A novel population-based local search for nurse rostering problem

Journal of Industrial and Management Optimization, 2021

Nurse Rostering is the activity of assigning nurses to daily shifts in order to satisfy the cover requirements, taking into account the operational requirements and nurses' preferences. The problem is usually modeled as sets of hard and soft constraints with an objective function to minimize violations of soft constraints. The nurse rostering problem is known to be NP-hard. Many metaheuristics were used to tackle this problem. One of the frequently used heuristics is the Variable Neighbourhood Search (VNS). The VNS is usually used as a standalone method or in integration with another exact or heuristic method. In this paper, a new hybrid VNS and Dynamic Programming based heuristic approach is proposed to handle the nurse rostering problem. In the proposed approach, two perturbation mechanisms are adopted simultaneously. The proposed approach is tested on two different benchmark data sets. A comparison with state-of-the-art methods from literature revealed the competitive performance of the proposed approach.

2011

The Nurse Rostering Problem (NRP) is one of the NP-hard problems that are difficult to solve for optimality. NRP deals with the distribution of working shifts to the staff nurses at healthcare organizations under given rules. Normally, NRP aims at generating a duty roster that satisfies all of the hard constraints (mandatory) and as many soft constraints (optional) as possible. This work introduces a greedy constructive heuristic algorithm, based on building two patterns of two-week's duration that satisfies all of the hard constraints and several soft constraints. The first pattern is designed mainly to fulfill the night shift coverage whilst the second pattern is concerned with morning and evening shifts only. If the solution is not feasible (for example the coverage is not met), a repairing mechanism algorithm is applied until a feasible solution is reached. Simulated Annealing (SA) is then used to improve the constructed solution. A real dataset from Universiti Kebangsaan Malaysia Medical Center (UKMMC) is used in this work to test the proposed approach. Currently, the duty roster at UKMMC is still constructed manually. Promising results have been obtained and presented in this paper.

Lecture Notes in Computer Science, 2011

Nurse rostering is the process of creating a plan for nurse working hours over a given time horizon. This problem, most variants of which are NP-hard, has been studied extensively for many years. Still, practical nurse rostering is mostly done manually, often by highly qualified health care personnel. This underlines the need to address the challenges of realistic, applied nurse rostering, and the implementation of advanced rostering methods in commercial software. In this paper, we present an industrial case study of a nurse rostering software currently used in several hospitals and other health care institutions in Norway and Sweden. The presented problem model has a rich set of hard and soft constraints, as required by Norwegian hospitals. Our solution approach is a hybrid: An Iterated Local Search framework that uses Constraint Programming for initial solution construction and diversification, and a Variable Neighborhood Descent for iterative improvement. The search method shows good results in terms of solution quality and computation time on a set of real world instances. We make these test instances available on-line.

European Journal of Operational Research, 2004

A practical nurse rostering problem, which arises at a ward of an Italian hospital, is considered. The nurse rostering problem is a typical employee timetabling problem, where each month it is required to generate the nursing staff shifts subject to various contractual and operational requirements. These requirements may be in conflict especially for those months in which manpower is reduced due to seasonal holidays. It is required to consider both holidays planning and parametric contractual constraints, but no cyclic schedules and corresponding weekly patterns. A local search approach is introduced which is based on a neighborhood operating on partial solutions completed by means of a greedy procedure so as to avoid the generation of infeasible solutions. Both a tabu search procedure and an iterated local search procedure are proposed. The solutions computed by the proposed procedures compare well with respect to a straightforward lower bound and strongly outperform those manually generated in the hospital ward. The proposed approach is now currently used on site.

1999

This paper deals with the problem of nurse rostering in Belgian hospitals. This is a highly constrained real world problem that was (until the results of this research were applied) tackled manually. The problem basically concerns the assignment of duties to a set of people with different qualifications, work regulations and preferences. Constraint programming and linear programming techniques can produce feasible solutions for this problem. However, the reality in Belgian hospitals forced us to use heuristics to deal with the over constrained schedules. An important reason for this decision is the calculation time, which the users prefer to reduce. The algorithms presented in this paper are a commercial nurse rostering product developed for the Belgian hospital market, entitled Plane.

European Journal of Operational Research, 2008

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Journal of Applied Computing Research, 2012

Health care providers are affected by problems of personnel costs. Usually, the generation of rosters is a hand-made and time-consuming task and does not always comply with the legislation and the internal rules. The article presents an approach to roster generation for nursing technicians according to legal and internal restrictions and in a satisfactory period of time. It is also designed to give employees a higher level of satisfaction concerning their day off preferences and a fair distribution of unpopular shifts. The article's proposal is to develop a hybrid system formed by a Tabu Search metaheuristic combined with a genetic algorithm. Experiments were carried out with artificial test cases based on real data. The results obtained were satisfactory, showing the feasibility of the solution in all tests performed.

ot foreseen. We propose a Tabu Search approach [5, 6] based on a neighborhood that operates on partial solutions in order to avoid the generation of unfeasible solutions. 2 The Tabu Search algorithm Given a set of nurses and a month to be scheduled, we have to allocate a shift or a day o# to every nurse for each day of the month, respecting contractual constraints. The day is partitioned into three shifts: morning shift, afternoon shift and night shift. The problem may be formulated in Linear Programming using decision variables x ijk ,where # x ijk = 1 if shift k in day j is allocated to nurse i, x ijk =0 otherwise, but this model requires thousands of boolean variables even for small size instances (e.g. 20 nurses). Contractual and operational requirements are in practice strongly in conflict, mostly when manpower is not enough to satisfy covering requests. Accordingtowardpolicy,contractual requirements become Porto, Portugal, July 16-20, 2001 MIC'2001 - 4th Metaheuristics Int

2005

This paper presents our work on decomposing a specific nurse rostering problem by cyclically assigning blocks of shifts, which are designed considering both hard and soft constraints, to groups of nurses. The rest of the shifts are then assigned to the nurses to construct a schedule based on the one cyclically generated by blocks. The schedules obtained by decomposition and construction can be further improved by a variable neighborhood search. Significant results are obtained and compared with a genetic algorithm and a variable neighborhood search approach on a problem that was presented to us by our collaborator, ORTEC bv, The Netherlands. We believe that the approach has the potential to be further extended to solve a wider range of nurse rostering problems.

2004

Constructing timetables of work for personnel in healthcare institutions is a highly constrained and difficult problem to solve. In this chapter, we will present an overview of our development of the algorithms that underpin a commercial nurse rostering decision support system that is in use in over 40 hospitals in Belgium. As such, we are particularly concerned with the real world regulations and requirements of Belgian institutions. We have concentrated upon short term rostering which involves assigning tasks to nurses in a hospital ward. Of course, the over-riding requirement of our algorithms is to assure a permanent level of care for the patients. However, our approaches are also required to consider administrative requirements, the nurses' contracts and their personal preferences.