Patrick De Causmaecker

The State of the Art of Nurse Rostering

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. The need for quality software solutions is acute for a number of reasons. In particular, it is very important to efficiently utilise time and effort, to evenly balance the workload among people and to attempt to satisfy personnel preferences. A high quality roster can lead to a more contented and thus more effective workforce.In this review, we discuss nurse rostering within the global personnel scheduling problem in healthcare. We begin by briefly discussing the review and overview papers that have appeared in the literature and by noting the role that nurse rostering plays within the wider context of longer term hospital personnel planning. The main body of the paper describes and critically evaluates solution approaches which span the interdisciplinary spectrum from operations research techniques to artificial intelligence methods. We conclude by drawing on the strengths and weaknesses of the literature to outline the key issues that need addressing in future nurse rostering research.

A Memetic Approach to the Nurse Rostering Problem

Constructing timetables of work for personnel in healthcare institutions is known to be a highly constrained and difficult problem to solve. In this paper, we discuss a commercial system, together with the model it uses, for this rostering problem. We show that tabu search heuristics can be made effective, particularly for obtaining reasonably good solutions quickly for smaller rostering problems. We discuss the robustness issues, which arise in practice, for tabu search heuristics. This paper introduces a range of new memetic approaches for the problem, which use a steepest descent improvement heuristic within a genetic algorithm framework. We provide empirical evidence to demonstrate the best features of a memetic algorithm for the rostering problem, particularly the nature of an effective recombination operator, and show that these memetic approaches can handle initialisation parameters and a range of instances more robustly than tabu search algorithms, at the expense of longer solution times. Having presented tabu search and memetic approaches (both with benefits and drawbacks) we finally present an algorithm that is a hybrid of both approaches. This technique produces better solutions than either of the earlier approaches and it is relatively unaffected by initialisation and parameter changes, combining some of the best features of each approach to create a hybrid which is greater than the sum of its component algorithms.

A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem

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.

Practice and Theory of Automated Timetabling IV

The basic class–teacher timetabling problem is examined with the additional constraints due to the (un-)availability of source teachers and/or classes at some periods. We mention a generalization of this problem which occurs in image reconstruction problems in tomography. Complexity issues are discussed for both types of problems and some solvable cases are presented which can be derived from the image reconstruction formulation. Reductions to canonical forms are also described. Some other types of unavailability constraints (for classrooms or for lectures) are also reviewed.

Variable neighborhood search for nurse rostering problems

Nurse rostering problems consist of assigning varying tasks, represented as shift types, to hospital personnel with different skills and work regulations. The goal is to satisfy as many soft constraints and personal preferences as possible while constructing a schedule which meets the required personnel coverage of the hospital over a predefined planning period. Real-world situations are often so constrained that finding a good quality solution requires advanced heuristics to keep the calculation time down. The nurse rostering search algorithms discussed in this paper are not aimed at specific hospitals. On the contrary, the intention is that such algorithms should be applicable across the whole sector. Escaping from local optima can be very hard for the metaheuristics because of the broad variety of constraints. In this paper, we present a variable neighborhood search approach. Hidden parts of the solution space become accessible by applying appropriate problem specific neighborhoods. The method allows for a better exploration of the search space, by combining shortsighted neighborhoods, and very greedy ones. Experiments demonstrate how heuristics and neighborhoods can be assembled for finding good quality schedules within a short amount of calculation time.

