Justin Goodson - Saint Louis University

Thursday, March 21, 2024 from 12pm to 1pm

Room F 137

Zoom meeting : https://essec.zoom.us/j/93257234545

Title: Optimal Service Time Windows

Abstract: Because customers must usually arrange their schedules to be present for home services, they desire an accurate estimate of when the service will take place. However, even when firms quote large service time windows, they are often missed, leading to customer dissatisfaction. Wide time windows and frequent failures occur because time windows must be communicated to customers in the face of several uncertainties: future customer requests are unknown, final service plans are not yet determined, and when fulfillment is outsourced to a third party, the firm has limited control over routing procedures and eventual fulfillment times. Even when routing is performed in-house, time windows often do not receive explicit consideration. In this paper, we show how companies can communicate reliable and narrow time windows to customers in the face of arrival time uncertainty when time window decisions are decoupled from routing procedures. Under assumptions on the shape of arrival time distributions, our main result characterizes the optimal policy, identifying structure that reduces a high-dimensional stochastic non-linear optimization problem to a root-finding problem in one dimension. The result inspires a practice-ready heuristic for the more general case. Relative to the industry standard of communicating uniform time windows to all customers, and to other policies applied in practice, our method of quoting customer-specific time windows yields a substantial increase in customer convenience without sacrificing reliability of service. Our results show that time windows should be tailored to individual customers, time window sizes should be proportional to the service level, larger time windows should be assigned to earlier requests and smaller time windows to later requests, larger time windows should be assigned to customers further from the depot of operation and smaller time windows to closer customers, high quality time windows can be identified even with limited data, and cost savings afforded by routing efficiency should be measured against potential losses to customer convenience.


Dana PIZARRO - Universidad de O'Higgins, Chile

Wednesday, January 31, 2024 from 12pm to 1pm / Room F122 

Title: The Value of Observability in Dynamic Pricing 

Abstract: We consider a dynamic pricing problem where a firm sells one item to a single buyer in order to maximize expected revenues. The firm commits to a price function over an infinite horizon. The buyer arrives at some random time with a private value for the item. He is more impatient than the seller and strategizes the time of his purchase in order to maximize his expected utility, which implies either buying immediately or waiting to benefit from a lower price. We study how important it is to observe the buyer's arrival time in terms of the seller's expected revenue. When the seller can observe the arrival of the buyer, she can make the price function contingent on his arrival time. On the contrary, when the seller cannot observe the arrival, her price function is fixed at time zero for the whole horizon. The value of observability (VO) is defined as the worst case ratio between the expected revenue of the seller when she observes the buyer's arrival and that when she does not. Our main result establishes that in a very general setting about valuation and arrival time distributions, the value of observability is bounded by a small constant. To obtain this bound we fully characterize the observable arrival setting and use this solution to construct a random and periodic price function for the unobservable case. 

This is joint work with José Correa and Gustavo Vulcano


Vinay Ramani - Indian Institute of Technology, Kanpur

Wednesday, December 13th, 2023 from 12pm to 1pm /  Room F133

Title: Supply chain role-specific investments and consumer welfare under the threat of manufacturer encroachment


Abstract: In many supply chain practices, the participants put in role-specific investments to increase their profit and supply chain profits. For example, the manufacturer invests in cost-reducing efforts, and the retailer invests in demand-enhancing efforts to manage the supply chain. In this study, we analyze the effect of manufacturer encroachment on decision-making and welfare when both the manufacturer and retailer make role-specific investments. Our analysis yields two interesting results. First, we find that while the retailer can deter manufacturer encroachment by role-specific investment, it can benefit the manufacturer and generate a higher total surplus under specific economic environments, though at the expense of a drop in consumer surplus. Second, in contrast to the conventional wisdom of encroachment benefiting manufacturers and consumers, role-specific investments may lower manufacturer profits and consumer surplus. Under such a scenario, regulators may need to protect the consumers by inducing supply chain participants to move from specialized role-specific investments to cooperative investments.


Brief Bio: Vinay Ramani is an Associate Professor in the Department of Management Sciences at the Indian Institute of Technology, Kanpur. He completed his doctoral degree in economics from the University at Buffalo (SUNY). His research interests include game-theoretic models of strategic delegation and encroachment in distribution channels, product cannibalization, and value creation in supply chains.


Layla MARTIN - Eindhoven University of Technology - Netherland 

October  25th, 12pm - 1pm /  Room C5

Exact Two-Step Benders Decomposition for the Time Window Assignment Traveling Salesperson Problem  

Abstract: Next-day delivery logistics services are redefining the industry by increasingly focusing on customer service. A challenge each logistics service provider faces is to jointly optimize time window assignment and vehicle routing for such next-day delivery services. To do so in a cost-efficient and customer-centric fashion, real life uncertainty such as stochastic travel times need to be incorporated in the optimization process. This paper focuses on the canonical optimization problem within this context; the Time Window Assignment Traveling Salesperson Problem with Stochastic Travel Times (TWATSP-ST). It belongs to the class of two-stage stochastic mixed-integer programming problems with continuous recourse. We introduce Two-Step Benders Decomposition with Scenario Clustering (TBDS) as a general exact solution methodology for solving such stochastic programs to optimality. The method combines and generalizes Benders dual decomposition, partial Benders decomposition, and Scenario Clustering techniques and does so within a novel two-step decomposition along the binary and continuous first-stage decisions. Extensive experiments show that TBDS is superior to state-of-the-art approaches in the literature. It solves TWATSP-ST instances with up to 25 customers to optimality. It provides better lower and upper bounds that lead to faster convergence than related methods. For example, Benders dual decomposition cannot optimally solve instances of 10 customers. We use TBDS to analyze the structure of the optimal solutions. By increasing routing costs only slightly, customer service can be improved tremendously, driven by smartly alternating between high- and low-variance travel arcs to reduce the impact of delay propagation throughout the executed vehicle route. Joint work with Sifa Celik, Layla Martin, Albert H. Schrotenboer, Tom Van Woensel