4 présentations

• 
  10h30 - 10h55

  Surgical Scheduling with Constrained Patient Waiting Times

  • Yun Zhou, prés., McMaster University
  • Mahmut Parlar, McMaster University
  • Vedat Verter, Queen's University
  • Shannon Fraser, McGill University

  We consider a surgical case scheduling problem with the objective of minimizing the expected time span of the schedule subject to a pre-specified upper bound on the expected waiting time for each patient. We derive an optimality bound for a simple sequencing rule that sorts the surgical cases in the ascending order of the surgical duration variability.
• 
  10h55 - 11h20

  CANCELED : Estimating causal effects with optimization-based methods: A review and empirical comparison

  • Martin Cousineau, prés., HEC
  • Vedat Verter, Queen's University
  • Susan A. Murphy, Harvard University
  • Joelle Pineau, McGill University

  In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given. To achieve this balance, there exist a wide variety of methods. In particular, several methods based on optimization models have been recently proposed in the causal inference literature. While these optimization-based methods empirically showed an improvement over a limited number of other causal inference methods in their relative ability to balance the distributions of covariates and to estimate causal effects, they have not been thoroughly compared to each other and to other noteworthy causal inference methods. In addition, we believe that there exist several unaddressed opportunities that operational researchers could contribute with their advanced knowledge of optimization, for the benefits of the applied researchers that use causal inference tools. In this talk, we present an overview of the causal inference literature and describe in more detail the optimization-based causal inference methods, provide a comparative analysis of the prevailing optimization-based methods, and discuss opportunities for new methods.
• 
  11h20 - 11h45

  The Impact of Strategically Booking Multiple Appointments on Hospital Operation and Patient Outcomes

  • Opher Baron, prés., Rotman School of Management, University of Toronto
  • Olga Bountali, Rotman School of Management
  • ARSENIY GORBUSHIN, Rotman School of Management
  • Binyamin Oz, Hebrew University of Jerusalem

  During the COVID-19 vaccination process, a significant mass of patients booked double or even multiple appointments for their vaccines with the hope of getting treated faster. This led to thousands of unfulfilled appointments in many cities both in Canada and worldwide, left capacity highly underutilized, and hindered the efficiency of the vaccination process at a very crucial period. This strategic double-booking and the resulting no-shows have also been encountered in the context of patients who are trying to book surgery or appointments with specialists and imaging or even in ride-hailing applications and private emergency care in developing countries. We introduce a queuing model with strategic patients to model the single- vs. double-booking decisions and examine their impact on both system performance and patient outcomes. We compare our model with a benchmark representative of transparency, i.e., a model where a central mechanism only allows patients to single book and quantify the corresponding loss/gain induced by double-booking. Further, we explore potential interventions for central planners and policymakers to mitigate the negative effect of double-booking.
• 
  11h45 - 12h10

  Model development and validation for the call centre of a non-profit organization

  • Arash Asgari, prés.,
  • Saied Samiedaluie, University of Alberta, Alberta School of Business
  • Armann Ingolfsson, University of Alberta, Alberta School of Business

  We study the operations of a call centre for a non-profit organization, in which the on-duty agents might become unavailable (at times that are not recorded) because of after-call work, meetings, or breaks. Our goal is to develop a model that reproduces the empirically observed average wait times and abandonment proportions for this call centre. First, we investigated the Erlang-S model, which allows for variability in the agents’ availability by expanding the parameter set of the commonly used Erlang-A model. The Erlang-S model outputs were closer than the Erlang-A model outputs to the empirical outputs. We obtain more valid results by extending the Erlang-S model to allow an agent’s unavailability period to have two phases: a short period (e.g., due to wrap-up) followed by a longer period (e.g., going on a break). We use a customized expectation-maximization (EM) algorithm to estimate the model parameters. The customized algorithm incorporates new formulas, which significantly reduce computation time and memory requirements.