Workforce planning is a strategic key factor in the efficient use of the organisation’s resources. By promoting and utilizing the workforce’s kowledge, skills, education and competence the organisation can achieve long-term success.The purpose of this work is to investigate how planning and management of the operators competence can be reflected in the production. The primary questions that are intended to be answered in this work are:In which ways can a production prove to be vulnerable in terms of the competence among operators and their quantities?What effects can be achieved by broadening the competence among operators thru learning multiple machines? And how will it be possible to achieve a broadening of the competence?The aim of this case study is to present suggestions on how the competence among operators can be increased to achieve a more flexible production.The result showed that the production became vulnerable in flexibility in form of transfering personnel between machines and to replace absence when the quantity of operators has been strictly adapted to the number of machines. Since the operators received no opportunity to develop their competence they were instead limited to their orginal workplace. A competence matrix on minifactory level containing current state, target and activity list were presented as one of the suggestions to easier identify where the operators competence level is.

In this thesis, we propose a hierarchical level decision support system for workforce planning in medical equipment maintenance services. In strategic level, customer clusters and the total number of field engineers is determined via a mixed integer programming and simulation. In MIP, we aim to find the minimum number of field engineers. Afterwards, we analyze service measures such as response time via simulation. In tactical level, quarterly training program for the field engineers is determined via mixed integer programming and the results are interpreted in terms of service level via simulation.

Datorspelsbranschen är en relativt ung bransch, både jämfört med andra delarav mjukvaruindustrin och nöjesbranschen. En av de stora utmaningarnaspelutvecklare står inför är planeringen av den långsiktiga kompetensförsörjningen.Dock saknas sammanställda historiska data och applicerbararesursestimeringsmetoder, vilket gör detta arbete näst intill omöjligt. Det förstadelmålet med den här studien är att samla in historiska data angående strukturenoch storleken på utvecklingsteam som utvecklat AAA-spel (professionelltutvecklade spel med en hög budget) för pc-datorer. Datan samlas in frånkreditlistor hämtade via webbsidan Mobygames.com. Det andra delmålet beståri att utreda huruvida den insamlade datan kan appliceras på modeller förestimering av framtida personalbehov. De två modellerna som används ärtrendanalys samt kvotanalys. Resultatet visar att det går att samla in och byggaupp en historisk databas samt att båda modellerna kan appliceras på databasenmed gott resultat.Trendanalysen visar att storleken på projektteamen de senaste20 åren i stort sett utvecklats linjärt och kvotanalysen att det finns klara ochtydliga relationer mellan olika yrkesgrupper samt relationer mellanprojektteamet och en yrkesgrupp. / The video-game business is relatively young in relation to the software andentertainment industries. One of the major challenges for video-gamedevelopers is long-term human resource planning. Due to missing historicaldata in combination with applicable estimations methods, it is close toimpossible to perform such planning. The first sub-goal of this study is tocollect historical data about the structures and sizes of game development teamsinvolved in developing "Triple-A" games (professionally-developed games witha large budget) for PC. The collection of data is done through the websitemobygames.com. The second sub-goal is to examine whether the collected datacould be applied to an existing model for use in the forecasting of futurestaffing needs. The two models selected for this purpose are the trend analysismodel and the ratio analysis model. The result shows it is possible to build up adatabase with historical data. Trend analysis shows that the size of projectteams over the past 20 years has evolved almost linearly and ratio analysisshows clear relationships between the sizes of different occupational groups.

Magister Educationis - MEd / A qualitative research study, supplemented by a policy and contextual analysis was conducted to evaluate the skills development planning process for Human Resources managers at the NMMU. The findings of the study indicate a need for the NMMU to implement effective skills development planning practices to ensure relevant skills are developed in the Human Resources department. The findings further highlighted the need for a Human Resources strategy and model to guide the skills planning process.

