كنفرانس بين المللی مهندسی صنايع و سيستم ها

صفحه اصلی / دوازدهمین كنفرانس بين المللی مهندسی صنايع و سيستم ها

Closed-Loop Supply Chain Optimization in Kidney Dialysis Units: A Two-Stage Stochastic MILP Approach with ICU Emergency Demand

نویسندگان :

Saba Shahbaz¹ Armin Saadatpour² Javad Nematian³

1- دانشگاه آزاد اسلامی تهران مرکز 2- دانشگاه صنعتی اصفهان 3- دانشگاه سراسری تبریز

کلمات کلیدی :

closed loop supply chain،repairable inventory،mixed integer programming،two stage stochastic programming،lexicographic optimization،ICU resource sharing،hemodialysis،healthcare operations

چکیده :

As dialysis demand continues to grow, hospital hemodialysis units face increasing pressure to manage a fixed, breakdown-prone fleet of machines alongside routine patient scheduling and intensive-care emergencies. Existing scheduling approaches typically assume machines are always available, leaving machine breakdown, repair, and spare-parts procurement outside the scope of the scheduling decision itself. This paper proposes a two-stage stochastic Mixed-Integer Linear Program that jointly optimizes spare-parts procurement, patient scheduling, and nurse staffing under uncertain machine breakdowns and ICU emergency demand, modeled as a closed-loop supply chain in which spare parts ordered from an external supplier return broken machines to service. The model applies a two-tier objective, minimizing patient delay before nurse overtime, and includes a mandatory dispatch requirement covering immobile ICU patients. A case study of the hemodialysis facility at Imam Reza Hospital, Tabriz, Iran, comprising three units (OMID, EHSAN, MEHR) with 120 machines and dedicated infection-control isolation capacity, is used to illustrate the model. The proposed model is solved using CPLEX. Results demonstrate that the proposed model minimizes patient delay to its lowest achievable level under high-breakdown conditions, effectively balances nurse workload through optimized staffing allocation, and enables proactive spare-parts ordering that anticipates breakdowns before they occur rather than reacting after the fact. The model further quantifies the operational cost of guaranteeing mandatory ICU emergency coverage, providing hospital decision-makers with a clear, data-driven basis for balancing patient care priorities across scheduling and staffing decisions. This research contributes to the broader goal of applying closed-loop supply chain optimization to healthcare equipment management, extending prior work beyond scheduling under fixed capacity assumptions.