Optimisation Models and Algorithms for Real-life Transportation Routing and Scheduling Problems

Chen, Binhui (2018) Optimisation Models and Algorithms for Real-life Transportation Routing and Scheduling Problems. PhD thesis, University of Nottingham.

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This thesis investigates the transportation routing and scheduling problems derived from the container transportation in Ningbo Port, which is the second largest port in China. In the container transportation problem, a fleet of trucks are scheduled to accommodate a sequence of container transshipment requests among multiple container terminals. This problem closely relates to the classic vehicle routing problem with time windows (VRPTW) in which the customers are routed to be serviced with minimal cost, satisfying capacity and time constraints. Facing the fast growing throughput in the port, an efficient automatic routing and scheduling system is urgently needed. In the Ningbo Port problem, there are mainly two types of transshipment requests (long-distance and short-distance) need to be scheduled to optimise fleet usage, while the transportation cost for the two types of tasks are different. As a result, to promote the vehicle utility and reduce operational cost at the same time, not only producing solutions of high quality are pursued in this research but also advanced problem modelling methods.

The research starts from studying the VRPTW to understand the characteristics of vehicle routing problems and the features of solution methodologies in detail. An improved variable neighbourhood search algorithm is developed. With the proposed new compounded neighbourhoods, the algorithm combines variable neighbourhood search and variable-depth neighbourhood search. In this single solution-based metaheuristic method, two objectives are optimised simultaneously, generating a number of new non-dominated solutions on benchmark instances. This research explores the ways of balancing diversification and intensification in metaheuristic search, providing experience and guidance for addressing the real-life Ningbo Port container transportation problem.

After the preliminary research on the VRPTW, the Ningbo Port problem then is studied in two steps. At the first stage, the short-haul problem in the port districts is considered. The mathematical model of Vehicle Routing Problem with Time Windows and Open routes (VRPTW-O) is established in this study, providing a more efficient scheduling scheme for Ningbo Port company. Two metaheuristic algorithms are implemented for this new problem, while both of them outperform the state-of-the-art methods for similar problems. Different construction heuristics are compared on diverse instances, providing recommendation heuristics for real-world scenarios. At the second stage of the Ningbo Port problem research, the routing and scheduling of long-haul requests related to inland dry ports are combined with the short-haul requests. By introducing artificial depot to the problem, both types of transshipment tasks are integrated into one vehicle routing problem model, called Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW). The driver salary cost is taken into consideration in this model to optimise the operational cost of the company, leading to a bi-objective VRP variant. To provide a generic solution methodology for multi-objective VRPs instead of a problem-specific algorithm, a selection perturbation hyper-heuristic is implemented in this study. The experimental results show that the proposed method performs well in both the real-life Ningbo Port problem and the classic VRPTW.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Qu, Rong
Keywords: transportation routing, transport routing, scheduling, computer science, ningbo port, verticle routing problem with time windows
Subjects: Q Science > QA Mathematics > QA 75 Electronic computers. Computer science
Faculties/Schools: UK Campuses > Faculty of Science > School of Computer Science
Item ID: 56241
Depositing User: Qu, Rong
Date Deposited: 19 Apr 2024 13:42
Last Modified: 19 Apr 2024 13:42
URI: https://eprints.nottingham.ac.uk/id/eprint/56241

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