Inom vården utförs ofta schemaläggning av personal manuellt, vilket kräver mycket tid och resurser. Att planera arbetet för en grupp läkare, med dess ofta mycket komplexa sammansättning vad gäller exempelvis arbetsuppgifter och kompetenser, är ingen lätt uppgift. Detta examensarbete studerar huruvida en automatiserad taktisk bemanningsplanering med en tidshorisont på ett halvår till ett år, skulle kunna underlätta denna uppgift. I rapporten presenteras en måloptimeringsmodell som implementerats i AMPL för att med CPLEX som lösare generera förslag till bemanningsplaner. För att utveckla en matematisk modell som väl representerar de förutsättningar som råder vid bemanningsplanering av läkare har alternativa formuleringar provats och utvärderats. Den mest lovande av modellerna, som baseras på måloptimering, har i en pilotstudie testats på data från Onkologiska kliniken vid Linköpings universitetssjukhus. Flexibiliteten i modellen gjorde att den enkelt kunde användas på de data som erhölls därifrån. Resultatet från pilotstudien indikerar att den utvecklade modellen har kapacitet att ge förslag till rimliga bemanningsplaner. / Scheduling of staff in the health care industry is typically done by hand, thus consuming a lot of time and effort. To plan the work for a group of physicians is a complex task, having to take into account factors like individual preferences and competences among the physicians. This thesis studies whether an automated tactical workforce planning, with a time horizon of half a year to a year, could facilitate this task. This thesis presents a goal programming model for generating suggestions to workforce plans for physicians. The model has been implemented in AMPL and is solved using CPLEX. During the development of the mathematical model for workforce planning, alternative model formulations have been tested and evaluated, and some of these are presented in the report. The most promising of them is one based solely on goal programming, and it has been tested in a pilot study on data from the Oncology Clinic at the University Hospital in Linköping. The flexibility of the model made it easy to use on the data provided by the clinic. The result of the pilot study indicates that the developed model has the capacity to give reasonable suggestions for workforce plans.

The recognition by societies of the problems caused by deafness and hearing impairment has led to a significant increase in the need for audiologists. Additionally, the rapid development of audiology in the last few decades, a wide diversity of audiology education programs, and increased globalisation highlighted the need to evaluate current audiology education and practice from an international perspective. A major aim of this thesis was therefore to study comparative audiology, to allow systematic analysis of what hearing health care is needed worldwide, and the strategies required to achieve future improvements. Rather than remaining theoretical, this thesis also aimed to develop an affordable and sustainable seeding model for audiology education, which would build capacity in countries with inadequate audiology education. An international study surveyed major audiology organisations, and completed surveys were received from 62 countries (representing 78% of the world population). Eighty-six percent of countries needed more audiologists, and cited a lack of government funding, low public awareness of deafness and inadequate audiology education as major reasons for the shortages. There was a wide range of professionals providing hearing health care and a large overlap in their scope of practices. To discuss these complex issues, a conceptual model for hearing health care was developed which formalised the interacting issues, and highlighted the feedback loops between them. The conceptual model presented a framework that was used to discuss individual countries in more detail, and provided a basis for the design of strategies aimed at improving hearing health care and education for groups of countries. Although five distinct groups were described, in practice, most countries may benefit to some extent from hearing health care systems that range from decentralised community-based models (providing basic care), to more institutionally-based models (offering advanced services and technologies). Evolution of hearing health care systems from basic community-based, to more advanced institutionally-based services, would presumably come with strengthening economies and health and education infrastructures, and concurrent growth in audiology education and private and public hearing health care markets. The rate at which this occurs depends in part on the strength of a country’s economy, but also on its other health priorities, the equality of its wealth distribution, the diversity and geographic spread of its population, and the adequacy and efficiency of its hearing health care education programs.

