| Author: | Ouyang, Xu |
| Title: | Logistics and service operations under disruptions |
| Advisors: | Chung, Sai-ho Nick (ISE) |
| Degree: | Ph.D. |
| Year: | 2026 |
| Subject: | Logistics -- Management Service industries -- Management Mathematical optimization Crisis management Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | 150 pages : color illustrations |
| Language: | English |
| Abstract: | Over the past five years, various disruptions have frequently occurred in the world, calling for a new level of operational resilience from both public and private organizations. This thesis aims to address this need through a structured investigation presented in three original studies, which collectively examine this theme. The thesis is progressive and contains i) a review of the recent development of studies on logistics and service operations under disruptions, as well as the methodology of modeling uncertainties arising in disruptions. In particular, it establishes an understanding of the current research landscape and identifies a powerful tool for modeling uncertainties, i.e., distributionally robust optimization (DRO). Various applications of DRO in operations are reviewed, bridging theory and practice. Equipped with a comprehensive understanding of both the problem domain and key methodologies, the research then proceeds to practical applications. The first analytical study is ii) an investigation of targeted intercity travel restriction policies under pandemics, which applies the DRO framework to a critical public health logistics problem. To explore operational challenges under disruption in a competitive environment, the third study shifts focus to the service sector. This work is iii) an analytical study on the adoption of service downgrading strategies (SDS) under economic downturns, which examines competitive strategy using game theory, while also incorporating DRO to model demand uncertainty. The above study sequence provides a progressive exploration of operational resilience across different contexts. Regarding i), the logistics and service sectors have recently struggled with major disruptions from events like the COVID-19 pandemic, geopolitical tensions, and extreme weather, creating critical operational challenges. To understand how to hedge against these impacts, we conduct a focused review of the latest literature. It identifies primary sources of disruption, classifies their effects on operational uncertainty, and demonstrates how classic problems like vehicle routing are being adapted. This review also surveys representative methodologies used to solve these problems. Among these methods, DRO has gained increasing attention for modeling under uncertainty. Therefore, we further illustrate the interface between DRO and specific operations problems. It begins with preliminary of DRO, presents its application in various scenarios like fleet operations and resource allocation, and uses the newsvendor model to deepen understanding. This work provides researchers and managers with insights into this vital tool for enhancing operational resilience. Regarding ii), as an effective non-pharmaceutical tool, travel restrictions are often adopted to contain the early spread of severe infectious diseases (e.g., SARS, COVID-19, and monkeypox). This study examines targeted intercity travel restriction policies to hedge against potential pandemic deterioration. We consider the correlation of disease spread among cities in deteriorating scenarios, modeled by DRO. The problem is formulated by a nonlinear huge-scale DRO model and then reduced to a mixed-integer linear equivalence of moderate scales. We develop a Benders decomposition algorithm, utilizing a special relation between master- and sub-problem variables. Experiments show our model "mean-variance dominates" a classic robust optimization model that ignores the correlation of spread, in terms of higher pandemic control performance and lower performance variation. In the case study, targeted policies exhibit two to five times the pandemic control performance (i.e., infections reduced or human lives saved) against uniform travel restriction policies adopted in the real world. Policymakers are advised to impose targeted intercity travel restrictions as early as possible, since pandemic control performance is more obvious at the early outbreak stage. Targeted restriction policies are promising for developing countries because of comparable pandemic control performance under limited budgets, compared with those with adequate budgets. Regarding iii), in recent years, facing economic downturns and intense competition, many companies in the service industry (e.g., American Airlines in aviation and Hilton in hospitality) have downgraded certain services to cut operating costs (e.g., Hilton cancelled free breakfasts in some places). We term this emerging practice the Service Downgrade Strategy (SDS). While SDS lowers operating expenses, it impairs customer experience, risking demand loss. To offset this, some companies innovatively pair SDS with loyalty programs. Loyalty programs reward customers with "points" redeemable for gifts or other perks, which foster engagement, retention, and repeat purchases in a cost-efficient manner. However, when to implement SDS and how to design the corresponding loyalty program in a competitive environment remain unexplored. To fill this gap, we develop a Stackelberg game model where two service companies compete in SDS commitment and associated loyalty program investments. Our model reveals the following insights: i) The expected post-SDS market size (EPSM) is the key determinant for the two companies' equilibrium outcomes. ii) SDS always requires higher loyalty program investments to offset demand loss. iii) Unexpectedly, low competition would drive the adoption of SDS for both companies. iv) Market power asymmetry shapes equilibrium outcomes. Specifically, the same operational efforts (i.e., cost-saving ability during SDS implementation) could exhibit opposite effects on the leader and the follower. In extensions, we generalize our analysis and examine the findings' robustness by considering three operational features, namely a quadratic cost structure of loyalty program investments, the distributional ambiguity of the post-SDS market size and companies' risk attitudes, and the game of one leader and more than two followers. In conclusion, this thesis contributes to the literature by exploring the operational challenges posed by disruptions. It progresses from broad literature surveys to focused methodological reviews and, finally, to the application of these advanced methods in critical logistics and service operations contexts. The collective findings provide valuable insights and decision-support tools for both public policymakers and private-sector managers aiming to enhance operational resilience in an increasingly uncertain world. |
| Rights: | All rights reserved |
| Access: | open access |
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