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

An Analysis of Robust Workforce Scheduling Models for a Nurse Rostering Problem

Disruptions impacting workforce schedules can be costly. A 1999 study of the United Kingdom\u27s National Health Service estimated that as much as 4% of the total resources spent on staffing were lost to schedule disruptions like absenteeism. Although disruptions can not be eliminated, workforce schedules can be improved to be more responsive to disruptions. One key area of study that has expanded over the past few years is the application of traditional scheduling techniques to re-rostering problems. These efforts have provided methods for responding to schedule disruptions, but typically require deviations to the disrupted schedule. This thesis examines five workforce scheduling models designed for a nurse rostering problem. Each model is designed to produce a robust workforce schedule that remains valid in the midst of disruptions and requires no schedule deviations. Each model is evaluated based on the number of disruptions it can receive before becoming invalid. Nonparametric statistical analysis is used to analyze the disruption data for each model and determine which workforce scheduling model produces the most robust schedule. The results of this research indicate that additional manpower must be applied to the correct skill sets in order to produce robust workforce schedules. Furthermore, workforce managers can consider leaving a portion of the workforce unscheduled (or in reserve) to accommodate schedule disruptions

A rostering approach to minimize health risks for workers: An application to a container terminal in the Italian port of Genoa

The evolving safety regulation is pushing seaports to comply with safety measures for workers performing heavy loads handling and repetitive movements. This paper proposes a risk-aware rostering approach in maritime container terminals, i.e., it addresses the rostering problem of minimizing and balancing workers’ risk in such terminals. To this end, a mixed integer mathematical programming model incorporating workforce risks is proposed, considering constraints such as the satisfaction of the workforce demand to perform the terminal operations, the worker-task compatibility and restrictions on the sequence of tasks assigned to the same worker. The model has been successfully applied to plan workforce over a six months horizon in a real container terminal located in Northern Italy, the Southern European Container Hub (SECH) in Genoa. As the workforce demand in SECH terminal is available at most two weeks in advance, a rolling horizon planning approach is devised. Experimental tests on real data provided by SECH terminal over a six months planning horizon highlight the effectiveness of the approach - the maximum monthly risk for workers is reduced by 33.9% compared to the current planning – and suitability to other container terminal contexts. Moreover, the model is applicable to a broad range of port situations, and robust enough to need little adaptation

Complicating factors in healthcare staff scheduling part 1 : case of nurse rostering

Nurse rostering is a hard problem inundated with inherent complicating features. This paper explores case studies on nurse rostering in order identify complicating factors common in the nurse rostering problem. A taxonomy of complicating factors is then derived. Furthermore, a closer look at the complicating factors and the solution methods applied is performed. Inadequacies of the approaches are identified, and suitable approaches derived. The study recommends future methods that are more intelligent, interactive, making use of techniques such fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems

An Algorithmic approach to shift structure optimization

Workforce scheduling in organizations often consists of three major phases: workload prediction, shift generation, and staff rostering. Workload prediction involves using historical behaviour of e.g. customers to predict future demand for work. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. In staff rostering, the generated shifts are assigned to employees. In general the problem and even its subproblems are NP-hard, which makes them highly challenging for organizations to solve. Heuristic optimization methods can be used to solve practical instances within reasonable running times, which in turn can result in e.g. improved revenue, improved service, or more satisfied employees for the organizations. This thesis presents some specific subproblems along with practical solution methods--- Työvoiman aikataulutusprosessi koostuu kolmesta päävaiheesta: työtarpeen ennustaminen, työvuorojen muodostus ja työvuorojen miehitys. Tulevaa työtarvetta ennustetaan pääasiassa menneisyyden asiakaskäytöksen perusteella käyttäen esimerkiksi tilastollisia malleja tai koneoppimiseen perustuvia menetelmiä. Työvuorojen muodostuksessa tehdään työvuororakenne, joka noudattaa ennustettua ja ennalta tiedettyä työtarvetta mahdollisimman tarkasti. Työvuorojen miehityksessä määritetään työvuoroille tekijät. Jokainen vaihe itsessään on haasteellinen ratkaistava. Erityisesti työvuorojen miehitys on yleensä NP-kova ongelma. On kuitenkin mahdollista tuottaa käytännöllisiä ratkaisuja järkevässä ajassa käyttäen heuristisia optimointimenetelmiä. Näin on saavutettavissa mitattavia hyötyjä mm. tuottoon, asiakkaiden palvelutasoon sekä työntekijöiden työtyyväisyyteen. Tässä väitöskirjassa esitellään eräitä työvoiman aikataulutuksen aliongelmia sekä niihin sopivia ratkaisumenetelmiä

