In Singapore, the national effort to increase productivity has focused mainly on the supply side. However, there is substantial scope to raise productivity on the demand side, particularly through self-service. Self-service technologies include automated teller machines, self-service checkout at supermarkets, and automated telephone systems such as phone banking, automated hotel checkout, self-service ticketing machines and hospital registrations, and internet and app-based services. In this project, we seek to understand the underlying reasons that may account for consumers’ resistance to using self-service technologies. In particular, consumers may feel that there is insufficient reason or benefit to using these technologies (low motivation), or that these technologies are too difficult to navigate such that consumers would rather adhere to their habits of using alternative solutions, such as human checkout in supermarkets (high adoption barriers). With an improved understanding of these underlying reasons, we aim to design effective interventions to increase the adoption of self-checkout technologies.

Federated lockers

This Project studies last mile parcel delivery service using the Singapore federated locker system. This proposed network of parcel lockers aim to reduce the cost to logistic companies. We have developed a predictive model to capture the appeal of locker pick-up option to consumers. A locker network design model is also developed to maximize the utilization of the locker system, after accounting for demand endogeneity. In the future, we will investigate the impact of the Federated Locker System on the performance of last mile delivery companies, Singapore E-commerce scene, and impact on environment.

Patient flow management

Hospital crowding has been a worldwide crisis of healthcare delivery, compromising the quality of and access to medical care. Numerous studies have revealed an association between crowding and increased morbidity and mortality. In Singapore, the demand for healthcare services is rapidly growing due to the ageing population. The healthcare system has been stretched and overloaded. In this line of research, we apply operations research methods to enhance the performance of the healthcare service delivery system. In particular, we focus on data-driven approaches to patient flow management in various departments of hospitals, such as patient scheduling in emergency departments, inpatient bed assignment in hospital wards, and surgical procedure scheduling for operating rooms.