Industrial and Manufacturing Systems Engineering

June 20, 2024 (Thursday) 4:30-5:30pm
Due to the rapid development of the e-commerce market and the surging urban logistics demand, the concept of collaborative passenger and freight transportation in the urban context (urban co-modality) is becoming increasingly popular. Urban co-modality exploits the under-utilized capacity in existing urban multimodal transportation systems. This talk will introduce three types of urban co-modality, namely, co-modality based on public transit systems, individual travelers (crowdshipping), and emerging modular vehicles. In this talk, we first introduce analytical and optimization studies of the co-modality problem based on public transit systems, including quantifying the impacts of introducing co-modality on public transit and urban freight systems, examining the Pareto-improving co-modal system that benefits both users and operators, and designing co-modal service networks. We then present recent studies on crowdshipping – engaging travelers in the crowd to carry freight. The optimal pricing strategies of the crowdshipping platform in alternative business formats are identified. Finally, we discuss prospective opportunities to use modular vehicle-based transit systems for co-modal service. Our studies demonstrate that collaborative passenger and freight transportation is a future trend of sustainable transportation, which has the potential to alleviate congestion, reduce carbon emissions, facilitate urban mobility, and create social benefits for users and operators.

May 09 2024 (Thursday) 4:30-5:30pm
Every day, numerous decisions must be made in healthcare systems, addressing a wide range of issues, including patient admissions, physician assignments, operating room scheduling, pharmaceutical inventory control, and ambulance dispatch. Conventionally, these decisions are made by experienced experts. However, human decision-makers may face challenges such as the unavailability of complete information, rapidly changing healthcare environments, personal biases, and prompt responses to medical emergence. Additionally, decision-makers often struggle to evaluate the effectiveness and possible consequences of their decisions.
This talk will introduce a set of scientific and systematic tools that leverage operations research techniques (originally developed to solve decision-making problems during World War II) and data collected in healthcare systems. The talk will begin with an overview of traditional healthcare decision-making approaches before delving into data-driven methodologies that have emerged in recent years. Subsequently, the speaker will share his experience over the years in tackling decision-making problems in healthcare systems in Hong Kong and the region, including emergency department patient flow modelling and optimization, medical appointment scheduling, vaccination strategies, and healthcare logistics. Finally, potential future directions for addressing healthcare decision-making challenges will be explored.
The supports by HKRGC (Refs: 27200419; 17204823; F-HKU704/22) and HMRF of the Health Bureau (Refs: 14202115; 21222881) for the projects to be presented in this talk are greatly acknowledged.

April 11 2024 (Thursday) 4:30-5:30pm
Self-service systems, such as electrical vehicle charging piles, are often deployed in a fleet at a designated location. These systems may fail either while idle or during customer use. If the system initially chosen by a customer is nonfunctional or fails during service, the customer may switch to another available system for service. This phenomenon is called failure-induced demand switching (FDS). To maximize the long-run profit of the fleet, the operator must balance revenue with operation and maintenance costs, by conducting appropriate maintenance actions. However, it is challenging to develop an optimal maintenance policy due to FDS. This talk defines and assesses the service level of such systems in terms of the proportion of demands being completed in the long run, and proposes a three-dimensional maintenance policy to maximize the fleet’s long-run profit rate. The insights are expected to guide the operators of such systems in enhancing their profitability.