A Framework For Workforce Management An Agent Based Simulation Approach

In today\u27s advanced technology world, enterprises are in a constant state of competition. As the intensity of competition increases the need to continuously improve organizational performance has never been greater. Managers at all levels must be on a constant quest for finding ways to maximize their enterprises\u27 strategic resources. Enterprises can develop sustained competitiveness only if their activities create value in unique ways. There should be an emphasis to transfer this competitiveness to the resources it has on hand and the resources it can develop to be used in this environment. The significance of human capital is even greater now, as the intangible value and the tacit knowledge of enterprises\u27 resources should be strategically managed to achieve a greater level of continuous organizational success. This research effort seeks to provide managers with means for accurate decision making for their workforce management. A framework for modeling and managing human capital to achieve effective workforce planning strategies is built to assist enterprise in their long term strategic organizational goals

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed

Considering skills evolutions in multi-skilled workforce allocation with flexible working hours

The growing need of responsiveness for manufacturing companies facing market volatility raises a strong demand for flexibility in their organisation. Since the company personnel are increasingly considered as the core of the organisational structures, a strong and forward-looking management of human resources and skills is crucial to performance in many industries. These organisations must develop strategies for the short, medium and long terms, in order to preserve and develop skills. Responding to this importance, this work presents an original model, looking at the line-up of multi-period project, considering the problem of staff allocation with two degrees of flexibility. The first results from the annualising of working time, and relies on policies of changing schedules, individually as well as collectively. The second degree of flexibility is the versatility of the operators, which induces a dynamic view of their skills and the need to predict changes in individual performance as a result of successive assignments. We are firmly in a context where the expected durations of activities are no longer predefined, but result from the performance of the operators selected for their execution. We present a mathematical model of this problem, which is solved by a genetic algorithm. An illustrative example is presented and analysed, and, the robustness of the solving approach is investigated using a sample of 400 projects with different characteristics

Attaining Knowledge Workforce Agility in a Product Life Cycle Environment using Real Options

The product life cycle (PLC) phenomenon has placed significant pressures on high-tech industries which rely heavily on the knowledge workforce in transferring cutting-edge technologies into products. This thesis examines systems where market changes and production technology advances happen frequently and unpredictably during the PLC, causing difficulties in predicting an appropriate demand on the knowledge workforce and in maintaining reliable performance. Knowledge workforce agility (KWA) is identified as a desirable means for addressing the difficulties, and yet previous work on KWA is incomplete. This thesis accomplishes several critical tasks for realizing the benefits of KWA in a representative PLC environment, semiconductor manufacturing. Real options (RO) is chosen as the approach towards exploiting KWA, since RO captures the essence of KWA-options in manipulating knowledge capacity, a human asset, or a self-cultivated organizational capability for pursuing interests associated with change. Accordingly, market demand change and workforce knowledge (WK) dynamics in adoption of technology advances are formulized as underlying stochastic processes during the PLC. This thesis models KWA as capacity options in a knowledge workforce and develops a RO approach of workforce training, either initial or continuous, for generating options. To quantify the elements of KWA that impact production, the role of the knowledge workforce in production and costs in obtaining KWA are characterized mathematically. It creates necessary RO valuation methods and techniques to optimize KWA. An analytical examination of the PLC models identifies that KWA has potential to reduce negative impacts and generate opportunities in an environment of volatile demand, and to compensate unreliable performance of knowledge workforce in adoption of technology advances. The benefits of KWA are especially important when confronting highly volatile demand, a low initial adoption level, shrinking PLCs, a growing market size, intense and frequent WK dynamics, insufficient learning capability of employees, or diminishing returns from investments in learning. The thesis further assesses RO, as an agility-driven approach, by comparing it to a chase-demand heuristic and to the Bass forecasting model under demand uncertainty. The assessment demonstrates that the KWA attained from the RO approach, termed RO-based KWA, leads to a stably higher yield, to a persistently larger net present value (NPV), and to a NPV distribution that is more robust to highly volatile demand. Subsequently, a quantitative evaluation of KWA value shows that the RO-based KWA creates a considerable profit growth, either with uncertainty in demand or in the WK dynamics. In evaluation, RO modeling and the RO valuation are identified to be useful in creation of KWA value especially in highly uncertain PLC environments. This thesis illustrates the effectiveness of the numerical methods used for solving the dynamic system problem. This research demonstrates an approach for optimizing KWA in PLC environments using RO. It provides an innovative solution for knowledge workforce planning in rapidly changing and highly unexpected environments. The work of this thesis is representative of studying KWA using quantitative techniques, where there is a dearth of quantitative studies in the literature