Orientador: Jorge Muniz Junior / Resumo: Esta dissertação fundamenta-se em estudos sobre Gestão de Pessoas na Administração Pública Federal, Planejamento da Força de Trabalho e Gestão por Competências para a realização de um estudo de caso exploratório no Instituto Federal de Educação, Ciência e Tecnologia e Roraima, cujo objetivo geral foi propor melhorias no processo de planejamento da força de trabalho dos servidores técnico-administrativos ocupantes do cargo de Assistente em Administração por meio do Mapeamento de Competências. Para alcançar tal objetivo, as competências necessárias para atuação desses servidores foram mapeadas por meio de análise documental e de conteúdo, e validadas por meio de questionários aplicados com os servidores e suas chefias. Ainda por meio de questionários foram identificadas as lacunas de competência que servirão de base para um planejamento de capacitação e desenvolvimento dos servidores pesquisados; foram identificadas algumas possibilidades de realocações desses servidores; e foi identificado que o dimensionamento atual atende às demandas de trabalho na Reitoria do IFRR. Vislumbram-se ainda algumas possibilidades de trabalhos futuros, como a elaboração das etapas de Desenvolvimento de Competências e de Avaliação de Desempenho por Competências. / Abstract: This dissertation is based on studies on People Management in the Federal Public Administration, Workforce Planning and Management by Competencies to carry out an exploratory case study at the Federal Institute of Education, Science and Technology and Roraima, whose general objective was propose improvements in the process of planning the workforce of the technical-administrative servers occupying the post of Assistant in Administration through Competency Mapping. In order to achieve this objective, the competencies needed to perform these servers were mapped through documentary and content analysis, and validated through questionnaires applied with the servers and their managers. Also through questionnaires were identified competency gaps that will serve as a basis for a training and development planning of the servers surveyed; some possibilities of reallocations of these servers were identified; and it was identified that the current dimensioning meets the demands of work in the Rectory of the IFRR. There are also some possibilities for future work, such as the elaboration of the Competence Development and Competency Performance Evaluation. / Mestre

Submitted by Maria Creuza Silva (mariakreuza@yahoo.com.br) on 2015-04-17T10:45:41Z No. of bitstreams: 1 DISS MP Luciano Moura. 2014.pdf: 12456929 bytes, checksum: 4c323db95b4e904c2baeb5a6cb2cd2e9 (MD5) / Approved for entry into archive by Maria Creuza Silva (mariakreuza@yahoo.com.br) on 2015-04-20T11:01:09Z (GMT) No. of bitstreams: 1 DISS MP Luciano Moura. 2014.pdf: 12456929 bytes, checksum: 4c323db95b4e904c2baeb5a6cb2cd2e9 (MD5) / Made available in DSpace on 2015-04-20T11:01:09Z (GMT). No. of bitstreams: 1 DISS MP Luciano Moura. 2014.pdf: 12456929 bytes, checksum: 4c323db95b4e904c2baeb5a6cb2cd2e9 (MD5) / O estudo analisa a produção científica sobre o planejamento da força de trabalho em saúde no período entre 1964 a 2013, ao tempo em que busca identificar critérios utilizados, métodos e as categorias de trabalhadores da saúde tomadas como objeto de estudos no Brasil. Realizou-se um levantamento de artigos nacionais selecionados nas bases de dados bibliográficas Lilacs e Scielo. Foram incluídos 58 trabalhos, analisados a partir das conjunturas políticas brasileiras em três momentos distintos, 1964 a 1979, 1980 a 1989 e 1990 a 2013. Observa-se uma tendência na utilização dos métodos normativos, iniciadas desde 1964 permanecendo ao longo de todo o período analisado. A centralização política no primeiro momento direcionou a prática do planejamento no setor saúde, com forte viés econômico, influência das organizações internacionais e foco na categoria médica. O momento seguinte, de intensa movimentação e proposta de reformulação das políticas de saúde no país é dada maior nitidez e visibilidade a estruturação da área de Recursos Humanos em Saúde. Nota-se que, embora influenciados pelo pensamento estratégico, o planejamento permaneceu com práticas e métodos normativos, voltados para os profissionais da área de enfermagem, incluindo o nível médio, técnico e universitário. Já o período entre 1990 a 2013, marcado pelo processo de implementação do SUS, caracteriza-se pela influência das políticas neoliberais, com redução do papel do Estado e forte processo de privatizações, levando ao pouco investimento no planejamento da força de trabalho em saúde (PFTS). Mesmo em um contexto preocupado com as questões sociais e de ampliação do acesso aos serviços de saúde, a preocupação com as questões referentes a força de trabalho permaneceram em respostas as discussões inerentes ao mercado de trabalho. Conclui-se que mesmo em diferentes contextos políticos e sociais, o planejamento da força de trabalho permaneceu voltado a responder as questões econômicas e com forte concentração no setor hospitalar.

