arXiv subjects

Hossein Hashemi Doulabi

Publications and source records attributed to Hossein Hashemi Doulabi.

4 recordsLinked to original sources

arXiv2026

Stochastic Appointment Scheduling with Patient-and-Time-Dependent Probability Distributions

Outpatient appointment scheduling must balance provider idle time, overtime, and patient waiting under uncertain service times, punctuality, and attendance. These factors may follow patient-and-time-dependent probability distributions, which existing models rarely incorporate simultaneously because of modeling and computational complexity. We propose a stochastic programming model that captures exponentially many scenarios with polynomially many variables and constraints, without sampling. It accommodates patient-dependent service times and patient-and-time-dependent no-shows and arrival times. We also discuss estimating these distributions from electronic health records. We examine how personalized reminders and changes in attendance due to incentives or negative clinic experiences affect scheduling. Our model optimally solves instances of up to 14 patients in reasonable computational time. Its schedules outperform classical Bailey-type rules. Even the best-performing variant has 97% higher expected cost on average. Incorporating patient-dependent service times reduces total costs by 34% on average. Accounting for patient-and-time-dependent unpunctuality and no-shows reduces costs by 12% and 67%, respectively. Personalized reminders can reduce costs by 23%, and sensitivity analyses show that these gains withstand moderate errors in estimated distributions. These findings show how modeling individual patient behavior and tailoring communication can reduce waiting, idle time, and overtime.

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arXiv2026

Drone-Aided Blood Collection Routing Problem: A Column Generation Approach

Platelet extraction requires whole blood to be processed within six hours of donation. To meet this deadline, blood collection organizations must optimally route a fleet of vehicles to pick up blood units from donation sites and deliver them to a processing center. This paper introduces a drone-aided blood collection routing problem in which a fleet of trucks, each equipped with a drone, operates in a synchronized manner to collect blood units before their processing time limit expires. Each truck-drone tandem can perform multiple trips throughout the planning horizon, allowing donation sites to be visited repeatedly as new blood units become available over time. We formulate this problem as a mixed-integer linear program that jointly optimizes the routing of trucks and drones, pickup schedules, and timing decisions to maximize the total number of viable blood units collected. We also develop a column generation approach that decomposes the problem into a master problem to select the optimal set of truck-drone tours and a pricing subproblem, which is solved using a tailored memetic algorithm to generate promising new columns. Through a comprehensive computational study, we show the operational benefits of integrating drones into the blood collection system. In addition, we demonstrate the superior performance of the proposed algorithm over Gurobi and two metaheuristics from the literature, namely the hybrid genetic algorithm and the invasive weed optimization, in both the drone-aided and truck-only settings.

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arXiv2026

A Reinforcement-learning-based Column Generation Algorithm for Integrated Operating Room Planning and Scheduling

In this paper, we propose a novel mixed integer programming model to formulate integrated operating room planning and scheduling problems, where several mandatory and elective surgeries are to be assigned and scheduled in operating rooms on different days. We consider both overtime in operating rooms and surgeons' daily availability limits. We propose a column generation (CG) algorithm to solve large-scale instances. In order to enhance the CG, we integrate the Reinforcement Learning Algorithm and the Genetic Algorithm and develop a hybrid algorithm to generate initial columns for the CG algorithm. For our analysis, we employed two sets of test instances: one consisting of synthetic data and the other based on real-world cases from a local hospital in Naples, Italy. Computational experiments demonstrate that our proposed model and methodology yields an average optimality gap of 1.23% for synthetic instances and 1.49% on real-world scenarios, significantly outperforming previous solution methodologies in the literature. Additionally, we demonstrate that the developed CG algorithm provides a high-quality solution for large-scale instances where other models and methods fail to obtain even a feasible solution. To further evaluate robustness under uncertainty, we examined scenarios with 20% variability in surgery durations. The results indicate that incorporating a 120-minute buffer time minimizes the overall cost. Moreover, we investigated the impact of emergency surgeries by either introducing additional cases or escalating surgical priorities. For synthetic instances, the inclusion of emergency surgeries increased the total rescheduling cost by 4.13%, whereas in the real-world Naples cases, priority escalation led to only a 0.11% increase.

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arXiv2026

Operating room planning with pooling downstream beds among specialties: A stochastic programming approach

In this paper, we study pooling downstream beds across specialties in a stochastic operating room planning problem. The main sources of uncertainty are stochastic surgical durations and patients' lengths of stay. We developed a two-stage stochastic programming model where in the first stage we decide on 1) the number of non-shared ICU and ward beds to be allocated to each specialty, and 2) the allocation of surgeries to operating rooms during the planning horizon. In the second stage, we decide on 1) the number of shared beds in ICU and wards to be allocated to different specialties on each day during the planning horizon, 2) the surge capacity required to satisfy downstream service to patients, and 3) the overtime incurred in operating rooms. The proposed model aims at minimizing the total cost including the patients' waiting cost, postponement cost, overtime and fixed cost of operating rooms, and the cost of downstream surge capacity. We have implemented the proposed stochastic programming model in a sample average approximation framework. To enhance the efficiency of sample average approximation, we have developed a specialized algorithm that quickly solves the second-stage model for any given first-stage solution for a large number of scenarios. We have carried out extensive computational experiments to evaluate the effectiveness of several pooling policies for downstream beds and also the efficiency of the proposed sample average approximation algorithm. Moreover, we have performed an extensive sensitivity analysis of cost and stochastic parameters. Our results demonstrated that a full-sharing policy among different specialties in the downstream units enhance the functionality of the system by up to 19.53%. Moreover, the results indicated that the solutions obtained by the proposed stochastic model outperform those from the corresponding deterministic problem by 17.43% on average.

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