Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Logistics and Maritime Studies | en_US |
| dc.contributor.advisor | Xu, Zhou (LMS) | en_US |
| dc.creator | Zhang, Silong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14659 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Routing and network design problems : new algorithms and optimization techniques | en_US |
| dcterms.abstract | Routing and network design problems are fundamental to optimizing the flow of resources, services, and information across interconnected systems. These problems are critical for ensuring the efficiency, scalability, and reliability of modern network infrastructure but present significant computational challenges due to complex operational constraints, vast solution spaces, and stringent survivability requirements. This thesis addresses three key challenges in this domain by developing innovative algorithms and optimization techniques: an enhanced exact algorithm for tackling multitrip scheduling in vehicle routing, novel optimality cuts and domain reduction techniques to reduce the search space of integer programs (including vehicle routing problems), and an efficient heuristic to address the scalability and survivability in large-scale telecommunications networks under double-link failures. | en_US |
| dcterms.abstract | The first study presents an enhanced exact algorithm for the capacitated multitrip vehicle routing problem with time windows (CMTVRPTW). This problem extends the classical vehicle routing problem by incorporating time windows and allowing vehicles to perform multiple trips. While this problem captures operational requirements of real-world logistics networks, it is significantly more challenging to solve. In this study, we present an enhanced exact algorithm to obtain the optimal integer solution by solving a trip-based formulation, which allows for an efficient solution method to the constraint separation problem. To further enhance computational efficiency, we reduce the number of variables by eliminating trips using the dominance rule, variable fixing technique, and optimality property of vehicle departure times. Additionally, a dynamic time discovery strategy is introduced to reduce the number of constraints. Computational experiments demonstrate that our enhanced algorithm is highly effective for solving challenging instances of the CMTVRPTW. On 14 benchmark instances previously unsolved or considered difficult, which involve 140–200 customers, our method outperforms the current state-of-the-art exact algorithm within a ten-hour time limit, solving 6 additional instances to optimality and reducing average runtime by over 20%. | en_US |
| dcterms.abstract | The second study concerns the effective reduction of the solution search space for integer programs, particularly in the context of routing problems. Integer programming formulations for combinatorial optimization problems face a significant computational challenge due to the exponential growth of the solution space. To address this challenge, we introduce Lagrangian underestimate cuts (LUCs), a novel class of optimality cuts derived from Lagrangian duals, including infeasible LP duals. We utilize LUCs to strengthen reduced cost cuts and to derive new domain reduction techniques. Additionally, we leverage LUCs to develop new arc fixing techniques for the vehicle routing problem with time windows. These methods demonstrate significant potential for pruning solution spaces. | en_US |
| dcterms.abstract | The third study investigates the survivable traffic grooming problem under double-link failures (STG2) in large-scale telecommunications networks, where each communication demand must be assigned a route for every possible scenario involving zero, one, or two failed fiber links. Ensuring protection against double-link failures is critical for maintaining reliable telecommunications services while minimizing equipment costs. To the best of our knowledge, this problem remains unexplored in the existing literature, with current research primarily focusing on simpler problem settings involving smaller networks, typically with no more than 300 nodes. To address this gap, we propose a novel hierarchical constructive heuristic for the large-scale STG2 problem. It incorporates various techniques, including demand aggregation, a reuse-then-search routing strategy, and parallel computing, to significantly enhance efficiency. Extensive experiments have been conducted on large-scale STG2 instances provided by an industry partner, encompassing networks with 1,000 to 2,600 nodes. The results demonstrate that within a one-hour time limit and a 16 GB memory limit required by the industry partner, our heuristic improves the objective values of the best-known solutions by 18.6% on average, highlighting its efficiency for large-scale telecommunications networks. | en_US |
| dcterms.abstract | Collectively, the studies presented in this thesis advance the state-of-the-art in routing and network design problems by introducing innovative algorithms and optimization techniques tailored to the computational challenges. By addressing issues such as multitrip scheduling, solution space reduction, and survivability in large-scale networks, these contributions provide effective and scalable solutions to complex real-world problems. These advancements are crucial for improving the efficiency, scalability, and reliability of modern network infrastructure. | en_US |
| dcterms.extent | xvii, 145 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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