2,252 research outputs found

    Operating Room Scheduling in Teaching Hospitals

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    Operating room scheduling is an important operational problem in most hospitals. In this paper, a novel mixed integer programming (MIP) model is presented for minimizing Cmax and operating room idle times in hospitals. Using this model, we can determine the allocation of resources including operating rooms, surgeons, and assistant surgeons to surgeries, moreover the sequence of surgeries within operating rooms and the start time of them. The main features of the model will include the chronologic curriculum plan for training residents and the real-life constraints to be observed in teaching hospitals. The proposed model is evaluated against some real-life problems, by comparing the schedule obtained from the model and the one currently developed by the hospital staff. Numerical results indicate the efficiency of the proposed model compared to the real-life hospital scheduling, and the gap evaluations for the instances show that the results are generally satisfactory

    Local search for the surgery admission planning problem

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    We present a model for the surgery admission planning problem, and a meta-heuristic algorithm for solving it. The problem involves assigning operating rooms and dates to a set of elective surgeries, as well as scheduling the surgeries of each day and room. Simultaneously, a schedule is created for each surgeon to avoid double bookings. The presented algorithm uses simple Relocate and Two-Exchange neighbourhoods, governed by an iterated local search framework. The problem's search space associated with these move operators is analysed for three typical fitness surfaces, representing different compromises between patient waiting time, surgeon overtime, and waiting time for children in the morning on the day of surgery. The analysis shows that for the same problem instances, the different objectives give fitness surfaces with quite different characteristics. We present computational results for a set of benchmarks that are based on the admission planning problem in a chosen Norwegian hospital

    Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment

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    Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital

    Operating theatre planning and scheduling in real-life settings.Problem analysis, models, and solution procedures

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    Falta palabras claveNowadays health care organizations experience an increasing pressure in order to provide their services at the lowest possible costs as a response to the combination of restrictive budgets, increasing waiting lists, and the aging of the population. In general, hospital resources are expensive and scarce, being the operating theatre the most critical and expensive resource. In most hospitals, the operating theatre is a complex system composed of operating rooms (ORs) together with their specialized equipment, preoperative and postoperative facilities and, finally, a diversity of human resources, including surgeons, anesthetists, nurses, etc. To handle such complexity, decisions related to operating theatre management are usually decomposed into three hierarchical decision levels, i.e.: strategic, tactical and operational. At the strategic level, hospital managers set the volume and the mix of surgeries that will be performed over a long-term horizon (typically, a year) to keep up acceptable size of waiting lists while achieving cost targets, thus making long-term decisions related to the dimensioning of surgical facilities (e.g. build new ORs, adding new recovery beds, etc.), the hiring of surgical staff (e.g. surgeons, nurses, etc.), the purchase of novel surgical devices, and the amount of operating theatre resources required by surgical specialties to perform their surgeries (OR time, number of beds, etc.). Once decisions at strategic level have been made, the operating theatre resources are allocated over a medium-term planning horizon (ranging from few weeks to 6 months) in the tactical level. Since the OR is both a bottleneck and the most expensive facility for most hospitals, surgical specialties are first assigned to OR days (i.e. a pair of an OR and a day) over the planning horizon, until the OR time allocated to each surgical specialty in the strategic level is reached. Then, the above assignment defines aggregate resource requirements for specialties, such as the demand of nurses, drugs, diagnostic procedures, laboratory tests, etc. Finally, the working shifts of human resources and their workload (e.g. the number of surgeries allocated to each surgeon) are defined over the medium-term planning horizon in order to achieve the volume of surgeries set by hospital managers. Finally, the surgical schedule is determined over a short-term planning horizon (ranging from few days to few weeks) at the operational level. The operational level is usually solved into two steps. The first step involves the determination of the date and the OR for a set of surgeries in the waiting list; while in the second step, a sequence of surgeries for each OR within each day in the planning horizon is obtained. Note that only a set of surgeries will be performed during the planning horizon due to capacity constraints (both facilities and human resources). The decomposition of the operational level into the two aforementioned steps intends to reduce the complexity of the resulting problem, although the quality of the so-obtained surgery schedule may be reduced due to the high interdependence among these two steps, being the integrated approach a popular topic of research. At the operational level, a feature greatly influencing the performance is the uncertainty in the surgical activities, as frequently large discrepancies between the scheduled duration and the real duration of the surgeries appear, together with the availability of the resources reserved for emergency arrivals. Despite the importance and the complexity of these hierarchical levels, decisions in practice are usually made according to the decision makers’ experience without considering the underlying optimization problems. Furthermore, the lack of usage of decision models and solution procedures causes the decision makers to consume long times on performing management tasks (e.g. determine the surgical schedule, react to unforeseen events, carry out what-if analyses, etc.), instead of healthcare tasks. The context discussed above stresses the need to provide healthcare decision makers with advanced operations research techniques (i.e. models and solution procedures) in order to improve the efficiency of the operating theatre resources and the quality of the healthcare services at the operational level. This Thesis is aimed at this goal

    Two-Stage Memory Allocation using AHP & Knapsack at PT Berca Hardayaperkasa

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    We propose to manage a (MicroStrategy) Business Intelligence Server in terms of RAM allocation for its Intelligent Cubes as a two-stage resource allocation problem in which the first stage is formulated as an multi-criteria problem that can be solved using Analytic Hierarchy Process (AHP) and the second stage is multiple (several) 0-1 classic Knapsack problems with the constraints that are obtained using the result from the first stage. This Approach happens to have an advantage in terms of computational complexity as well, it reduces from O(nM) to O(max{nj}max{Mj}) when calculated in parallel. We illustrate our proposal with a numerical example based on our experience

    Dynamic Surgery Assignment of Multiple Operating Rooms With Planned Surgeon Arrival Times

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    International audienceThis paper addresses the dynamic assignment of a given set of surgeries to multiple identical operating rooms (ORs). Surgeries have random durations and planned surgeon arrival times. Surgeries are assigned dynamically to ORs at surgery completion events. The goal is to minimize the total expected cost incurred by surgeon waiting, OR idling, and OR overtime. We first formulate the problem as a multi-stage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of static scheduling and First Come First Serve (FCFS) strategy

    Suitable task allocation in intelligent systems for assistive environments

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    The growing need of technological assistance to provide support to people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.Peer ReviewedPostprint (author's final draft
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