics and Supply Chain in China --- p.12 / Chapter 2.2 --- Beer in China --- p.22 / Chapter 2.2.1 --- The Expanding Market --- p.22 / Chapter 2.2.2 --- The Multi-tiered Supply Chain --- p.22 / Chapter 2.2.3 --- Reverse Logistics --- p.25 / Chapter 2.2.4 --- Manual Demand and Inventory Manage- ment --- p.26 / Chapter 2.2.5 --- Retail Fees and Value Chains --- p.28 / Chapter 2.2.6 --- Packaging --- p.30 / Chapter 2.3 --- The Wholesaler --- p.31 / Chapter 2.3.1 --- High Service Quality under Fierce Com- petition --- p.31 / Chapter 2.3.2 --- Use of Vans --- p.33 / Chapter 2.3.3 --- Delivery Problem of Wholesalers --- p.35 / Chapter 3 --- Literature Review --- p.37 / Chapter 3.1 --- Beer Logistics in China --- p.37 / Chapter 3.2 --- Modelling Delivery Problems --- p.39 / Chapter 3.3 --- Applications of the Vehicle Routing Problem --- p.42 / Chapter 3.4 --- Heuristics and Metaheuristics --- p.43 / Chapter 3.5 --- Round up --- p.45 / Chapter 4 --- Problem Definition --- p.48 / Chapter 4.1 --- Problem Definition --- p.48 / Chapter 4.2 --- Assumptions --- p.53 / Chapter 5 --- Problem Formulation --- p.55 / Chapter 5.1 --- Introduction --- p.55 / Chapter 5.2 --- Notations --- p.58 / Chapter 5.2.1 --- Indices --- p.58 / Chapter 5.2.2 --- Parameters --- p.58 / Chapter 5.2.3 --- Decision Variables --- p.59 / Chapter 5.3 --- Mixed Integer Programming Model --- p.60 / Chapter 5.3.1 --- The Model --- p.60 / Chapter 5.3.2 --- Descriptions --- p.61 / Chapter 5.3.3 --- Complexity and Polynomial Number of Con- straints --- p.66 / Chapter 6 --- Solution Methodology --- p.69 / Chapter 6.1 --- Input Parameters --- p.69 / Chapter 6.2 --- Finding the Optimal Solution --- p.70 / Chapter 6.2.1 --- By CPLEX Optimization Package --- p.70 / Chapter 6.2.2 --- Problems of Using Optimization Packages --- p.72 / Chapter 6.2.3 --- Observations of Some Optimal Solutions --- p.74 / Chapter 6.3 --- Heuristics Development --- p.77 / Chapter 6.3.1 --- Solution Strategies --- p.77 / Chapter 6.3.2 --- Evaluation of the Strategy Combinations --- p.84 / Chapter 6.3.3 --- Best Combinations --- p.90 / Chapter 6.3.4 --- Final Heuristic --- p.92 / Chapter 7 --- Computational Results --- p.94 / Chapter 7.1 --- Methodology --- p.94 / Chapter 7.2 --- Results of Using the Final Heuristic --- p.95 / Chapter 7.3 --- Computational Time --- p.99 / Chapter 8 --- Managerial Insights --- p.102 / Chapter 8.1 --- Practical Issues --- p.102 / Chapter 8.2 --- Managerial Insights --- p.105 / Chapter 9 --- Future Work and Conclusion --- p.109 / Chapter 9.1 --- Future Work --- p.109 / Chapter 9.2 --- Conclusion --- p.112 / Bibliography --- p.115