This one day seminar is jointly organised by the Engineering Systems and Design (ESD) pillar of Singapore University of Technology and Design (SUTD) and the National University of Singapore (NUS) Institute of Operations Research and Analytics (IORA). The seminar will bring together academia leaders and researchers from local universities to create greater awareness of the implications of revenue management in the sharing economy. The seminar is the platform to stimulate interdisciplinary research and collaboration among the research communities to address the opportunities and threats for revenue management with the exponential growth in sharing economy. The seminar consists of seven research talks. Click here to register and secure your place at the seminar.

Programme

Time Programme
9.00am – 9:30am Start of seminar with light breakfast served
9.30am – 9:45am Welcome address
9.45am – 10.45am Professor David Simchi-Levi, Massachusetts Institute of Technology

Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints

10.45am – 11.15am Break
11.15am – 12.15pm Professor Guillermo Gallego, The Hong Kong University of Science and Technology

Joint Pricing and Inventory Decisions for Substitutable Products

12.15pm – 12.45pm Assistant Professor Napat Rujeerapaiboon, National University of Singapore

Robust Multidimensional Pricing: Separation Without Regret

12.45pm – 2.00pm Lunch
2.00pm – 2.30pm Assistant Professor Yan Zhenzhen, Nanyang Technological University

Data Driven Optimization to Full Cut Promotion in E-commerce

2.30pm – 3.30pm Professor Saif Benjaafar, University of Minnesota

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

3.30pm – 4.00pm Break
4.00pm – 4.30pm Assistant Professor Guiyun Feng, Singapore Management University

We Are on the Way: Analysis of On-Demand Ride-Hailing Systems

4.30pm – 5.00pm Assistant Professor Ying Xu, Singapore University of Technology and Design

How Ride Hailing Affects Vehicle Demand and Mileage: An Empirical Study in Singapore

5.00pm End of seminar

International and Local Faculty Speakers

Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints

Professor David Simchi-Levi,
Civil and Environmental Engineering
Massachusetts Institute of Technology

We consider the classical stochastic multi-armed bandit problem with a constraint on the total cost incurred by switching between actions. We prove matching upper and lower bounds on regret and provide near-optimal algorithms for this problem. Surprisingly, we discover phase transitions and cyclic phenomena of the optimal regret. That is, we show that associated with the multi-armed bandit problem, there are phases defined by the number of arms and switching costs, where the regret upper and lower bounds in each phase remains the same and drop significantly between phases. The results enable us to fully characterize the trade-off between regret and incurred switching cost in the stochastic multi-armed bandit problem, contributing new insights to this fundamental problem. Under the general switching cost structure, the results reveal a deep connection between bandit problems and graph traversal problems, such as the shortest Hamiltonian path problem. This presentation is based on joint work with Yunzong Xu.
David Simchi-Levi is a Professor of Engineering Systems at MIT. He is considered one of the premier thought leaders in supply chain management and business analytics. His research focuses on developing and implementing robust and efficient techniques for operations management. He has published widely in professional journals on both practical and theoretical aspects of supply chain and revenue management. Professor Simchi-Levi co-authored the books Managing the Supply Chain (McGraw-Hill, 2004), the award winning Designing and Managing the Supply Chain (McGraw-Hill, 2007) and The Logic of Logistics (3rd edition, Springer 2013). He also published Operations Rules: Delivering Customer Value through Flexible Operations (MIT Press, 2011). Professor Simchi-Levi is the current Editor-in-Chief of Management Science, one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005). He is an INFORMS Fellow, MSOM Distinguished Fellow and the recipient of the 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; 2009 INFORMS Revenue Management and Pricing Section Prize and Ford 2015 Engineering Excellence Award. Professor Simchi-Levi has consulted and collaborated extensively with private and public organizations. He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain intelligence. The company became part of the Accenture Applied Intelligence in 2018.

