Deluge Harmony Search Algorithm For Nurse Rostering Problems

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.

International Journal of Natural Computing Research, 2012

In this paper, a hybridization of Harmony Search Algorithm (HSA) with a greedy shuffle move is proposed for Nurse Rostering Problem (NRP). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a pre-determined scheduling period. HSA is a population-based method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. The performance of HSA is enhanced by hybridizing it with a greedy shuffle move. The proposed method is evaluated using a dataset defined in first International Nurse Rostering Competition (INRC2010). The hybrid HSA obtained the best results of the comparative methods in four datasets.

Journal of Intelligent Systems

One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health-care systems, creating nurse schedules comes to the fore. Nurse scheduling problem (NSP) is a complex optimization problem that allows for the preparation of an appropriate schedule for nurses and, in doing so, considers the system constraints such as legal regulations, nurses’ preferences, and hospital policies and requirements. There are many studies in the literature that use exact solution algorithms, heuristics, and meta-heuristics approaches. Especially in large-scale problems, for which deterministic methods may require too much time and cost to reach a solution, heuristics and meta-heuristic approaches come to the fore instead of exact methods. In the first phase of the study, harmony search algorithm (HSA), which has shown progress recently and can be adapted ...

International Journal of Electrical and Computer Engineering (IJECE), 2021

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.

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.

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.

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.

Journal of King Saud University - Computer and Information Sciences, 2013

In this paper, the Harmony Search Algorithm (HSA) is proposed to tackle the Nurse Rostering Problem (NRP) using a dataset introduced in the First International Nurse Rostering Competition (INRC2010). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a predefined scheduling period. HSA is an approximation method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. It improvises the new harmony iteratively using three operators: memory consideration, random consideration, and pitch adjustment. Recently, HSA has been used for NRP, with promising results. This paper has made two major improvements to HSA for NRP: (i) replacing random selection with the Global-best selection of Particle Swarm Optimization in memory consideration operator to improve convergence speed. (ii) Establishing multi-pitch adjustment procedures to improve local exploitation. The result obtained by HSA is comparable with those produced by the five INRC2010 winners' methods.

Applied Optimization, 2003

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. In this paper, we present a variable neighbourhood search approach for the nurse rostering problem. It takes the broad variety of constraints into consideration and succeeds better in escaping from local optima than other previously tested metaheuristics. Hidden parts of the solution space become accessible by applying appropriate problem specific neighbourhoods. The method allows for a better exploration of the search space, by combining shortsighted neighbourhoods, and very greedy ones. Experiments demonstrate how heuristics and neighbourhoods can be assembled for finding good quality schedules in a short calculation time. We modelled Belgian nurse rostering problems, that appear to be among the most complex in the world. However, 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.

Applied Soft Computing, 2020

Please cite this article as: P.-S. Chen and Z.-Y. Zeng, Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse rostering problems,