Cost-efficient staffing under annualized hours

We study how flexibility in workforce capacity can be used to efficiently match capacity and demand. Flexibility in workforce capacity is introduced by the annualized hours regime. Annualized hours allow organizations to measure working time per year, instead of per month or per week. An additional source of flexibility is hiring employees with different contract types, like full-time, part-time, and min-max, and by hiring subcontractors. We propose a mathematical programming formulation that incorporates annualized hours and shows to be very flexible with regard to modeling various contract types. The objective of our model is to minimize salary cost, thereby covering workforce demand, and using annualized hours. Our model is able to address various business questions regarding tactical workforce planning problems, e.g., with regard to annualized hours, subcontracting, and vacation planning. In a case study for a Dutch hospital two of these business questions are addressed, and we demonstrate that applying annualized hours potentially saves up to 5.2% in personnel wages annually

An integrated mathematical model of crew scheduling

In conditions of air transport companies, the process of planning flight schedules is one of the most important processes each airline has to deal with. The flight schedule planning process consists of several consecutive plans. The first step of the planning process is defining which air routes will be operated, the decision is based on the business plan of the air transport company. Consequently, suitable airplanes have to be assigned to the individual air routes. And finally, on the basis of the pre-vious steps shifts of pilots can be planned, the shifts are usually planned one month in advance. However, with no respect to the created plan some unexpected disruptions of the flying staff, especially of the pilots, may happen in practice due to many reasons. In such cases the original plan has to be modified in order to react to the disruptions. The modifications can represent an optimisation problem – the air transport company has a set of the pilots and on the basis of their qualification and experience the company has to create new aircrews. The pilots can be found in different localities that are different from the airports of the planned flight departures. That means the newly planned aircrews are assigned to the individual flights with respect to costs associated with transportation of the aircrews to the airports of their departure. The problem can be solved by many approaches. One of the possible approaches is a heuristic approach which is based on sequential solving two linear mathematical models. The first model decides about the aircrews (matches the pilots with respect to their compatibility). The second model solves the assignment problem – the air-crews are matched with the individual flights. The article presents an integrated linear model which deals with both problems at the same time.V podmínkách leteckých dopravců je hlavním výsledkem plánovacího procesu letový řád. Samotná tvorba letového řádu je posloupností několika na sebe navazujících dílčích plánů. Prvním krokem v procesu plánování je naplánování linek podle obchodního záměru dopravce, následně se naplánovaným letům přidělí konkrétní typ letadla. Zpravidla s měsíčním předstihem je nutné vytvořit plán práce pro posádky pilotů, kteří budou letouny obsluhovat. Bez ohledu na vytvořený plán práce posádek však může dojít k neočekávaným výpadkům personálu. Potom je nutné operativně upravit připravený plán a posádky přeplánovat. Jedná se tedy o optimalizační problém, kdy dopravce má k dispozici množinu pilotů, z nichž je nutné na základě jejich kvalifikace a zkušeností vytvořit nové posádky. Piloti se mohou nacházet v různých destinacích, které mohou být různé od letišť odletů. Nově vytvořené posádky jsou potom přidělovány konkrétním letadlům v závislosti na velikosti nákladů spojených s přepravou posádek k letadlům. Uvedený problém lze řešit různými způsoby. První způsob je heuristický založený na postupném řešení dvou lineárních modelů. V prvním modelu se rozhoduje o vytvoření posádek. Druhý model vytvořené posádky přiděluje letadlům. Cílem tohoto příspěvku bude prezentovat integrovaný lineární model řešící oba problémy současně

Optimisation methods for staff scheduling and rostering: an employee-friendly approach

The growth in the global call centre industry over the last twenty years has been huge. The main motivating factor for businesses to introduce call centres as their main vehicle for handling customer contacts has been that call centres are inherently efficient. Since the mid-1980's, UK businesses have sought to establish competitive advantage by using call centres to reduce the cost of managing their customer contacts. Over the last decade or so, however, an alternative strategy has emerged based not on cost-reduction and efficiency, but on revenue generation and service quality. This new strategy places high value on customer and staff retention. This thesis is concerned with the operations management task of employee rostering. We argue that traditional models for producing rosters for call centre employees are designed to support the older efficiency-based culture, and are inappropriate for call centres adopting the more recent quality-based culture. We show how the use of methods and models drawn from conflicting management philosophies contributes to the high level of employee turnover, and inhibits the drive for service quality. Our primary contributions are to identify a set of rostering goals which reflect the interests of the employees, and to quantitatively represent these goals in a system of mathematical rostering models designed to support the revenue generation strategy. Our models are implemented using the robust Mixed Integer Programming methodology. In addition, we adapt our model to address the related problem of nurse rostering, and solve two benchmark problems to optimality. We demonstrate that our model generates rosters of a higher quality than the alternatives, at no additional cost