A Constraint-directed Local Search Approach to Nurse Rostering Problems

In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach

Rostering from staffing levels: a branch-and-price approach

Many rostering methods first create shifts from some given staffing levels, and after that create rosters from the set of created shifts. Although such a method has some nice properties, it also has some bad ones. In this paper we outline a method that creates rosters directly from staffing levels. We use a Branch-and-Price (B\&P) method to solve this rostering problem and compare it to an ILP formulation of the subclass of rostering problems studied in this paper. The two methods perform almost equally well. Branch-and-Price, though, turns out to be a far more flexible approach to solve rostering problems. It is not too hard to extend the Branch-and-Price model with extra rostering constraints. However, for ILP this is much harder, if not impossible. Next to this, the Branch-and-Price method is more open to improvements and hence, combined with the larger flexibility, we consider it better suited to create rosters directly from staffing levels in practice

Nurse Rostering: A Tabu Search Technique With Embedded Nurse Preferences

The decision making in assigning all nursing staffs to shift duties in a hospital unit must be done appropriately because it is a crucial task due to various requirements and constraints that need to be fulfilled. The shift assignment or also known as roster has a great impact on the nurses’ operational circumstances which are strongly related to the intensity of quality of health care. The head nurse usually spends a substantial amount of time developing manual rosters, especially when there are many staff requests. Yet, sometimes she could not ensure that all constraints are met. Therefore, this research identified the relevant constraints being imposed in solving the nurse rostering problem (NRP) and examined the efficient method to generate the nurse roster based on constraints involved. Subsequently, as part of this research, we develop a Tabu Search (TS) model to solve a particular NRP. There are two aspects of enhancement in the proposed TS model. The first aspect is in the initialization phase of the TS model, where we introduced a semi-random initialization method to produce an initial solution. The advantage of using this initialization method is that it avoids the violation of hard constraints at any time in the TS process. The second aspect is in the neighbourhood generation phase, where several neighbours need to be generated as part of the TS approach. In this phase, we introduced two different neighbourhood generation methods, which are specific to the NRP. The proposed TS model is evaluated for its efficiency, where 30 samples of rosters generated were taken for analysis. The feasible solutions (i.e. the roster) were evaluated based on their minimum penalty values. The penalty values were given based on different violations of hard and soft constraints. The TS model is able to produce efficient rosters which do not violate any hard constraints and at the same time, fulfill the soft constraints as much as possible. The performance of the model is certainly better than the manually generated model and also comparable to the existing similar nurse rostering model

A Greedy Double Swap Heuristic for Nurse Scheduling

One of the key challenges of nurse scheduling problem (NSP) is the number of constraints placed on preparing the timetable, both from the regulatory requirements as well as the patients' demand for the appropriate nursing care specialists. In addition, the preferences of the nursing staffs related to their work schedules add another dimension of complexity. Most solutions proposed for solving nurse scheduling involve the use of mathematical programming and generally considers only the hard constraints. However, the psychological needs of the nurses are ignored and this resulted in subsequent interventions by the nursing staffs to remedy any deficiency and often results in last minute changes to the schedule. In this paper, we present a staff preference optimization framework which is solved with a greedy double swap heuristic. The heuristic yields good performance in speed at solving the problem. The heuristic is simple and we will demonstrate its performance by implementing it on open source spreadsheet software

Priorities of the Nurse Schedule by using MODM Approach: A case Study

El efecto bienestar de una migración internacional es habitualmente calculado como la variación del ingreso per capita de quienes quedan atrás luego de la migración. En este trabajo se presenta una crítica de dicho criterio que toma en cuenta que el efecto bienestar es opuesto para asalariados y capitalistas en el caso en que la migración modifique la relación K/L de la economía. Se propone un criterio alternativo que descubra de manera adecuada los efectos que la migración tiene para cada uno de los grupos mencionados.The welfare effect of an international migration is usually calculated as the per capita income variation of those left behind after the migration. A critique of this criterion and a proposal of an alternative one is presented in this paper, considering the fact that in the case in which the overall K/L ratio changes, the welfare effect of wage earners is the opposite of the welfare effect of capital owners.Instituto de Investigaciones Económica

Modeling the labor scheduling problem considering wellbeing for the clinic’s employees

Resumen El problema de la programación de turnos de trabajo, consiste en programar el horario de trabajo o los turnos de los distintos empleados buscando la minimización de los costos asociados al personal, el cual es un problema NP-hard. En este artículo se presenta un modelo de programación lineal entera mixta aplicado a un caso real, el cual tiene como objetivo minimizar el costo laboral, cumplir con los requerimientos de demanda y establecer condiciones laborales adecuadas para los empleados mediante la incorporación de restricciones que garanticen bienestar, para así generar una asignación óptima de los turnos de trabajo de los fisioterapeutas en las áreas de cuidados intensivos e intermedios de una Clínica. Para esto, se definieron diferentes escenarios variando tanto el número de fisioterapeutas como la estructura del modelo, resultando así que el número apropiado de fisioterapeutas a programar en la Clínica es de 32, ya que satisface todos los requerimientos de demanda, la legislación laboral, y las necesidades de la empresa y de los empleados. Abstract The Labor Scheduling Problem consists of planning the shifts for the employees, and minimizing costs associated to the workforce, which is a NP-hard problem.This paper presents a mixed integer linear programming modelapplied to a real case, which minimizes labor costs, satisfies the requirements of demand and establishes adequate working conditions for employees by incorporating constraints that ensure well-being to generate an optimal assignment of physiotherapist shifts in the intensive and intermediate care areas in a Clinic.For this, different scenarios were defined by varying both the number of physiotherapists and the structure of the model, the result that the appropriate number of physiotherapists in the clinic schedule is 32, since it satisfies the requirements of demand, employment law and the needs of the company and employees

A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure