3 Presentations

• 
  01:30 PM - 01:55 PM

  Home Health Care Delivery with Consistency Consideration in a Stochastic Environment

  • Seyede Saeede Hosseini, presenter, Polytechnique Montréal
  • Yossiri Adulyasak, HEC Montréal
  • Louis-Martin Rousseau, Polytechnique Montreal & CIRRELT

  We study the coordination of multi-period assignment, routing, and scheduling of home health care workers, ensuring consistency in personnel and visit times assignment for each patient. We integrate stochastic travel and service times into the deterministic model to enhance service quality and employ a chance-constraint approach. This study addresses the stochastic optimization model of the home health care routing and scheduling problem (HHCRSP) presented in the literature. Our methodology comprises two representations: a scenario-based and an extreme value theory-based (ETV-based) approximation. Our formulations include a master problem (MP) and a set of subproblems (SPs). We implement branch-and-check (B&Ch) to optimally solve practical-sized instances, where MP is solved by mixed integer linear programming, and SPs are solved by constraint programming. Compared to the deterministic model, our experimental results demonstrate a significant improvement in service level for scenario-based and EVT-based models while the average optimal value is generally not much lower than the deterministic model.

• 
  01:55 PM - 02:20 PM

  Real-time demand-driven inventory management in a hospital pharmacy

  • Ali Jafari, presenter, Polytechnique Montreal
  • Antoine Legrain, Polytechnique Montréal
  • Nadia Lahrichi, Polytechnique Montréal

  Hospital pharmacies receive thousands of prescriptions per day, consequently, it is crucial that the hospital medication circuit be as optimal as possible. Nevertheless, hospital inventory management is highly complex for various reasons, including space limitations, demand uncertainty, and human resource limitations. Furthermore, the inventory policy must be adaptable to real-time fluctuations in patient demands, with a simultaneous emphasis on preventing backorders and stockouts, given their potential to harm patients' well-being. Additionally, it is not operationally effective for care units (CUs) to repeatedly request new supplies every day to avert situations of insufficient stock. To address this problem, we present a real-time inventory control model based on the characteristics of medication demand while considering uncertainty in demand and space limitations, with the main objective of minimizing CU replenishment frequency. First, we categorize the medications into two groups; fast-moving and slow-moving medications, then, we utilize a continuous and dynamic review inventory control policy for these categories respectively. For optimizing the inventory control parameters we propose a stochastic optimization model for each category. To efficiently solve the proposed inventory policy in real-time, we employ a receding-horizon control (RHC) strategy, where the models are solved iteratively over a predetermined time horizon. To validate the effectiveness of our proposed approach, we use a real-world inventory management setting for a hospital in Montréal and conduct a comparative analysis between the proposed model and the existing state of the inventory policy to demonstrate the advantages of this research.

• 
  02:20 PM - 02:45 PM

  A chance-constrained home healthcare districting and staff dimensioning problem

  • Erfaneh Nikzad, presenter, polytechnique montréal
  • Nadia Lahrichi, Polytechnique Montréal

  This study explores decision-making regarding districting and staff dimensioning in home healthcare systems under uncertainty. Its aim is to divide areas into smaller ones, factoring in caregiver hiring costs and workload balancing Various uncertainties in home healthcare planning, like annual patient numbers, travel, and service times, can impact parameters. Accounting for uncertainty aids in designing an efficient system. Here, we treat the annual patient number and service time as stochastic parameters, employing a chance constraint programming framework to address uncertainty. Furthermore, our model ensures caregiver satisfaction by limiting the probability of overtime. We propose a matheuristic algorithm to efficiently solve the proposed model efficiently.