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The re-planning and scheduling of surgical cases in the operating room department after block release time with resource rescheduling

机译:通过资源重新安排在块释放时间后重新规划和调度手术室部门的手术案件

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The decisions on the strategic and tactical level contained in the operation room scheduling process are based on the expected demand. This demand is used to construct the master surgery schedule in a block booking system, which serves as a base for the planning and scheduling of surgical cases. However, in the operational phase, the actual demand may differ from the expected demand. This leads to increased waiting times, the cancellation of surgical cases and an inefficient utilisation of the operation room department for surgeons with spare capacity. In this paper, we study the Surgical Case re-Planning and Scheduling problem with resource re-scheduling that arises at block release time where the operation room planner tries to balance the capacity and the demand on the operational level. To that purpose, changes with respect to the surgeon schedule and the nurse roster are considered to adequately re-plan and schedule surgical cases. The problem under study minimises the number of changes related to the patient planning, the patient waiting time and the resource re-scheduling cost. We propose a three-phase heuristic that uses column generation to construct a high-quality feasible solution, which is further improved via local branching. Computational experiments have been conducted on an artificial dataset generated in a controlled and structured manner and on real-life data. Results demonstrate that our approach is able to produce (near-)optimal solutions and show the contribution of the different algorithmic building blocks. (C) 2019 Elsevier B.V. All rights reserved.
机译:经营室调度过程中所载战略和战术水平的决定基于预期的需求。这种需求用于在块预订系统中构建硕士外科进度,它是用于手术病例的规划和调度的基础。但是,在运营阶段,实际需求可能与预期的需求不同。这导致等待时间增加,外科手术病例的取消以及备用能力的外科医生的低效利用。在本文中,我们研究了在块释放时间内出现的资源重新调度的外科案例重新计划和调度问题,其中操作室计划员试图平衡运算级别的能力和需求。为此目的,关于外科医生日程安排和护士名单的变化被认为是充分重新计划和安排手术案件。正在研究的问题最大限度地减少了与患者规划相关的变化次数,患者等候时间和资源重新调度成本。我们提出了一种三相启发式,使用列生成来构造高质量的可行解决方案,通过局部分支进一步改善。在以受控和结构化方式和现实生活数据中生成的人工数据集进行了计算实验。结果表明,我们的方法能够生产(近)最佳解决方案并显示不同算法构建块的贡献。 (c)2019 Elsevier B.v.保留所有权利。

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