Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | en_US |
| dc.contributor.advisor | Fu, Xiaowen (ISE) | en_US |
| dc.contributor.advisor | Xu, Zhou (LMS) | en_US |
| dc.creator | Huang, Zhenyu | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14137 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | A simulation-optimization framework for resource allocation at parcel-sorting hub | en_US |
| dcterms.abstract | The rapid growth of e-commerce and live-streaming retail in China has intensified demand volatility in parcel delivery services, posing significant challenges for parcel-sorting hubs to allocate labor, equipment, and workstations effectively. At SF Express—China's leading logistics provider — traditional planning tools such as spreadsheets and deterministic models have struggled to address the stochastic and dynamic nature of parcel hub operations, resulting in rising operational costs and frequent service failures. | en_US |
| dcterms.abstract | To overcome these challenges, this thesis proposes and implements a three-phase Static-Simulation-Optimization (SSO) framework at SF Express's Shenzhen hub. The approach begins with static analysis to establish operational baselines and generate domain-specific insights. Discrete-event simulation (DES) is then used to identify systemic bottlenecks and evaluate improvement opportunities. Finally, solver-based optimization—driven by a validated simulation model and customized search strategies—systematically explores the solution space to identify optimal resource configurations that minimize costs while meeting operational constraints. | en_US |
| dcterms.abstract | Field testing conducted on a high-priority ground-to-air operation from March to April 2024 achieved an 18.7% cost reduction through simulation-guided improvements and up to 33.5% savings with solver-based optimization. When extended to all Shenzhen hub operations for the remainder of 2024, the framework delivered an average cost reduction of 11%, with the most significant gains observed in air-bound flows constrained by strict outbound schedules. | en_US |
| dcterms.abstract | Designed for rapid deployment by frontline teams, the framework supports timely, data-driven decision-making without requiring advanced analytics expertise. Its successful application at SF Express demonstrates the practical potential of simulation-optimization to enhance logistics planning in increasingly stochastic environments, offering a scalable and replicable model for industry-wide adoption. | en_US |
| dcterms.extent | xiii, 164 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | Eng.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8589.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 14.86 MB | Adobe PDF | View/Open |
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