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Past Operations and Logistics Division and Centre for Operations Excellence Seminars

Past OPLOG Division Seminars September 2017 - August 2018
Past OPLOG Division Seminars - September 2016 - July 2017
Past OPLOG Division and COE Seminars - September 2015 - August 2016

Past OPLOG Seminars January 2015 -  August 2015
Past OPLOG Seminars - August 2014 - December 2014
Past OPLOG Seminars September 2013 - May 2014

Past OPLOG Seminars - September 2012 - May 2013

OPLOG Division Seminars September 2018 - ONWARDS


Date: Monday, February 25th, 2019

Speaker: Kostas Bimpikis, Stanford University
Title: "Spatial Pricing in Ride-Sharing Networks"
Time: 2:30 PM - 3:30 PM
Place: HA 968

Abstract: We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders are heterogeneous in terms of their destination preferences and their willingness to pay for receiving service. Drivers decide whether, when, and where to provide service so as to maximize their expected earnings, given the platform’s prices. Our findings highlight the impact of the demand pattern on the platform’s prices, profits, and the induced consumer surplus. In particular, we establish that profits and consumer surplus are maximized when the demand pattern is "balanced" across the network’s locations. In addition, we show that they both increase monotonically with the balancedness of the demand pattern (as formalized by its structural properties). Furthermore, if the demand pattern is not balanced, the platform can benefit substantially from pricing rides differently depending on the location they originate from. Finally, we consider a number of alternative pricing and compensation schemes that are commonly used in practice and explore their performance for the platform.

Joint work with Ozan Candogan and Daniela Saban

 

Link to the paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2868080


 

Date: Monday, March 4th, 2019

Speaker: Kevin Shang, Duke University
Title: "Inventory Management for Multidivisional Firms with Cash Pooling"
Time: 2;30 PM - 3:30 pm
Place: DL 125

Abstract: Cash pooling is a powerful management tool that allows each division’s cash balance to be transferred to a single account managed by the corporate treasury in the headquarter. While the reported benefits of cash pooling are associated with the reduction of transaction and financing costs, the value of cash pooling is not clear from a perspective of improving operational efficiency. In this talk, we examine the benefit of cash pooling on inventory replenishment for multidivisional firms through two models. The first model considers a series supply chain in which demand occurs at the most downstream division, and each division orders from its upstream division. The second model considers a distribution supply chain in which each division replenishes from an outside supplier to meet its local demand. The corporate treasury receives cash payments from customers and determines how much to invest externally for a positive return in each period. There are holding costs for the on-hand inventory and unfilled demands incur backorder costs. The objective is to obtain the optimal joint cash retention and inventory replenishment policy that maximizes the expected net worth (equity).  For the series model, we show that the optimal policy has a simple structure—each division implements a base-stock policy for inventory replenishment; the corporate treasury monitors the system working capital and implements a two-threshold policy for cash retention. For the distribution model, we provide a simple and effective heuristic derived from the construction of a lower bound to the optimal value function. Our lower bound improves the so-called Lagrangian-relaxation bound and the induced-penalty bound in the multi-echelon literature. We quantify the value of cash pooling for both models. Our research leads to a new online simulation game, called Cash Beer Game, which incorporates cash flows into the standard Beer Game. I shall demonstrate this online game in this talk.  


Date: Monday, March 11th, 2019

Speaker: Nikos Trichakis, MIT
Title: "TBA"
Time
: 2:30 PM - 3:30 PM
Place: HA 254

 Abstract: TBA


Date: Monday, March 18th, 2019

Speaker: Opher Baron, Rotman School of Business, University of Toronto
Title: "TBA"
Time: 2:30 PM - 3:30 PM
Place: DL 125

Abstract: TBA


Date: Monday, April 1st, 2019

Speaker: Van-Anh Truong, Columbia University
Title: "Dynamic Optimization of Mobile Push Advertising Campaigns"
Time: 2:30 PM - 3:30 PM
Place: HA 968

Abstract: We study a novel resource-allocation problem faced by Alibaba Group. In this problem, mobile “push messages” must be sent over the course of a day to hundreds of millions of users. Each message can be sent to any number of users, and yields a reward when it generates a clickthrough, subject to a budget constraint on the total reward over all users for the message. This budget represents the maximum amount that an advertiser is willing to pay for clickthroughs for the message on a given day. Given users’ diverse preferences, the problem aims to deliver the “right messages” to the “right users” to maximize ad revenues without overwhelming each user with too many messages. Due to the large size of the real application, we analyze algorithms for the above problem in an asymptotic regime. We consider a novel scaling of the problem “size,” called big-data scaling. In this scaling, as the problem size grows, the number of users, as well as their diversity, grow. The scaling captures the fact that individual user information remains highly granular and distinctive even as the size of the user base increases. We prove that solving the problem as a static assignment problem results in a regret of O( √ t), where t is the parameter scaling the problem. Furthermore, adding a single recourse opportunity, by sending push messages in two cycles over the course of a day and making use of information observed in the first cycle to adapt decisions in the second cycle, can reduce the regret to O(t 1/4 log t). Finally, the difference in regret between the static and dynamic strategy can be Ω(√ t). Numerical experiments on three real data sets, each containing several hundred million users, show that the latter strategy improves the regret of the former by at least 10%-50%.  

This is joint work with Xinshang Wang, Shenghuo Zhu, and Qiong Zhang.


Date: Monday, April 8th, 2019

Speaker: Tamar Cohen, MIT
Title: "TBA"
Time: 2:30 PM - 2:30 PM
Place: HA 969

Abstract: TBA


 

Date: Monday, April 15th, 2019

Speaker: Jing Dong, Columbia University
Title: "TBA"
Time: 2:30 PM - 3:30 PM
Place: HA 968

Abstract: TBA


Date: Wednesday, April 17th, 2019

Speaker: Shouqiang Wang, University of Texas at Dallas
Title: "TBA"
Time: 2:30 PM - 3:30 PM
Place: DL 125

Abstract: TBA


Date: Wednesday, April 24th, 2019

Speaker: Ho-Yin Mak, Oxford University, UK
Title: "TBA"
Time: 12:30 PM - 1:30 PM
Place: DL 125





 

Date:  Tuesday, June 19th, 2018 Speaker: David Stanford, The University of Western Ontario Title:  "An updated approach to Emergency Department prioritization in light of empirical data" Time: Noon - 1:00 PM Place: Henry Angus 968
Date:  Tuesday, June 19th, 2018 Speaker: David Stanford, The University of Western Ontario Title:  "An updated approach to Emergency Department prioritization in light of empirical data" Time: Noon - 1:00 PM Place: Henry Angus 968
Date:  Tuesday, June 19th, 2018 Speaker: David Stanford, The University of Western Ontario Title:  "An updated approach to Emergency Department prioritization in light of empirical data" Time: Noon - 1:00 PM Place: Henry Angus 968
Date:  Tuesday, June 19th, 2018 Speaker: David Stanford, The University of Western Ontario Title:  "An updated approach to Emergency Department prioritization in light of empirical data" Time: Noon - 1:00 PM Place: Henry Angus 968