4 présentations

• 
  10h30 - 10h55

  Fix and Optimize approach for Integrated Healthcare Timetabling

  • Prakash Gawas, prés., Polytechnique Montreal
  • Camille Pinçon, Polytechnique Montréal
  • Nohaila Ahssinou, Polytechnique Montréal
  • Erfaneh Nikzad, Polytechnique Montréal

  The IHTC 2024 competition involves integrating three interconnected subproblems: Surgical Case Planning (SCP), Patient Room Assignment (PAS), and Nurse-to-Room Assignment (NRA). To tackle this highly complex problem, we adopt a two-step approach. First, we construct an initial feasible solution by sequentially solving each subproblem, ensuring both feasibility and computational efficiency. Next, we refine this solution using a Fix-and-Optimize (F&O) matheuristic, which iteratively enhances solution quality by selectively optimizing decision variables. The SCP phase focuses on scheduling feasibility while accounting for capacity constraints and workload balance. The PAS phase assigns patients to rooms based on gender and capacity constraints, employing two alternative strategies to maximize feasibility. The NRA phase ensures continuity of care and workload balance through a segmented planning horizon approach. Additionally, we apply a set of targeted destroyers that unfix parts of the feasible solution and then resolve specific subproblems to improve overall quality. Parallel computing is leveraged to accelerate solution generation and refinement. Our methodology is implemented using the JuMP library in Julia, with Gurobi as the optimization solver, running on multi-core architectures. This approach effectively balances feasibility, computational efficiency, and solution quality.

• 
  10h55 - 11h20

  2bin inventory optimization considering space constraint

  • Ali Jafari, prés., Polytechnique Montreal
  • Nadia Lahrichi, Polytechnique Montreal
  • Antoine Legrain, Polytechnique Montreal
  • Ly-Anne Lachapelle, CHUM

  The 2bin inventory system is widely used in hospitals to manage low-cost, high-volume medical supplies due to its simplicity and efficiency. Although previous research has explored determining optimal bin sizes under cycle service level (CSL) constraint, limited attention has been given to incorporating real-world operational constraints. This research presents an optimization model that integrates operational constraints such as storeroom capacity, supplier-imposed packaging sizes, CSL constraint, and the requirement to store related items together. We also derive a closed-form expression for calculating the expected inventory costs under periodic review and periodic review with deferred replenishment policies. Using real hospital data, we validate the proposed model through simulations, demonstrating its effectiveness in enhancing inventory management. The results show notable improvements in efficiency, including reductions in holding and ordering costs, fewer shortages, and lower staff workload. These findings highlight the advantages of using proposed policies for different classes of items.

• 
  11h20 - 11h45

  Réoptimisation progressive pour un problème d'affectation dans un contexte de soins à domicile

  • Auriane Peter--Hemon, prés., Polytechnique Montréal
  • Louis-Martin Rousseau, Polytechnique Montréal
  • Nadia Lahrichi, Polytechnique Montréal

  Le problème d’affectation dans un contexte de soins à domicile consiste à déterminer, pour une semaine, quel soignant ira voir quel patient quel jour et à quelle heure. Habituellement, une fois qu’un patient a été programmé, il n’est pas possible de modifier ses rendez-vous : il gardera le même soignant et le même horaire durant toute la durée de son traitement. Dans le cadre de ce travail, on s’autorise à modifier les rendez-vous de certains patients au fur et à mesure que de nouveaux patients arrivent afin de se rapprocher d’un scheduling optimal. Pour cela, on utilise des méthodes de résolution exactes.

• 
  11h45 - 12h10

  Scheduling Chemotherapy Drug Preparation: Minimizing Delays and Costs in Hospital Pharmacies

  • Camille Pinçon, prés., Polytechnique Montréal
  • Antoine Legrain, GERAD - Polytechnique Montréal
  • Nadia Lahrichi, Polytechnique Montréal

  Chemotherapy treatment relies on the preparation of dangerous and expensive drugs, making timely and accurate drug manufacturing crucial for patient care. To address the challenges faced by hospital pharmacies, we propose a multi-objective optimization model for scheduling chemotherapy drug production. Our method aims to minimize : the maximum delay in delivering treatments, the sum of all delays and reduce raw material costs, while considering human and material resource constraints. The approach relies on a Mixed-Integer Linear Programming model to approximate scheduling, followed by a reconstruction algorithm to generate practical schedules for pharmacists. Additionally, the model supports the selection of medications for early preparation, increasing the production buffer for subsequent days and further minimizing waste. Computational experiments, using real-world data from a regional cancer center, highlight the method's effectiveness in reducing delays, optimizing resource usage, and enhancing productivity. This scheduling framework contributes to a more efficient and cost-effective workflow in chemotherapy clinics, ultimately improving patient care and resource management.