<p dir="ltr">Employee staffing and scheduling are critical aspects of resource management in labor-intensive, customer-centric service organizations. This thesis investigates the optimal decision-making process for these critical tasks in the presence of non-stationary uncertainty, such as case-mix resident need, recommended staffing hours, and potential staffing turnover, a challenge prevalent in various domains, including healthcare and nursing home management.</p><p dir="ltr">The research begins predicting resident needs accurately. For this purpose, we present a novel Bayesian modeling approach to predict nursing home need-based resident census and staffing time. The resultant time series data of need-based resident census and staffing time are nonstationary with potential correlations between resource utilization groups. We thus propose Bayesian latent variable models with time-varying latent states to capture the dynamic patterns of resident service needs. We demonstrate the superiority of the proposed Bayesian prediction models by comparing their forecasting performance with several popular benchmark models, using historical assessment and aggregate staffing data from representative nursing homes.</p><p dir="ltr">The thesis further incorporates a rolling-horizon scheduling approach that integrates a periodically evolving Bayesian forecasting method into a series of stochastic look-ahead decision actions over multiple periods. To deal with the workforce scheduling with nonstationary demand uncertainty, we introduce a stochastic lookahead optimization framework that executes two-stage stochastic programming periodically along a rolling horizon to address the evolving non-stationary uncertainty. We obtain two-stage stochastic programming models to design effective work schedules, specifically assigning nurses to various shifts while balancing the staff workload and accommodating fluctuating resident needs.</p><p dir="ltr">We finally introduce the SNHSSO framework (stochastic nursing home staffing and scheduling optimizer), encompassing data modeling and addressing multi-period, multi-uncertainty, and multi-objective staffing and scheduling challenges. When the SNHSSO Optimizer is executed with the provided inputs, it generates recommended staffing decisions for longer planning horizons, as well as schedules and contingency plans for shorter planning horizons. These adapted decisions and adjusted parameters are archived for future reference, facilitating subsequent iterations of the process. SNHSSO optimizes caregiver assignments by taking into account probabilistic forecasts of service requirements, resident acuity, and staff turnover, all within two-stage stochastic mixed integer linear programs. Our approach leverages a scenario-based rolling horizon methodology to effectively solve the SNHSSO model.</p><p dir="ltr">The empirical foundation of this work is built on case studies conducted using Minimum Data Set (MDS) data spanning five years from 2014 to 2018 in Indiana nursing homes.</p>

Stochastic programming is an optimization technique that incorporates random variables as parameters. Because it better reflects the uncertain real world than its traditional deterministic counterpart, stochastic programming has drawn increasingly more attention among decision-makers, and its applications span many fields including financial engineering, health care, communication systems, and supply chain management. On the flip side, stochastic programs are usually very difficult to solve, which is further compounded by the fact that in many of the aforementioned applications, we also have discrete decisions, thereby rendering these problems even more challenging. In this dissertation, we study the class of two-stage stochastic mixed-integer programs (SMIP), which, as its name suggests, lies at the confluence of two formidable classes of problems. We design a novel algorithm for this class of problems, and also explore specialized approaches for two related real-world applications. Although a number of algorithms have been developed to solve two-stage SMIPs, most of them deal with problems containing purely integer or continuous variables in either or both of the two stages, and frequently require the technology and/or recourse matrices to be deterministic. As a ground-breaking effort, in this work, we address the challenging class of two-stage SMIPs that involve 0-1 mixed-integer variables in both stages. The only earlier work on solving such problems (Car&#248;e and Schultz (1999)) requires the optimization of several non-smooth Lagrangian dual problems using subgradient methods in the bounding process, which turns out to be computationally very expensive. We begin with proposing a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having 0-1 mixed-integer variables in both stages. Since the second-stage problems contain binary variables, their value functions are in general nonconvex and discontinuous; hence, the classical Benders' decomposition approach (or the L-shaped method) for solving two-stage stochastic programs, which requires convex subproblem value functions, cannot be directly applied. This motivates us to relax the second-stage problems and accompany this relaxation with a convexification process. To make this process computationally efficient, we propose to construct a certain partial convex hull representation of the two-stage solution space, using the relaxed second-stage constraints and the restrictions confining the first-stage variables to lie within some hyperrectangle. This partial convex hull is sequentially