A multiobjective ϵ-constraint based approach for the robust master surgical schedule under multiple uncertainties

International audienceThe efficient scheduling of elective surgeries in hospitals is critical for ensuring patient satisfaction, cost-effectiveness, and overall operational efficiency. However, operating theater (OT) managers face complex and competing scheduling problems due to numerous sources of uncertainty and the impact of the proposed schedule on downstream recovery units, such as the intensive care unit (ICU). To address these challenges, this study develops a multiobjective robust planning model for the weekly Master Surgical Schedule (MSS) under multiple uncertainties. The model takes into account patient priority, assignment cost and workload balancing, while also considering the constraints of the OT, surgeon availabilities, downstream resources, and the uncertainty of surgery duration and patients’ length of stay (LOS) in the ICU. To evaluate the robust solutions, a Monte Carlo simulation is used to calculate the risk of constraint violations, and an adapted -constraint algorithm is used for the four-objective problem to compute the Pareto front and calculate the hypervolume for every degree of uncertainty. This provides a comprehensive decision tool for OT decision makers and allows for the comparison of various scenarios in terms of the number of scheduled patients, canceled patients, and the utilization rate of the OT

Towards a Robust Multiobjective Master Surgical Schedule under Multiple Uncertainty

International audiencePatient scheduling within the operating room (OR) is subject to several forms of uncertainties related to the state of health of the patients and also to the availability of resources. Surgery duration and postoperative length-of-stay (LOS) in downstream recovery units, namely the intensive care unit (ICU) and the post-surgery units are two of the most significant patient-related sources of disturbance to the schedule. Elective patients undergo the surgery, then go to the post-surgery units. However, some patients must stay for one night or more in the ICU beds before being transferred to the post-surgery unit. Several existing works focus only on the upstream scheduling that concerns only the OR planning, which yields infeasible and sub-optimal schedules. On the other hand, the limited literature that handles the postoperative resources assumes that surgery duration and patients' LOS follow a well-known distribution. Unfortunately, it is challenging to use distributions to deduce surgery duration and LOS as is the case in stochastic approaches for medium and short-term planning. Consequently, we propose a new methodology for tactical planning using robust optimization. As stated in Shehadeh and Padman (2022), some research papers assume that the OR manager has a unique objective to satisfy. In reality, the OR can be managed by a group of decision-makers that strive to satisfy conflicting objectives. This work focuses on building a robust master surgical schedule (MSS) for tactical planning. To do this, we assign elective patients to available ORs under the block scheduling strategy. Our approach considers multiple conflicting objectives: patient priority, assignment cost, and workload balancing. We consider several OR restrictions: OR resources, surgeons' and downstream resources availability while incorporating uncertainty in surgery duration and patients' LOS in the ICU

A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services

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A Robust Home Health Care Scheduling and Routing Approach with Time Windows and Synchronization Constraints Under Travel Time and Service Time Uncertainty

Part 4: Lean and Six Sigma in Services HealthcareInternational audienceHome health care (HHC) services represent a set of medical services given to patients at their homes. The patients require a set of care that must be coordinated and treated by skilled caregivers corresponding to their needs. This study proposes an HHC routing and assignment approach based on a mixed-integer linear programming model that aims to minimize total route cost. The HHC approach takes into account a set of HHC specific constraints and criteria. Secondly, we propose a new robust counterpart HHC model under uncertainty based on the well-known budgeted uncertainty set. The robust counterpart HHC model deals with travel and service times uncertainty. The computational results compare the deterministic model with its robust counterpart model. The small and medium instances have been solved using TSP benchmarks with specific data concerning HHC problems. The models have been implemented using ILOG CPLEX Optimization Studio. The computational results of small and medium instances indicated the efficiency of the proposed approach. Robustness analysis of the obtained results was conducted using a Monte Carlo simulation and indicated the price of robustness. The increase of route cost in comparison with the risk of infeasibility shows the importance of the designed robust routes for HHC routing and scheduling problems

A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources

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Metaheuristics: Proceedings of 15th Metaheuristics International Conference, MIC 2024

International audienceThis volume constitutes the refereed proceedings of Metaheuristics on 15th International Conference, MIC 2024, held in Lorient, France, during June 4–7, 2024.The 36 full papers presented together with 34 short papers were carefully reviewed and selected from 127 submissions. The conference focuses on artificial intelligence, combinatorial optimization, computer science, graph theory, evolutionary algorithms, genetic algorithms, simulated annealing, optimization, optimization problems

A Multi-objective Approach for the Combined Master Surgical Schedule and Surgical Case Assignment Problems

International audienceIn this study, we propose a multi-objective approach to address the Master Surgical Schedule (MSS) and the Surgical Case Assignment (SCAP) problems for the common Operating Theater (OT) in an integrated hospital facility. The approach accounts for both surgeons' and Operating Rooms' (OR) availability and restrictions. We propose in this paper a multi-objective programming model that supports OT decision making to ensure patients' and surgeons' satisfaction and hospital quality of service by respecting surgeries due dates and balancing surgeons' workloads. The proposed approach determines the surgical discipline to perform on each session, the surgical cases assigned on each session, and the operations' start time on a weekly basis. The-constraint method is used to solve the multi-objective problem. The computational experiments are performed using ILOG CPLEX optimization studio. The model is tested on the empirical data archives of a medium-size French hospital and show the quality of solutions achieved using the proposed approach

A Three-stage Approach for the Multi-period Green Home Health Care Problem with Varying Speed Constraints

International audienceIn this paper, we address an environment-friendly approach to solve the Green Home Health Care (GHHC) routing and assignment problem. The approach aims to reduce fuel consumption to minimize carbon-dioxide emissions. The Home Health Care (HHC) routing and assignment problem is a very complex problem due to the multifactors and criteria that must be taken into consideration during the planning. The main of this work is to find an efficient working plan on a weekly basis, respecting continuity of care, with environmental concern, which ensures the satisfaction of both patients and caregivers and considers most of HHC constraints. The proposed approach is a threestage methodology; the first stage is a multi-objective programming model that minimizes the total traveled distance and fuel consumption with varying speed. In the second stage, we introduce an assignment heuristic, and the third stage is an Integer Linear Programming (ILP) model that aims to balance caregivers' workload. The small and medium instances are solved using CPLEX optimizer studio and show that better route planning and minimum fuel consumption is achieved with varying speed concept