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

Shift rostering using decomposition: assign weekend shifts first

This paper introduces a shift rostering problem that surprisingly has not been studied in literature: the weekend shift rostering problem. It is motivated by our experience that employees’ shift preferences predominantly focus on the weekends, since many social activities happen during weekends. The Weekend Rostering Problem (WRP) addresses the rostering of weekend shifts, for which we design a problem specific heuristic. We consider the WRP as the first phase of the shift rostering problem. To complete the shift roster, the second phase assigns the weekday shifts using an existing algorithm. We discuss effects of this two-phase approach both on the weekend shift roster and on the roster as a whole. We demonstrate that our first-phase heuristic is effective both on generated instances and real-life instances. For situations where the weekend shift roster is one of the key determinants of the quality of the complete roster, our two-phase approach shows to be effective when incorporated in a commercially implemented algorithm

A harmony search algorithm for nurse rostering problems

Harmony search algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The nurse rostering problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature

A tensor based hyper-heuristic for nurse rostering

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances

A hybrid constraint integer programming approach to solve nurse scheduling problems

The Nurse Scheduling Problem can be simply defined as assigning a series of shift sequences (schedules) to several nurses over a planning horizon according to some constraints and preferences. The inherent benefits of having higher-quality and more flexible schedules are a reduction in outsourcing costs and an increase of job satisfaction in health organizations. In this paper, we present a novel systematic hybrid algorithm, which combines Integer Programming (IP) and Constraint Programming (CP) to efficiently solve highly-constrained Nurse Scheduling Problems. Our focus is to exploit the problem-specific information to improve the performance of the algorithm, and therefore obtain high-quality solutions as well as strong lower bounds. We test our algorithm based on some real-world benchmark instances. Very competitive results are reported compared to the state-of-the-art algorithms from the recent literature, showing that the proposed algorithm is able to solve a wide variety of real-world instances with different complex structures

Integration of operations research and artificial intelligence approaches to solve the nurse rostering problem

Please note, incorrect date on spine and title page (2016). Degree was awarded in 2019.Nurse Rostering can be defined as assigning a series of shift sequences (schedules)to several nurses over a planning horizon according to some limitations and preferences. The inherent benefits of generating higher-quality rosters are a reduction in outsourcing costs and an increase in job satisfaction of employees.This problem is often very dicult to solve in practice, particularly by applying a sole approach. This dissertation discusses two hybrid solution methods to solve the Nurse Rostering Problem which are designed based on Integer Programming,Constraint Programming, and Meta-heuristics. The current research contributes to the scientific and practical aspects of the state of the art of nurse rostering. The present dissertation tries to address two research questions. First, we study the extension of the reach of exact method through hybridisation. That said, we hybridise Integer and Constraint Programming to exploit their complementary strengths in finding optimal and feasible solutions, respectively. Second,we introduce a new solution evaluation mechanism designed based on the problem structure. That said, we hybridise Integer Programming and Variable Neighbourhood Search reinforced with the new solution evaluation method to efficiently deal with the problem. To benchmark the hybrid algorithms, three different datasets with different characteristics are used. Computational experiments illustrate the effectiveness and versatility of the proposed approaches on a large variety of benchmark instancesNurse Rostering can be defined as assigning a series of shift sequences (schedules)to several nurses over a planning horizon according to some limitations and preferences. The inherent benefits of generating higher-quality rosters are a reduction in outsourcing costs and an increase in job satisfaction of employees.This problem is often very dicult to solve in practice, particularly by applying a sole approach. This dissertation discusses two hybrid solution methods to solve the Nurse Rostering Problem which are designed based on Integer Programming,Constraint Programming, and Meta-heuristics. The current research contributes to the scientific and practical aspects of the state of the art of nurse rostering. The present dissertation tries to address two research questions. First, we study the extension of the reach of exact method through hybridisation. That said, we hybridise Integer and Constraint Programming to exploit their complementary strengths in finding optimal and feasible solutions, respectively. Second,we introduce a new solution evaluation mechanism designed based on the problem structure. That said, we hybridise Integer Programming and Variable Neighbourhood Search reinforced with the new solution evaluation method to efficiently deal with the problem. To benchmark the hybrid algorithms, three different datasets with different characteristics are used. Computational experiments illustrate the effectiveness and versatility of the proposed approaches on a large variety of benchmark instance

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