generated using a convexification scheme, such as the Reformulation-Linearization Technique (RLT), which yields valid inequalities that are functions of the first-stage variables and, of noteworthy importance, are reusable in the subsequent subproblems by updating the values of the first-stage variables. Meanwhile, since the first stage contains continuous variables, whenever we tentatively fix these variables at some given feasible values, the resulting constraints may not be facial with respect to the associated bounding constraints that are used to construct the partial convex hull. As a result, the constructed Benders' subproblems define lower bounds for the second-stage value functions, and likewise, the resulting Benders' master problem provides a lower bound for the original stochastic program defined over the same hyperrectangle. Another difficulty resulting from continuous first-stage variables is that when the given first-stage solution is not extremal with respect to its bounds, the second-stage solution obtained for a Benders' subproblem defined with respect to a partial convex hull representation in the two-stage space may not satisfy the model's binary restrictions. We thus need to be able to detect whether or not a Benders' subproblem is solved by a given fractional second-stage solution. We design a novel procedure to check this situation in the overall algorithmic scheme. A key property established, which ensures global convergence, is that these lower bounds become exact if the given first-stage solution is a vertex of the defining hyperrectangle, or if the second-stage solution satisfies the binary restrictions. Based on these algorithmic constructs, we design a branch-and-bound procedure where the branching process performs a hyperrectangular partitioning of the projected space of the first-stage variables, and lower bounds for the nodal problems are computed by applying the proposed modified Benders' decomposition method. We prove that, when using the least-lower-bound node-selection rule, this algorithm converges to a global optimal solution. We also show that the derived RLT cuts are not only reusable in subsequent Benders iterations at the same node, but are also inheritable by the subproblems of the children nodes. Likewise, the Benders' cuts derived for a given sub-hyperrectangle can also be inherited by the lower bounding master programs solved for its children nodes. Using these cut inheritance properties results in significant savings in the overall computational effort. Some numerical examples and computational results are presented to demonstrate the efficacy of this approach. The sizes of the deterministic equivalent of our test problems range from having 386 continuous variables, 386 binary variables, and 386 constraints, up to 1795 continuous variables, 1539 binary variables, and 1028 constraints. The results reveal an average savings in computational effort by a factor of 9.5 in comparison with using a commercial mixed-integer programming package (CPLEX 8.1) on a deterministic equivalent formulation. We then explore an important application of SMIP to enhance the traditional airline fleet assignment models (FAM). Given a flight schedule network, the fleet assignment problem solved by airline companies is concerned with assigning aircraft to flight legs in order to maximize profit with respect to captured path- or itinerary-based demand. Because certain related crew scheduling regulations require early information regarding the type of aircraft serving each flight leg, the current practice adopted by airlines is to solve the fleet assignment problem using estimated demand data 10-12 weeks in advance of departure. Given the level of uncertainty, deterministic models at this early stage are inadequate to obtain a good match of aircraft capacity with passenger demands, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. From this viewpoint, the initial decision should embrace various market scenarios so that it incorporates a sufficient look-ahead feature and provides sufficient flexibility for the subsequent re-fleeting processes to accommodate the inevitable demand fluctuations. With this motivation, we propose a two-stage stochastic programming approach in which the first stage is concerned with the initial fleet assignment decisions and, unlike the traditional deterministic methodology, focuses on making only a family-level assignment to each flight leg. The second stage subsequently performs the detailed assignments of fleet types within the allotted family to each leg under each of the multiple potential scenarios that address corresponding path- or itinerary-based demands. In this fashion, the initial decision of what aircraft family should serve each flight leg accomplishes the purpose of facilitating the necessary crew scheduling decisions, while judiciously examining the outcome of future re-fleeting actions based on different possible demand scenarios. Hence, when the actual re-fleeting process is enacted several weeks later, this anticipatory initial family-level assignment will hopefully provide an improved overall fleet type re-allocation that better matches demand. This two-stage stochastic model is complemented with a secondary model that performs adjustments within each family, if necessary, to provide a consistent fleet type-assignment information for accompanying decision processes, such as yield management. We also propose several enhanced fleet assignment models, including a robust optimization model that controls decision variation among scenarios and a stochastic programming