The Second International Nurse Rostering Competition

Ceschia, Sara; Dang, Nguyen; Causmaecker, Patrick De; Haspeslagh, Stefaan; Schaerf, Andrea

doi:10.1007/s10479-018-2816-0

Cited by 38 publications

(46 citation statements)

References 21 publications

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“…The problem instances for evaluating the proposed approach are selected from the Second International Nurse Rostering Competition (INRC-II) [4]. The are three sets of instances, all available at [8].…”

Section: Experimental Design and Results Analysismentioning

confidence: 99%

“…If the first week is the current solving stage, history data is randomly selected from built-in artificial files [4]. History data for each stage must be produced by solvers before processing to the next stage and it should include the following information for each individual roster:…”

Section: Problem Descriptionmentioning

confidence: 99%

“…(3) where c s is the soft constraint violation cost and V sc is the number of violation for each constraint. The calculation of the constraint violation is fully described in [4].…”

Section: Experimental Settingsmentioning

confidence: 99%

“…In this paper, the NRP is tackled as a multi-stage optimisation problem is used to test the proposed technique because it is a widely investigated problem and presents an interesting challenge to ADP. Tackling the NRP as a multi-stage problem was proposed by [4].…”

Section: Introductionmentioning

confidence: 99%

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Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Shi

Landa-Silva

2017

Lecture Notes in Computer Science

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Abstract. An approximate dynamic programming that incorporates a combined policy, value function approximation and lookahead policy, is proposed. The algorithm is validated by applying it to solve a set of instances of the nurse rostering problem tackled as a multi-stage problem. In each stage of the problem, a weekly roster is constructed taking into consideration historical information about the nurse rosters in the previous week and assuming the future demand for the following weeks as unknown. The proposed method consists of three phases. First, a pre-process phase generates a set of valid shift patterns. Next, a local phase solves the weekly optimization problem using value function approximation policy. Finally, the global phase uses lookahead policy to evaluate the weekly rosters within a lookahead period. Experiments are conducted using instances from the Second International Nurse Rostering Competition and results indicate that the method is able to solve large instances of the problem which was not possible with a previous version of approximate dynamic programming.

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Section: Experimental Design and Results Analysismentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

“…(3) where c s is the soft constraint violation cost and V sc is the number of violation for each constraint. The calculation of the constraint violation is fully described in [4].…”

Section: Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Shi

Landa-Silva

2017

Lecture Notes in Computer Science

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“…Typically, these benchmark sets, such as NSPLib (Vanhoucke and Maenhout 2009), are not real-world-based instances but are generated randomly. Within the nurse scheduling research field, two competitions have been organized, for various problem configurations, such as multi-stage nurse rostering (Ceschia et al 2015). In the first competition, three tracks were presented, based on the available running time of the algorithms, including mall, medium, and large sized instances to solve (Haspeslagh et al 2014).…”

Section: Benchmark Setsmentioning

confidence: 99%

Case mix classification and a benchmark set for surgery scheduling

Leeftink

Hans

2017

J Sched

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Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of "instance proximity" to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

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Sea lion attacking‐based deer hunting optimization algorithm for dynamic nurse scheduling in health care sector contribution of hybrid algorithm in cloud

Sarkar¹,

Aryan²,

Chaki

2022

Concurrency and Computation

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Summary The main intent of this article is to frame the dynamic NSP in the cloud using a multi‐objective optimization strategy. The benefit of the dynamic scheduling over static scheduling is that the scheduling will be done week by week based on the available number of nurses. For solving this problem, several constraints like single assignment per day, under‐staffing, shift type successions, consecutive assignments, consecutive resting days, and complete week‐end are considered. With reference to these constraints, a minimized objective function is used based on the nurse demand in week by week. Each constraint is having some conditions for solving this problem. These schedules are stored in cloud for efficient decision making regarding NSP and it helps in assisting the future scheduling purposes. It also offers secure storage when compared to the other storage devices. As a novelty, this article tries to employ the hybrid meta‐heuristic algorithm called sea lion attacking‐based deer hunting optimization algorithm. Hybrid optimization algorithms have been reported to be promising for certain search problems with a higher convergence rate. Hence, the developed hybrid optimization algorithm hardly helps to generate a feasible and near‐optimal schedule at the end of the horizon.

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The Second International Nurse Rostering Competition

Cited by 38 publications

References 21 publications

Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Case mix classification and a benchmark set for surgery scheduling

Sea lion attacking‐based deer hunting optimization algorithm for dynamic nurse scheduling in health care sector contribution of hybrid algorithm in cloud

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