Last mile innovations: The case of the Locker Alliance Network
In "Seminars and talks"

Speakers

Chung Piaw Teo
Chung Piaw Teo

Provost’s Chair Professor in NUS Business School, Executive Director of the Institute of Operations Research and Analytics (IORA), National University of Singapore

Chung Piaw Teo is Provost’s Chair Professor in NUS Business School and Executive Director of the Institute of Operations Research and Analytics (IORA) in the National University of Singapore, and concurrently a co-director in the SIA-NUS Digital Aviation Corp Lab. With a focus on optimization and supply chain management, Professor Teo is trying to bridge the gap between theoretical research and practical applications of OR and Analytics in business and engineering.

 

He was a fellow in the Singapore-MIT Alliance Program, an Eschbach Scholar in Northwestern University (US), Professor in Sungkyunkwan Graduate School of Business (Korea), and a Distinguished Visiting Professor in YuanZe University (Taiwan). He is department editor for MS (Optimization), and a former area editor for OR (Operations and Supply Chains). He was elected Fellow of INFORMS and Chang Jiang Scholar (China) in 2019. He has also served on several international committees such as the Chair of the Nicholson Paper Competition (INFORMS, US), member of the LANCHESTER and IMPACT Prize Committee (INFORMS, US), Fudan Prize Committee on Outstanding Contribution to Management (China), and recently chaired the EIC search committee for Operations Research, an INFORMS journal.


Quanmeng Wang
Quanmeng Wang

Research Fellow, Institute of Operations Research and Analytics

Quanmeng Wang is a research fellow at the Institute of Operations Research and Analytics, where he also earned his PhD. His research mainly focuses on model development for operation problems in logistics. He participated in several research projects collaborated with industry partner of IORA, including a leading express company of China and a government public service sector of Singapore.


Date:
Friday, 15 September 2023
Time:
10:00 am - 11:30 am
Venue:
NUS Business School
Mochtar Riady Building BIZ1 0205
15 Kent Ridge Drive
Singapore 119245 (Map)

Abstract

In this talk, we’ll explore a collection of academic research we’ve conducted, funded by IMDA, focusing on Singapore’s “Locker Alliance Network” (LAN). This government-led initiative aims to establish a network of public lockers in residential areas and community hubs to improve the efficiency of last-mile parcel deliveries. Our research tackles key operational questions, such as the ideal density, coverage, and impact of the LAN.

 

To address these questions, we’ve employed locker usage data from a commercial courier service to calibrate a model that gauges how walking distance and other variables influence customer preferences for locker pickups versus traditional home or office deliveries. Additionally, we’ve created a facility location model that leverages existing parcel delivery data to optimize the LAN’s design. Contrary to traditional thinking, our results indicate that peak parcel volume areas are not necessarily the best locations for lockers. Instead, our model recommends an optimal coverage radius of 250 meters for the LAN in Singapore. One unique challenge we faced was the absence of home-office pair information for residents, leading us to develop a new type of facility location model where the choice set is unknown. Our findings suggest that under realistic assumptions—namely, that home delivery will always be more popular than locker pickup—the lack of this specific information has minimal impact on the performance of our locker facility location model.

 

In related research, we’ve also examined the LAN’s effects on routing efficiency and conducted empirical tests to understand how exposure and popularity influence adoption choices. We also discuss how the challenges in this public facility (that it is interoperable and used by many different LSPs) are partially addressed due to a “nested” pattern in the optimal solution to the facility location model.