model that considers the recapture effect of spilled demand. In addition to the above modeling concepts and framework, we also contribute in developing effective solution approaches for the proposed model, which is a large-scale two-stage stochastic 0-1 mixed-integer program. Because the most pertinent information needed from the initial fleet assignment is at the family level, and the type-level assignment is subject to change at the re-fleeting stage according to future demand realizations, our solution approach focuses on assigning aircraft families to the different legs in the flight network at the first stage, while finding relaxed second-stage solutions under different demand scenarios. Based on a polyhedral study of a subsystem extracted from the original model, we derive certain higher-dimensional convex hull as well as partial convex hull representations for this subsystem. Accordingly, we propose two variants for the primary model, both of which relax the binary restrictions on the second-stage variables, but where the second variant then also accommodates the partial convex hull representations, yielding a tighter, albeit larger, relaxation. For each variant, we design a suitable solution approach predicated on Benders' decomposition methodology. Using certain realistic large-scale flight network test problems having 900 flight legs and 1,814 paths, as obtained from United Airlines, the proposed stochastic modeling approach was demonstrated to increase daily expected profits by about 3% (which translates to about $160 million per year) in comparison with the traditional deterministic model in present usage, which considers only the expected demand. Only 1.6% of the second-stage binary variables turn out to be fractional in the first variant, and this number is further reduced to 1.2% by using the tighter variant. Furthermore, when attempting to solve the deterministic equivalent formulation for these two variants using a commercial mixed-integer programming package (CPLEX 8.1), both the corresponding runs were terminated after reaching a 25-hour cpu time limit. At termination, the software was still processing the initial LP relaxation at the root node for each of these runs, and no feasible basis was found. Using the proposed algorithms, on the other hand, the solution times were significantly reduced to 5 and 19 hours for the two variants, respectively. Considering that the fleet assignment models are solved around three months in advance of departure, this solution time is well acceptable at this early planning stage, and the improved quality in the solution produced by considering the stochasticity in the system is indeed highly desirable. Finally, we address another practical workforce planning problem encountered by a global financial firm that seeks to manage multi-category workforce for functional areas located at different service centers, each having office-space and recruitment-capacity constraints. The workforce demand fluctuates over time due to market uncertainty and dynamic project requirements. To hedge against the demand fluctuations and the inherent uncertainty, we propose a two-stage stochastic programming model where the first stage makes personnel recruiting and allocation decisions, while the second stage, based on the given personnel decision and realized workforce demand, decides on the project implementation assignment. The second stage of the proposed model contains binary variables that are used to compute and also limit the number of changes to the original plan. Since these variables are concerned with only one quality aspect of the resulting workforce plan and do not affect feasibility issues, we replace these binary variables with certain conservative policies regarding workforce assignment change restrictions in order to obtain more manageable subproblems that contain purely continuous variables. Numerical experiments reveal that the stochastic programming approach results in significantly fewer alterations to the original workforce plan. When using a commercial linear programming package CPLEX 9.0 to solve the deterministic equivalent form directly, except for a few small-sized problems, this software failed to produce solutions due to memory limitations, while the proposed Benders' decomposition-based solution approach consistently solved all the practical-sized test problems with reasonable effort. To summarize, this dissertation provides a significant advancement in the algorithmic development for solving two-stage stochastic mixed-integer programs having 0-1 mixed-integer variables in both stages, as well as in its application to two important contemporary real-world applications. The framework for the proposed solution approaches is to formulate tighter relaxations via partial convex hull representations and to exploit the resulting structure using suitable decomposition methods. As decision robustness is becoming increasingly relevant from an economic viewpoint, and as computer technological advances provide decision-makers the ability to explore a wide variety of scenarios, we hope that the proposed algorithms will have a notable positive impact on solving stochastic mixed-integer programs. In particular, the proposed stochastic programming airline fleet assignment and the workforce planning approaches studied herein are well-poised to enhance the profitability and robustness of decisions made in the related industries, and we hope that similar improvements are adapted by more industries where decisions need to be made in the light of data that is shrouded by uncertainty. / Ph. D.