About

David Simchi-Levi is the William Barton Rogers Professor in Energy and a Professor of Civil and Environmental Engineering and Engineering Systems at MIT. He also serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics. His research interests include statistical learning and decision making, transportation and logistics systems, supply chain management, revenue and yield management, optimization-based decision support systems, operations research, and flexibility and risk management. Professor Simchi-Levi has made significant contributions to the development and dissemination of innovative paradigms for risk identification and mitigation in global supply chains, and he has been recognized with numerous awards, including the 2020 INFORMS Impact Prize and election to the National Academy of Engineering in 2023. He has also served as editor-in-chief for leading journals such as Management Science and Operations Research, and has founded several companies specializing in supply chain optimization and analytics.

Research topics

• Computer Science
• Computer network
• Business
• Engineering
• Operations management
• Artificial Intelligence
• Data science
• Operations research
• Chemistry

Selected publications

• Two-stage Online Reusable Resource Allocation: Reservation, Overbooking and Confirmation Call

  arXiv (Cornell University) · 2024

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Business

  We study a two-stage online reusable resource allocation problem over T days involving advance reservations and walk-ins. Each day begins with a reservation stage (Stage I), where reservation requests arrive sequentially. When service starts (Stage II), both reserved and walk-in customers arrive to check in and occupy resources for several days. Reserved customers can cancel without penalty before or during a confirmation call initiated by the decision maker (DM) before day's end. The DM must immediately accept or reject each booking or check-in request, potentially overbooking by accepting more reservations than capacity. An overbooking loss occurs if a reserved customer's check-in is rejected in Stage II; a reward is obtained for each occupied resource unit daily. Our goal is to develop an online policy that controls bookings and check-ins to maximize total revenue over the T-day horizon. We show that due to cancellation uncertainties and complex correlations between occupancy durations, any online policy incurs a regret of Ω(T) compared to the offline optimal policy when the \textit{busy season} assumption does not hold. To address this, we introduce decoupled adaptive safety stocks, which use only single-day information to hedge against overbooking risks and reduce resource idling. Under the busy season condition, our policy decouples the overall offline optimal into single-day offline optimal policies. Consequently, the regret between our policy and the offline optimal decays exponentially with the time between the confirmation call and day's end, suggesting the DM can delay confirmation calls while maintaining near-optimal performance. We validate our algorithm through sythetic experiments and empirical data from an Algarve resort hotel.

  DOI

• Online Local False Discovery Rate Control: A Resource Allocation Approach

  SSRN Electronic Journal · 2024 · 2 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence
  DOI

• Two-Stage Online Reusable Resource Allocation: Reservation, Overbooking and Confirmation Call

  2024 · 3 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Operations management
  DOI

• Design and Analysis of Switchback Experiments

  Management Science · 2022 · 45 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Switchback experiments, where a firm sequentially exposes an experimental unit to random treatments, are among the most prevalent designs used in the technology sector, with applications ranging from ride-hailing platforms to online marketplaces. Although practitioners have widely adopted this technique, the derivation of the optimal design has been elusive, hindering practitioners from drawing valid causal conclusions with enough statistical power. We address this limitation by deriving the optimal design of switchback experiments under a range of different assumptions on the order of the carryover effect—the length of time a treatment persists in impacting the outcome. We cast the optimal experimental design problem as a minimax discrete optimization problem, identify the worst-case adversarial strategy, establish structural results, and solve the reduced problem via a continuous relaxation. For switchback experiments conducted under the optimal design, we provide two approaches for performing inference. The first provides exact randomization-based p-values, and the second uses a new finite population central limit theorem to conduct conservative hypothesis tests and build confidence intervals. We further provide theoretical results when the order of the carryover effect is misspecified and provide a data-driven procedure to identify the order of the carryover effect. We conduct extensive simulations to study the numerical performance and empirical properties of our results and conclude with practical suggestions. This paper was accepted by George Shanthikumar, big data analytics. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4583 .

  PublisherDOI

• Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-Dependent Competitive Ratios

  Operations Research · 2020 · 83 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization

  Resource Allocation and Pricing in the Absence of a Demand Forecast

  DOI

Frequent coauthors

• Ruicheng Ao

  3 shared

• H Fu

  2 shared

• Will Ma

  2 shared

• Hongyu Chen

  California State University, Long Beach

  1 shared

• Guillermo Gallego

  1 shared

• Elaheh Fata

  1 shared

• Feng Zhu

  1 shared

• Karthik Natarajan

  Vinayaka Missions University

  1 shared

Labs

• MIT Data Science LabPI

Awards & honors

• INFORMS Impact Prize (2020)
• INFORMS Fellow
• MSOM Distinguished Fellow
• INFORMS Koopman Award (2020)
• Ford Motor Company Engineering Excellence Award (2015)

Similar researchers at Massachusetts Institute of Technology

• Rudolf Jaenischh-295
• Robert A. Weinbergh-291
• Susumu Tonegawah-237
• James J. Collinsh-232
• Phillip A. Sharph-227