3 présentations

• 
  14h45 - 15h07

  Online assignment-routing for organ delivery

  • Sean Lo, prés., Massachusetts Institute of Technology
  • Alexandre Jacquillat, Massachusetts Institute of Technology

  The US organ transplantation system has been experiencing declining utilization rates, resulting in a smaller fraction of donated organs being transplanted into recipients. One potential solution lies in portable devices that preserve the shelf-life of donated organs, allowing for less time-dependent organ donor-recipient matchings and improved healthcare outcomes. We partner with TransMedics, a US-based healthcare startup that develops and operates such devices for organ transplants. The operations of these devices features a complex logistics problem with a hybrid assignment / routing structure and an online optimization structure. We develop an optimization model that assigns surgeons, technicians, and devices to organ donors, and routes them to organ recipients via a fleet of planes. We propose an online algorithm which takes into account uncertainty in case arrivals, and demonstrate benefits over computational and practical benchmarks. Real-world experiments suggest improvements in the volume of transplants.

• 
  15h07 - 15h29

  Relay-Hub Network Design for Consolidation Planning Under Demand Variability

  • Onkar Kulkarni, Georgia Institute of Technology
  • Mathieu Dahan, prés., Georgia Institute of Technology
  • Benoit Montreuil, Georgia Institute of Technology

  We study the problem of designing large-scale relay logistics hub networks resilient to demand variability. We formulate a two-stage stochastic optimization model that integrates second-stage tactical consolidation decisions with first-stage strategic hub location and capacity planning. To solve this problem exactly, we develop a branch-and-cut algorithm with nested Benders decomposition and integer L-shaped cuts. Our three-stage approach decomposes the problem twice: across the stochastic demand scenarios and across origin-destination pairs within each scenario, resulting in network flow and shortest path subproblems. We validate our methodology by designing relay networks for finished vehicle deliveries in partnership with a U.S.-based car manufacturer. Computational experiments show that our approach efficiently generates near-optimal solutions for large-scale instances using sample average approximation. The resulting logistics networks showcase a significant improvement in resilience, achieving an 8% reduction in average delivery costs compared to designs based on deterministic demand and continuously approximated routing.

• 
  15h29 - 15h51

  Decomposition Algorithms for Dynamic Capacitated Multiple Allocation Hub Location under Demand Uncertainty

  • Ziyuan Sun, prés., Concordia University
  • Claudio Contardo, Concordia University
  • Jean-François Cordeau, HEC Montréal, GERAD, CIRRELT

  This work investigates dynamic (multi-period) capacitated multiple allocation hub location problems under demand uncertainty. We first present a two-stage stochastic mixed-integer programming formulation (MIP) for the problem. Given the NP-hard nature of the problem and the difficulty of solving this MIP with general-purpose solvers for large-scale instances, we address it by means of Benders decomposition. Due to the distinctive feature of the model, involving a large number of integer variables for the modular capacities of hubs in the Benders primal subproblem, linear programming duality theory cannot be applied directly to generate Benders cuts. We propose a heuristic Benders decomposition algorithm combined with combinatorial Benders cuts to ensure that a high-quality, feasible integer solution can be found. Additionally, acceleration techniques for solving the Benders master problem and subproblem, as well as selecting strong cuts based on minimal infeasible subsystems, are introduced.