Browsing by Author "Saure, Antoine"

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    An approximate dynamic programming approach to network-based scheduling of chemotherapy treatment sessions
    (2024) Wenzel, Arturo; Saure, Antoine; Cataldo, Alejandro; Rey, Pablo A.; Sanchez, Cesar
    A solution approach is proposed for the interday problem of assigning chemotherapy sessions at a network of treatment centres with the goal of increasing the cost-efficiency of system-wide capacity use. This network-based scheduling procedure is subject to the condition that both the first and last sessions of a patient's treatment protocol are administered at the same centre the patient is referred to by their oncologist. All intermediate sessions may be administered at other centres. It provides a systematic way of identifying effective multi-appointment scheduling policies that exploit the total capacity of a networked system, allowing patients to be treated at centres other than their home centre. The problem is modelled as a Markov decision process which is then solved approximately using techniques of approximate dynamic programming. The benefits of the approach are evaluated and compared through simulation with the existing manual scheduling procedures at two treatment centres in Santiago, Chile. The results suggest that the approach would obtain a 20% reduction in operating costs for the whole system and cut existing first-session waiting times by half. A key conclusion, however, is that a network-based scheduling procedure brings no real benefits if it is not implemented in conjunction with a proactive assignment policy like the one proposed in this paper.
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    An integer programming approach to curriculum-based examination timetabling
    (2017) Cataldo Cornejo, Alejandro; Ferrer Ortiz, Juan Carlos; Miranda, Jaime; Rey, Pablo A.; Saure, Antoine
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    Design of a single window system for e-government services: the Chilean case
    (2018) Cataldo Cornejo, Alejandro; Ferrer Ortiz, Juan Carlos; Rey, Pablo A.; Saure, Antoine
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    Identifying proactive ICU patient admission, transfer and diversion policies in a public-private hospital network
    (2021) Marquinez, Jose Tomas; Saure, Antoine; Cataldo, Alejandro; Ferrer, Juan-Carlos
    Management of hospital beds is a high-impact issue for two-tier healthcare systems, due principally to their critical importance and high costs. Bed capacity in the public sector is generally insufficient to provide immediate care to all critical patients and thus a significant proportion of public expenditure is assigned to the diversion of patients for treatment in the private sector. We formulate and approximately solve a discounted infinite-horizon Markov Decision Process (MDP) that seeks to identify cost-effective policies for transferring ICU patients between hospitals or diverting them to private clinics. The solution approach employs an affine architecture for approximating the value function of the MDP model and solves the equivalent linear programming model using column generation. The approach can handle a high level of dimensionality, enabling it to consider the arriving patients' many different diagnostic groups and their corresponding lengths of stay. The decisions generated through this approach often differ from the intuitive ones produced in a typical day-by-day decision process, that does not consider the impact of the current day's decisions on the future performance of the system. In particular, the resulting policies will in many cases proactively transfer patients to a different public facility or divert them to a private one even though the hospital they first arrived at had beds available. The performance of the proposed method was evaluated by simulating a case study based on data from a hospital network in Santiago, Chile, producing savings of almost 49% due mostly to reduced usage of private services.
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    Multi-stage process for chemotherapy scheduling and effective capacity determination
    (2023) Cataldo, Alejandro; Sufan, Sebastian; Lorca, Alvaro; Andresen, Max; Sanchez, Cesar; Saure, Antoine
    A novel solution approach is developed for the scheduling of chemotherapy sessions at cancer treatment centers. The problem is divided into two subproblems determining the day (interday scheduling) and the time slots (intraday scheduling), respectively. The interday subproblem is solved by a model that allows for effective treatment center capacity choices while the intraday subproblem is addressed using two optimization models. New patient arrivals and treatment protocols specifying the latest starting date and session spacing are sources of uncertainty. Unlike other existing approaches, the proposed method incorporates the concept of effective treatment capacity which facilitates the interaction between the interday and intraday subproblems allowing them to be solved sequentially and iteratively to thus achieve much more resource-efficient solutions. A case study using real data from a Chilean cancer center to conduct comparative simulations of its manual scheduling methods and the proposed methodology found that the latter almost always performed better, often significantly so, on makespan, resource utilization, overtime, and patient diversion metrics.