首页> 外文会议>2011 3rd Conference on Data Mining and Optimization >Hybridization of heuristic approach with variable neighborhood descent search to solve nurse Rostering problem at Universiti Kebangsaan Malaysia Medical Centre (UKMMC)
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Hybridization of heuristic approach with variable neighborhood descent search to solve nurse Rostering problem at Universiti Kebangsaan Malaysia Medical Centre (UKMMC)

机译:启发式方法与可变邻域后裔搜索的混合解决了马来西亚马来西亚大学医学中心(UKMMC)的护士名册问题

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Nurse Rostering problem (NRP) represents a subclass of scheduling problems that are difficult to solve for optimality. It deals with assigning shifts to staff nurses subject to satisfying required workload and other constraints. The constraints are classified into hard constraints (compulsory) and soft constraints (should be satisfied as much as possible). A feasible solution is a solution that satisfies all hard constraints. However, the quality of the duty roster is measured based on satisfying the soft constraints. This study is an attempt to solve a real world scenario from Universiti Kebangsaan Malaysia Medical Center (UKMMC). Currently, the duty roster is constructed manually by head nurses in different wards. So, the main goal of our work is to generate good duty roster that satisfied all the hard constraints which are required by (UKMMC). A constructive heuristic is introduced to solve (UKMMC) nurse rostering problem. This heuristic is a hybridization of cycling schedule with non-cycling schedule (random order). If the solution is not feasible, we apply a repairing mechanism to produce feasible solution. Then, the initial solution is improved by applying variable neighborhood descent search. Computational results are presented to demonstrate the effectiveness of the proposed approach.
机译:护士名册问题(NRP)代表调度问题的子类,很难解决其最优性。它负责满足需要的工作量和其他约束条件的情况下,为员工护士分配轮班。约束分为硬约束(强制性)和软约束(应尽可能满足)。可行的解决方案是满足所有硬约束的解决方案。但是,基于满足软约束条件来测量值班名册的质量。这项研究是为了解决马来西亚大学医学中心(UKMMC)的现实情况。目前,值班表由不同病房的护士长手动建立。因此,我们工作的主要目标是生成能够满足(UKMMC)要求的所有严格限制的良好职责名册。引入了一种建设性的启发式方法来解决(UKMMC)护士排班问题。此启发式方法是骑行计划与非骑行计划(随机顺序)的混合。如果解决方案不可行,我们将采用修复机制来产生可行的解决方案。然后,通过应用可变邻域下降搜索来改进初始解决方案。计算结果被提出来证明所提出的方法的有效性。

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