A Multi Criteria Metaheuristic Approach to Nurse Scheduling

Nurse scheduling problem is an NP-hard combinatorial problem for assigning various nurses to shifts under given constraints according to the hospital regulations. The enviable environment for the nurses is the balanced workload created by the roster by considering all constraints. The major goal of the nurse scheduling problem is to design a roster which satisfies all the hard and soft constraints provided by the hospital regulations. This rostering methodology will ensure the availability of nurses based on their preference during scheduled period. Hospitals are repeatedly needed to produce the roster schedule for their working nurses. A well scheduled roster of nurses will impact on the development of resources, quality of the hospital and recruitment of the nurses. Since 75's researchers have occupied major contribution towards real-time problems like nurse scheduling problem. Many hospitals still depend on the manual method of scheduling the nurses which is carried out by the head nurses. This work describes the detailed survey of nurse scheduling problem and methodologies to solve NSP. In this survey many approaches is examined by including penalty calculation, preferences of nurses to tackle optimization algorithm.

Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)

Nurse scheduling problem (NSPs) represents a di cult class of Multi-objective optimization problems consisting of a number of interfering objectives between the hospitals and individual nurses. The objective of this research is to investigate di culties that occur during solution of NSP using Evolutionary Algorithms, in particular Genetic Algorithms (GA). As the solution method a population-less Cooperative Genetic Algorithms (CGA) is taken into consideration. Because contrary to competitive GAs, we have to simultaneously deal with the optimization of the tness of the individual nurses and also optimization of the entire schedule as the nal solution to the problem in hand. To conrm the search ability of CGA, rst a simpli ed version of NSP is examined. Later we will report a more complex and useful version of the problem. We will also compare CGA with another multi-agent evolutionary algorithm using pheromone style communication of real ants. Finally, we will report the results of computer simulations acquired throughout the experiments.

2010

Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.

The nurse scheduling problem (NSP) is multicriteria decision problem concerned with allocation of shift schedules to available nurses over a planning horizon of one week to one month. Developing interactive, multi-objective, and fast optimization approaches for solving the NSP is imperative. The NSP has posed continued challenges to decision makers in healthcare organizations. This paper presents a fuzzy simulated metamorphosis algorithm (FSM), inspired by biological metamorphosis evolution. The algorithm mimics the metamorphosis process by going through three phases, namely, initialization, growth, and maturation. Initialization randomly generates a single candidate solution using a guided constructive heuristic. Subsequently, the algorithm goes through growth and maturation loops, till termination criteria are satisfied. Computational results based on benchmark problems in the literature demonstrate that, compared to related metaheuristic algorithms, FSM is more efficient and effective, producing better solutions within reasonable computation times.

International Transactions in Operational Research, 2003

supported by FINEP (ProNEx 107/97) and Abstract Several heuristics, based on evolutive algorithms and local search, are used to solve the nurse scheduling problem at a large hospital. Due to several intricate and specific restrictions imposed on the schedules, the problem is a difficult one to solve by hand. Moreover, some of the restrictions have a subjective value attached to them, and this constrains the use of exact methods that search for global optima. In order to facilitate the use of the solver modules by the hospital staff, a user interface was also implemented.

2015

In a fuzzy environment where the decision making involves multiple criteria, fuzzy multi-criteria decision making approaches are a viable option. The nurse re-rostering problem is a typical complex problem situation, where scheduling decisions should consider fuzzy human preferences, such as nurse preferences, decision maker’s choices, and patient expectations. For effective nurse schedules, fuzzy theoretic evaluation approaches have to be used to incorporate the fuzzy human preferences and choices. The present study seeks to develop a fuzzy multi-criteria simulated evolution approach for the nurse re-rostering problem. Experimental results show that the fuzzy multi-criteria approach has a potential to solve large scale problems within reasonable computation times. Keywords—fuzzy simulated evolution; fuzzy theory; multiple criteria; nurse re-rostering

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.

2015 International Conference on Industrial Engineering and Operations Management (IEOM), 2015

Motivated by the biological metamorphosis process and the need to solve multi-objective optimization problems with conflicting and fuzzy goals and constraints, this paper proposes a simulated metamorphosis algorithm, based on the concepts of biological evolution in insects, such as moths, butterflies, and beetles. By mimicking the hormone controlled evolution process the algorithm works on a single candidate solution, going through initialization, iterative growth loop, and finally maturation loop. The method is a practical way to optimizing multi-objective problems with fuzzy conflicting goals and constraints. The approach is applied to the nurse scheduling problem. Equipped with the facility to incorporate the user's choices and wishes, the algorithm offers an interactive approach that can accommodate the decision maker's expert intuition and experience, which is otherwise impossible with other optimization algorithms. By using hormonal guidance and unique operators, the algorithm works on a single candidate solution, and efficiently evolves it to a near-optimal solution. Computational experiments show that the algorithm is competitive.

Computing Research Repository, 2008

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using cooperating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

IEEE Congress on Evolutionary Computation, 2001

When applying evolutionary algorithms to difficult real-world problems, the fitness function routinely needs evaluating for a very high number of intermediary cases. The paper is concerned with real-world nurse rostering problems with highly constrained resources. We consider a particular approach, which allows for a quick evaluation and is general enough to deal with other kinds of resource planning problems with