Joint Pricing and Inventory Decisions for Substitutable Products

Head & Chair Professor Guillermo Gallego
Department Industrial Engineering and Decision Analytics
The Hong Kong University of Science and Technology (HKUST)

We propose asymptotically optimal policies for a joint inventory and price control problem where the seller replenishes substitute products only once and dynamically controls the prices during the selling season to maximise the total expected profit. The algorithm consists of an efficient nonlinear program to determine prices and a linear complementarity problem to decide on inventory levels reflecting consumer-driven substitution. We also show that a simple heuristic to dynamically update prices can further improve expected profits.
Professor Guillermo Gallego is the Department Head of Industrial Engineering and Decision Analytics, and also the Crown Worldwide Professor of Engineering. Prior to his appointment in January 2016, Professor Gallego was the Liu Family Professor at the Department of Industrial Engineering and Operations Research at Columbia University, where he served as the Department Chairman from 2002-2008. He was named a Manufacturing and Service Operations Management Society (MSOM) Distinguished Fellow in 2013, an INFORMS Fellow in 2012 and has been the recipient of many awards including the Revenue Management Historical Prize (2011), the Revenue Management Practice Prize (2012), the INFORMS Impact Prize (2016), and the Management Science Best Paper Award (2017). Professor Gallego’s research interests are Dynamic Pricing and Revenue Optimization, Supply Chain Management, Electronic Commerce, and Inventory Theory. He has published influential papers in the leading journals of his field where he has also occupied a variety of editorial positions. His work has been supported by numerous industrial and government grants. In addition to theoretical research, Professor Gallego has developed strong collaboration with global corporations such as Disney World, Hewlett Packard, IBM, Lucent Technologies, Nomis Solutions, and Sabre Airline Solutions. He has also worked with government agencies such as the National Research Council, the National Science Foundation in United States and the Ireland Development Agency. His graduate students are associated with prestigious universities and occupy leading roles in their chosen fields. He spent his 1996-97 sabbatical at Stanford University and was a visiting scientist at the IBM Watson Research Center from 1999-2003. Professor Gallego received both his PhD degree (1988) and MS degree (1987) in Operations Research and Industrial Engineering from Cornell University.

Robust Multidimensional Pricing: Separation Without Regret

Assistant Professor Napat Rujeerapaiboon
Industrial Systems Engineering and Management
National University of Singapore (NUS)

We study a robust monopoly pricing problem with a minimax regret objective, where a seller endeavours to sell multiple goods to a single buyer, only knowing that the buyer’s values for the goods range over a rectangular uncertainty set. We interpret this problem as a zero-sum game between the seller, who chooses a selling mechanism, and a fictitious adversary, who chooses the buyer’s values. We prove that this game admits a Nash equilibrium that can be computed in closed form. We further show that the deterministic restriction of the problem is solved by a deterministic posted price mechanism.
Napat Rujeerapaiboon recently joined the Department of Industrial Systems Engineering and Management, National University of Singapore, in 2018. He holds a PhD degree in Risk Analytics and Optimisation from École Polytechnique Fédérale de Lausanne. His research interests are in the areas of robust and distributionally robust optimisation, data-driven optimisation, and risk analytics.

Data Driven Optimization to Full Cut Promotion in E-commerce

Assistant Professor Yan Zhenzhen
School of Physical & Mathematical Sciences
Nanyang Technological University (NTU)

Full-cut promotion is a type of promotion where customers spend a minimum threshold amount in a single order to enjoy a fixed amount of discount for that order. It is widely used in e-commerce companies such as VIPshop and Tmall in China. In this paper, we first applied machine learning approach to extract the full cut demand from the sales data which is a mixture of transactions of promotion sensitive customer and promotion insensitive customer. We further propose a sequential choice model to model consumer’s choice of multiple items in a single transaction, which generalises the traditional choice model which assumes at most one item chosen in one transaction. Based on the sequential choice model, we build a general convex pricing optimisation model under certain condition. We further show the full cut promotion optimisation problem fits in the framework but with a set of pricing constraints. In the case that there are at most two items in each order, we show the pricing problem can be solved by a mixed integer second order program. In a general case, we further propose an iterative approach to solve a sequence of mixed integer program to approximately solve the full cut promotion optimisation problem.
Yan Zhenzhen is an Assistant Professor at School of Physical and Mathematical Sciences (SPMS), Nanyang Technological University. She joined SPMS since 2018. Before that, she received her PhD in Management Science from the National University of Singapore, and her B.Sc. and M.Sc. in Management Science, Operations Research from the National University of Defense and Technology in China. Her research interests mainly focus on the interplay between optimisation and data analytics. She is keen to solve various operations management problems and engineering problems from the distrbibutionally robust perspective, including supply chain design and operations, e-commerce operations and healthcare operations. She is also particularly interested in data driven pricing problem and sequential decision making problems.

