47 research outputs found
An Optimization Technique to Prepare Nurse Schedule for a Monthly TIME Horizon
Nurse scheduling problem is one of the most difficult scheduling problems to solve since its solution space is large and it expects to comply many constraints. There is no standard model or a method of solution for nurse scheduling. The main objective of this study is to search for a scientific method to prepare a monthly working schedule for a group of nursing officers employed in a hospital. We propose an optimization method to prepare an optimal schedule. Initially, we develop an optimization model by formulating the objective and the constraints of the problem. The optimization model that we are interestedin is a 0-1 Integer Linear Programming problem. We apply the Branch-and-Bound technique to solve the problem using the optimization software package LINGO. Finally, the solution to the optimization problem is formulated to a regular nurse schedule. The methodology is illustrated by preparing a monthly schedule for a private hospital in Sri Lanka
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
An Estimation of Distribution Algorithm for Nurse Scheduling
Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems
An application of demand profiling and optimisation of staffing levels within Leicestershire Police Force
In modern society there is a necessity for
industries to maximise their productivity but also
to minimise their outlay, be that in the form of
equipment or personnel. Motivated by interest
within UK policing towards professionalization
of their service, the dual problem of modelling
the demands upon front-line Police officers and
the optimisation of available resources is
investigated. Demand borne from calls to service
from the general public is considered and a
method whereby this may be realistically
quantified in a predictive model is shown. An
optimisation technique is described that
minimises the number of staff required in order to
meet expected demand using a user input series
of shift definitions.
A tool is created that contains both the demand
profiling and optimisation functions described.
The effectiveness of this tool is then shown by
application to the shift allocation of Police
personnel with results illustrating this provided
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
A time predefined variable depth search for nurse rostering
This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive