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首页> 外文期刊>International Journal of Production Research >An adjustable robust optimisation method for elective and emergency surgery capacity allocation with demand uncertainty
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An adjustable robust optimisation method for elective and emergency surgery capacity allocation with demand uncertainty

机译：需求不确定的选择性和紧急手术能力分配的可调鲁棒优化方法

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This article addresses the problem of allocating limited operating room (OR) capacity among subspecialties in hospitals where two types of demands exist: elective surgeries and emergency surgeries. In many medium- and small-scale hospitals, no OR capacity is affiliated with a particular subspecialty, but several subspecialties share the OR capacity in the hospital. The administrator needs to decide how much OR capacity to assign to each subspecialty and how much to reserve for emergency surgeries. Because such an allocation is usually decided several weeks or even months before, the only information about future demands is their range. We focus on finding a robust solution that handles disturbances in the surgery demand. An adjustable robust model is developed to solve this surgery capacity allocation problem with demand uncertainty. The worst-case revenue loss resulting from a shortage of OR resources is minimised. We examine the impact of conservativeness of the robust model on the revenue loss of the surgery department, which provides hospital administrators guidance for setting the adjustable parameters. An implementer-adversary algorithm is applied to solve the robust optimisation model. We present computational results comparing the proposed robust optimisation approach with a scenario-based stochastic optimisation; the results show that by adjusting the conservatism, the expected objective value realised by the robust solution is very close to that obtained by the stochastic programming approach. Moreover, the robust optimisation method has the benefit of limiting the worst-case outcome of the surgery capacity allocation problem.
机译：本文解决了在存在两种类型需求的医院子科之间分配有限手术室（OR）能力的问题：选修外科和急诊外科。在许多中小型医院中，特定亚专科医院没有OR能力，但几个亚专科医院共享OR能力。管理员需要确定为每个子专业分配多少OR能力以及为急诊手术保留多少资源。因为这种分配通常是在数周甚至数月之前决定的，所以有关未来需求的唯一信息就是它们的范围。我们专注于寻找一种能够解决手术需求中干扰的强大解决方案。开发了可调整的鲁棒模型来解决具有需求不确定性的手术能力分配问题。由OR资源短缺导致的最坏情况下的收入损失被最小化。我们研究了健壮模型的保守性对手术部门收入损失的影响，这为医院管理员设置可调参数提供了指导。应用实施者-对手算法来求解鲁棒优化模型。我们提出的计算结果将提出的鲁棒优化方法与基于场景的随机优化进行了比较；结果表明，通过调整保守性，鲁棒解决方案实现的预期目标值与随机规划方法获得的目标值非常接近。此外，鲁棒的优化方法的好处是可以限制手术能力分配问题的最坏情况。

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