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Professor Saif Benjaafar
Distinguished McKnight University Professor
Industrial and Systems Engineering
University of Minnesota

The sharing economy, a term we use to refer to business models built around on-demand access to products and services mediated by online platforms that match many small suppliers or service providers to many small buyers, has emerged as an important area of study in operations management. We first describe three “canonical” applications that have garnered much attention from the operations management community: (1) peer-to-peer resource sharing, (2) on-demand service platforms, and (3) on-demand rental networks. We use these applications to highlight distinguishing features of sharing economy business models and to point out research questions that are new. For each application, we describe our attempt at addressing some of these questions. We conclude by drawing connections between classical operations management theory/models and theory/models that have been used to study sharing economy applications.
Saif Benjaafar is Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and Design where he served as Head of Engineering Systems and Design. He is the Editor in Chief of the INFORMS journal Service Science. He serves on the board of directors of Hourcar, a social car sharing organization. His research is in the area of operations management broadly defined, with a current focus on sustainable operations and innovation in business models, including sharing economy, on-demand services, and digital marketplaces. The work described in this talk has been funded by grants from the US National Science Foundation, the Bill and Melinda Gates Foundation, and the Singapore Ministry of Education.

We Are on the Way: Analysis of On-Demand Ride-Hailing Systems

Assistant Professor Guiyun Feng
Operations Management
Lee Kong Chian School of Business
Singapore Management University (SMU)

Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smart phones to submit trip requests and match them to drivers based on their locations and drivers’ availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system, in particular, whether it will help reduce passengers’ average waiting time compared to traditional street-hailing systems. The on-demand ride hailing problem has gained much academic interest recently. The results we find in the ride hailing system have a significant deviation from classic queueing theory where en route time does not play a role. In this paper, we shed light on this question by building a stylised model of a circular road and comparing the average waiting times of passengers under various matching mechanisms. We discover the inefficiency in the on-demand ride-hailing system when the en route time is long, which may result in non-monotonicity of passengers’ average waiting time as passenger arrival rate increases. After identifying key trade-offs between different mechanisms, we find that the on-demand matching mechanism could result in lower efficiency than the traditional street hailing mechanism when the system utilisation level is medium and the road length is long. To overcome the disadvantage of both systems, we further propose adding response caps to the on-demand ride-hailing mechanism and develop a heuristic method to calculate a near-optimal cap. We also examine the impact of passenger abandonments, idle time strategies of taxis and traffic congestion on the performance of the ride-hailing systems. The results of this research would be instrumental for understanding the trade-off of the new service paradigm and thus enable policy makers to make more informed decisions when enacting regulations for this emerging service paradigm.
Guiyun Feng is currently an Assistant Professor of Operations Management in the Lee Kong Chian School of Business at Singapore Management University. She holds a Ph.D. from the Industrial & Systems Engineering Department at the University of Minnesota. Her research interests include revenue management, service operations and stochastic simulation. Besides academic research, she also interned as a machine learning scientist in Amazon during the summer of 2017.

How Ride Hailing Affects Vehicle Demand and Mileage: An Empirical Study in Singapore

Assistant Professor Ying Xu
Engineering Systems and Design
Singapore University of Technology and Design (SUTD)

Ride-hailing platforms like Uber, Grab and Didi affect individuals’ willingness to purchase and drive personal vehicles in two competing ways: on the one hand, one might wish to own a car and provide ride-hailing service to earn extra income; on the other hand, one might choose to use the ride-hailing service rather than driving itself to satisfy one’s travel needs. Facing the trade-off, individuals switch between providers and users of ride-hailing service under different conditions, leading to varying matching rates of ride-hailing service, which inversely affects their utilities from being ride-hailing service provider or user. With such complexity we develop a discrete choice model to capture the aggregate choices of a group of heterogeneous individuals at equilibrium. We calibrate the model using Singapore data. Our counterfactual analysis illustrates the impacts of ride-hailing rate and driving cost on car demand and mileage, and thus provides insight for ride-hailing platforms and governmental traffic management departments.
Ying Xu received her Ph.D. in operations management at Carnegie Mellon University. Her research involves applying stochastic modelling and game theory to solve emerging problems in service operations management, socially responsible operations, and renewable energy demand-side management.

Admission is FREE!
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Only 50 seats are available, on a first come first served basis.

Closing date: 24 June, 2019
contact phdesd@sutd.edu.sg for queries

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