Integrated Oncologist Consultation And Chemotherapy Appointment Coordination Under Uncertainty In Outpatient Chemotherapy Clinics

Official URL: https://risc01.sabanciuniv.edu/record=b3205832

The scheduling of chemotherapy treatments in outpatient chemotherapy clinics poses significant challenges due to limited resources, uncertainty in infusion durations, and the critical nature of cancer treatment. This study addresses these challenges through integrating oncologist consultation and chemotherapy scheduling by coordinating oncologist appointment times and chemotherapy treatment start times for a daily list of patients. A two-stage stochastic mixed-integer programming model is developed, considering continuous time frames for appointments and stochastic factors such as infusion times and the status of chemotherapy treatment approval. The first stage of the model arranges patients in a sequence based on their designated oncologists, while the second stage assigns patients to chairs and nurses. The objective function penalizes the expected weighted sum of the closing time of the chemotherapy clinic and patient waiting times. To reduce problem complexity, a scenario reduction algorithm is applied to the original scenario set prior to optimization. The proposed method, a Wasserstein Distance-Based Local Search Algorithm (WDB-LSA), is tested using real data obtained from a major academic oncology hospital in Turkey. The algorithm is compared with several practical heuristics from the literature using a commercial solver. The results demonstrate the effectiveness and computational efficiency of WDB-LSA in optimizing chemotherapy scheduling in outpatient clinics, taking into account multiple uncertainties and limitations. The impact of varying model parameters was assessed under sensitivity analysis, and the solution methodology was tested against the mean value solution in order to estimate the value of the stochastic solution.