Mahsa Hosseini

Mahsa Hosseini

PhD Candidate at Rotman School of Management On the 2022-2023 academic job market

Posts by Collection

portfolio

publications

Dynamic Relocations in Car-Sharing Networks

Published in Major revision at second round of review at Operations Research, 2021

We propose a novel dynamic car relocation policy for a car-sharing network with centralized control and uncertain, unbalanced demand. The policy is derived from a reformulation of the linear programming fluid model approximation of the dynamic problem. We project the full-dimensional fluid approximation onto the lower-dimensional space of relocation decisions only. This projection results in a characterization of the problem as n+1 linear programs, where n is the number of nodes in the network. The reformulation uncovers structural properties that are interpretable using absorbing Markov chain concepts and allows us to write the gradient with respect to the relocation decisions in closed form. Our policy exploits these gradients to make dynamic car relocation decisions. We provide extensive numerical results on hundreds of random networks where our dynamic car relocation policy consistently outperforms the standard static policy. Our policy reduces the optimality gap in steady-state by more than 23\% on average. Also, in a short-term, time-varying setting, the lookahead version of our dynamic policy outperforms the static lookahead policy on average to a greater degree than that observed in the time-homogeneous tests.

Recommended citation: Hosseini, M., Milner, J., & Romero, G. (2021). Dynamic relocations in car-sharing networks. Rotman School of Management Working Paper, (3774324). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3774324

Diverse Assortments in Online Recommendations

Published in Manuscript in Preparation, 2022

This paper studies the business case for incorporating diverse content in recommendation systems. As online platforms and marketplaces have grown in recent years, they have increasingly relied on recommendation and ranking algorithms. These algorithms promote popular content, leading to a “rich get richer” effect. On the surface, this appears to be a favorable outcome for the platform as it would improve user engagement in the short term; however, undermining diverse (and new) items may affect the long-term health of the marketplace. In this paper, we make a connection between customer engagement and satiation. Satiation examines how past consumption patterns determine consumers’ willingness to pay for goods/services. In this setting, we find conditions on parameters under which the optimal policy always alternates and favors a more diverse recommendation. Under less restrictive conditions, the optimal policy favors diversity in earlier periods, and after some time, the optimal policy keeps offering the product with the largest value. This implies that a more diverse recommendation set is preferred over longer time horizons. Moroever, we show that we have a satiation threshold policy. This threshold assigns more area to the myopically better action as time passes and we get closer to the end of the horizon.

Recommended citation: Hosseini, M., Baron, O., Sekar, Sh., Malekian, A.

talks

teaching

Teaching Assistant, Rotman School of Management

Instructed the discussion classes. Designed and graded assignments and projects., Rotman School of Management, University of Toronto, 1900

  • Project Coach, Rotman Master of Management Analytics
    Guide student teams working on real-world analytical problems in collaboration with industry partners:
    • Understanding the managerial problem and developing the analytical model
    • Designing an analytical data set and developing a model
    • Deriving data-based insights and presenting solutions to the managerial problem
      Host Companies: Canadian Tire, Unity Health, Veeva, Government of Ontario, Teranet, Scotia Bank, TD, BMO
  • Other courses
    • RSM8414 (MMA) – Tools for Probabilistic Models and Prescriptive Analytics, Fall 2021
    • RSM8423 (MMA) – Optimizing Supply Chain Management and Logistics, Spring 2021
    • RSM2408 (MBA) – Modeling and Optimization for Decision Making, Fall 2020, Fall 2021
    • RSM2405 (MBA) – Supply Chain Management, Fall 2021
    • RSM2406 (MBA) – Operations Management Strategy, Winter 2021
    • RSM 270 – Operations Management, From Fall 2018 to present.

Teaching Assistant, Management and Economics Department

Instructed the discussion classes. Designed and graded assignments and projects., Management and Economics Department, Sharif University of Technology, 2013

  • MBA - Guest Lecturer: Project Management,Spring and Fall 2014
  • MBA - Management Decision Models, Spring 2013- Spring 2015
  • MBA - Operation Strategy, Fall 2013
  • MBA - Game Theory for Managers, Fall 2015