15h30 - 15h55
A real-time replenishment policy for medicine supply chain in a hospital pharmacy
Hospital pharmacies receive thousands of prescriptions per day, so they must be able to meet
patients’ needs at all times. It is, therefore, essential that the medication circuit at the hospital
be as optimal as possible. Because of various aspects such as the life cycle of drugs, demand
uncertainty, and space limits, supply chain management in hospitals is extremely difficult. In
this research, the real-time inventory replenishment policy of a pharmacy in a general hospital
is investigated. The goal of this research is to determine the optimal minimum/maximum
replenishment policy. To address this problem, a mixed-integer linear programming optimization
model based on the care unit’s features is proposed and solved with the rolling-horizon algorithm
for a single care unit in the hospital.
15h55 - 16h20
Analyzing sustainability and resilience for COVID-19 testing
This study aims to analyze the efficiency of a mixed public-private system COVID-19 PCR testing laboratories. We developed a discrete-event simulation model that represents the flow of samples in the labs from specimen collection to reporting results. Different scenarios were then tested to see the potential resilience of laboratories to face different events during a pandemic. Scenarios include demand variation, different work organizations and disruptions. We use simulation to compare laboratories at different administrative levels (including private) and compare to benchmark laboratories.
16h20 - 16h45
Parking Incentive Allocation Problem in Ridesharing Systems
Ridesharing refers to an agreement within a group of people having similar schedules as well as itineraries, and willing to travel together so as to reduce the commuting cost of the participants. In this paper, we study how to incentivize drivers to participate in ridesharing systems using parking spaces. To this end, we develop a Parking Incentive Allocation (PIA) problem to promote and distribute parking spaces to ridesharing drivers in a stochastic and dynamic environment. The optimization problem at each period is formulated by a multi-stage stochastic (MSS) program. To overcome the complexity of the model, we propose two approximation models, the Two-Stage Stochastic (TSS) and the Expected Value (EV) model, for the MSS program. We evaluate the effectiveness of the approximations on the data generated from trips GPS information collected in the MTL Trajet project of Montreal city. The computational results indicate that the TSS model is more effective than the EV model in achieving objective function, promoting and allocating spaces while the EV model generates fewer cancellations than the TSS model does.
16h45 - 17h10
Simulation of Volunteer Transportation of Breast Cancer Patients During Their Treatments
Transportation to treatment centers is a major concern for cancer patients. Besides the costs and accessibility, patients need a certain degree of comfort in their transport process. The objective of this research is to analyze various aspects of implementing a volunteer ride service for patients in an organization located in Montreal area. Therefore, a simulation model is created, and strategic and operational scenarios are tested which include one by one or multiple services, driver schedules, number of patients, and return requests. The results provide a decision support tool for the organization to analyze the capacity and service level in possible situations and choose the best strategy.