METAHEURISTICS FOR HANDLING TIME INTERVAL COVERAGE CONSTRAINTS IN NURSE SCHEDULING

The problem of finding a high quality timetable for personnel in a hospital ward has been addressed by many researchers, personnel managers, and schedulers over a number of years. Nevertheless, automated nurse rostering practice is not common yet in hospitals. Many head nurses are currently spending several days per month on constructing their rosters by hand. In recent years, the emergence of larger and more constrained problems has presented a real challenge because finding good quality solutions can lead to a higher level of personnel satisfaction and to flexible organizational procedures. Compared to many industrial situations (where personnel schedules normally consist of stable periodic morning-day-night cycles) health care institutions often require more flexibility in terms of hours and shift types. The motivation for the research presented in this paper has been provided by real-world hospital administrators/schedulers and the approach that we describe has been implemented in over 40 hospitals in Belgium. This paper consists of two main contributions: modeling the real-world situation more accurately than has previously been done in the literature; and presenting and evaluating an efficient and effective tabu search procedure to solve these problems (as represented in the real-world model). The approach described in this paper concentrates on an advanced representation of the daily personnel requirements of healthcare institutions. We introduce time interval personnel requirements. Instead of formulating the requirements as a number of personnel needed per shift type for each day of the planning period, time interval requirements allow for the representation of the personnel requirements per day in terms of the start and end times of personnel attendance. This formulation enables the provision of a greater choice of shift work and part-time work and reduces the amount of unproductive time because it enables the shifts to be split and combined. We present an algorithmic approach to handle this new formulation. We also set up a series of experiments which indicate that, not only does this approach take into account the requests and requirements of hospital schedulers, but it also generates higher quality schedules when compared with earlier approaches. The obtained results are better in the sense that various specific real-world soft constraints can be satisfied by scheduling appropriate shift type combinations, whereas in the shift type approach, fixed shift types restricted the solution space.

A multi-objective approach for robust airline scheduling

We present a memetic approach for multi-objective improvement of robustness influencing features (called robustness objectives) in airline schedules. Improvement of the objectives is obtained by simultaneous flight retiming and aircraft rerouting, subject to a fixed fleet assignment. Approximations of the Pareto optimal front are obtained by applying a multi-meme memetic algorithm. We investigate biased meme selection to encourage exploration of the boundaries of the search space and compare it with random meme selection. An external population of high quality solutions is maintained using the adaptive grid archiving algorithm. The presented approach is applied to investigate simultaneous improvement of reliability and flexibility in real world schedules from KLM Royal Dutch Airlines. Experimental results show that the approach enables us to obtain schedules with significant improvements for the considered objectives. A large scale simulation study was undertaken to quantify the influence of the robustness objectives on the operational performance of the schedules. Rigorous sensitivity analysis of the results shows that the influence of the schedule reliability is dominant and that increased schedule flexibility could improve the operational performance.

A hybrid tabu search algorithm for automatically assigning patients to beds

OBJECTIVE We describe a patient admission scheduling algorithm that supports the operational decisions in a hospital. It involves efficiently assigning patients to beds in the appropriate departments, taking into account the medical needs of the patients as well as their preferences, while keeping the number of patients in the different departments balanced. METHODS Due to the combinatorial complexity of the admission scheduling problem, there is a need for an algorithm that intelligently assists the admission scheduler in taking decisions fast. To this end a hybridized tabu search algorithm is developed to tackle the admission scheduling problem. For testing, we use a randomly generated data set. The performance of the algorithm is compared with an integer programming approach. RESULTS AND CONCLUSION The metaheuristic allows flexible modelling and presents feasible solutions even when disrupted by the user at an early stage in the calculation. The integer programming approach is not able to find a solution in 1h of calculation time.

Local search neighbourhoods for dealing with a novel nurse rostering model

A novel nurse rostering model is developed to represent real world problem instances more accurately. The proposed model is generic in the sense that it allows modelling of essentially different problem instances. Novel local search neighbourhoods are implemented to take advantage of the problem properties represented by the model. These neighbourhoods are used in a variable neighbourhood search and in an adaptive large neighbourhood search algorithm. The performance of the solution method is evaluated empirically on real world data. The proposed model is open to further extensions for covering personnel planning problems in different sectors and countries.

The first international nurse rostering competition 2010

Nurse rostering is a complex task of practical relevance. Over the last years, researchers have been able to solve increasingly larger and more complex problems. In this paper, we describe the full procedure of running the First International Nurse Rostering Competition. The aim of the competition was to develop further interest in the area and to stimulate new solution approaches by bringing together researchers from different areas.We describe the competition’s spirit and its rules, the problem description and evaluation of solutions. We also explain the selection process and the final results. In addition, we give a brief description of the algorithmic approaches undertaken by the participants. Finally, we discuss the lessons learned from the competition and future activities to undertake.