Optimization Days 2019

HEC Montréal, May 13-15, 2019


HEC Montréal, 13 — 15 May 2019

Schedule Authors My Schedule

MD5 Healthcare Optimization II

May 13, 2019 03:30 PM – 05:10 PM

Location: Hélène-Desmarais

4 Presentations

  • 03:30 PM - 03:55 PM

    The team orienteering problem with 2D time-varying profit in the context of rescue operations.

    • Yu Qinxiao, presenter,
    • Yossiri Adulyasak, HEC Montréal
    • Louis-Martin Rousseau, Polytechnique Montréal
    • Ning Zhu,
    • Shoufeng Ma,

    This paper addresses the search and rescue (SAR) team routing and scheduling problem in the response phase of post-disaster circumstances, where numerous sites are destroyed and people are trapped in the affected area, but the number of SAR team is limited. We take the fact that the arrival time and service time effect the success of rescuing survivors into account, then model this problem as a team orienteering problem with time-varying profit. The objective is to maximize the number of survivors in the limited time by identifying a set of routes among candidate sites as well as deciding the duration of service time at each visited site. We formulate a mixed integer nonconvex programming model (MINLP) for the problem, and propose a Benders branch-and-cut algorithm and a hybrid heuristic to solve the problem with small size and large size, respectively. Computational experiments show that the proposed exact method is capable of solving instances where MINLP solver fails in finding the optimal solution within 2 hours, and the proposed heuristic could be quite effective in finding good quality solutions in a reasonable time.

  • 03:55 PM - 04:20 PM

    Optimization of appointment grid and technologist scheduling

    • Dina Ben Tayeb, presenter, Polytechnique Montréal
    • Nadia Lahrichi, Polytechnique Montréal
    • Louis-Martin Rousseau, Polytechnique Montréal

    Our work concerns a simultaneous optimization of appointment grid and technologist scheduling in a radiology center. We develop a mixed integer programming model that provides
    an optimal allocation of personal resources to maximize the machine utilization and the number of treated patients. We evaluate the optimization model using a real case of the Magnetic Resonance Imaging in the CHUM radiology department.

  • 04:20 PM - 04:45 PM

    A case study of Montreal emergency medical services using discrete-event simulation

    • Gabriel Lavoie, presenter, Polytechnique
    • Nadia Lahrichi, Polytechnique Montréal
    • Valérie Bélanger, CIRRELT, HEC Montréal

    Emergency medical services (EMS) provide pre-hospital care
    and transportation to hospitals following an emergency call. This
    article presents a simulation model of Urgences-sante, an EMS in
    Quebec. The model is validated using real data and is used to evaluate
    several scenarios aiming to improve the EMS performance.

  • 04:45 PM - 05:10 PM

    A learning tabu search algorithm to improve the patient flow by determining a physician schedule

    • Nazgol Niroumandrad, presenter, Polytechnique Montreal
    • Nadia Lahrichi, Polytechnique Montréal

    The period between the referral of a patient to a cancer center and the confirmation of the treatment plan is defined as the pretreatment phase which includes consultation with the physician, scan, treatment planning and finally the treatment. Physicians play a key role in this process and have been identified as bottlenecks since they must confirm each step. In this project, the goal is to construct a task schedule for physicians that improves the patient flow and shortens the pretreatment duration. We presented a MIP model for the problem and developed a tabu search algorithm, considering both deterministic and stochastic cases. We are improving the performance of tabu search by a learning mechanism. Experiments are conducted and show the benefit of using a learning mechanism under deterministic conditions.