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

Scheduling and staffing of multiskilling of workforce in the context of off-side construction

Aim: There is an increase of interest in multiskilling research from the academy, industry and governmental authorities. Multiskilling of a workforce refers to enhancing flexibility of production by enabling labor to be reallocated in response to change in production priorities during the production horizon. Production priorities can change for several reasons; however, this study considers changes due to alterations in bottleneck configurations. The aim of this research is to investigate the extent to which operational benefits associated with different multiskilled resource management policies pertaining to bottleneck configurations can be achieved in off-site construction. To achieve this aim, first the multiskilling of a workforce in an off-site construction context should be understood as it is a complex matter in both conception and application. Second, an appropriate scheduling method should be developed to allocate an existing workforce to the right tasks, based on their skill level and set, during the production makespan. Third, a staffing platform should be developed to facilitate recruiting and hiring of a multiskilled workforce with an appropriate skill level and set. Methodology: In the Chapter 2 a two-stage paper-screening methodology was used to collect relevant papers in the literature review section. A flow-shop-based optimization methodology is used in the Chapter 3 to schedule multiskilled crew during the production makespan to achieve the production objective. A quadratic resource allocation model was developed to allocate a workforce to different tasks with consideration of the scheduling cost. A piece-wise linearization method is deployed to linearize quadratic constraints and decrease solution time. The Chapter 4 adopts a hybrid method including optimization and multi-criteria decision-making techniques to advise the best multiskilling strategy by comparing the performance of existing multiskilling staffing configurations based upon a range of existing qualitative and quantitative criteria. PROMETHEE is recognized as a suitable multicriteria decision-making approach to incorporate qualitative criteria. A flow shop scheduling method is used to obtain an optimized performance from alternatives pertaining to quantitative criteria. The Chapter 5 of this thesis presents a decision-support tool to optimize a multiskilled staffing strategy. The methodology in this chapter differs from that in Chapter 3 in that the developed staffing optimization platform explores every possible multiskilling strategy to find the optimal staffing configuration. Findings: In Chapter 2, the literature review results in the development of a construction multiskilling framework. This framework investigates multiskilling literature in conception and application. Multiskilling framework includes four main categories of multiskilling context, collateral effects, Mainstream research and strategy. A developed scheduling platform in Chapter 3 indicates that an optimal multiskilled labor allocation can lead to significantly different outcomes in terms of cost and time, based upon whether the location of the bottleneck is fixed or variable. The findings in Chapter 4 indicate that chaining and hiring a multiskilled workforce which is able to contribute to four different tasks, are the best multiskilling staffing strategies among existing ones. Sensitivity analysis pertaining to different criteria weight illustrates that the results of this investigation are stable in a wide range of alterations in the weight allocation. In Chapter 5 the decision-support tool illustrates that the optimal multiskilling strategy is highly context specific and should be customized in relation to production circumstances and data, especially the magnitude of bottlenecks. A slight alteration in the production characteristics can lead to significant changes in the optimal cross-training policy. Subjective multiskilling of a workforce could lead to counterproductive results such as a significant cost overrun. Numerical experiments indicate that if there is no extra capacity to allocate more workers to a bottleneck workstation, multiskilling of the workforce in the workstation immediately preceding the bottleneck workstation can lead to enhancement in the productivity. Contribution: The main contribution of the Chapter 2 is to identify theoretical gaps in the cross-training research and pave the way for comprehensive studies to produce more realistic multiskilling knowledge that considers both technical and managerial details. Research findings in Chapter 3, contribute to the scheduling literature by presenting an optimization platform for multi-skilled resource allocation and relocation during the makespan pertaining to the project objective. Research findings in Chapter 4 contribute to staffing literature by presenting a hybrid methodology which can encompass qualitative criteria as well. Research findings in Chapter 5 contribute to staffing literature by presenting a novel optimization platform to optimize configuration of multiskilled labor pertaining to their skill set. Chapter 3, 4 and 5 make another important contribution to the body of knowledge which is quantifying how performance measures and labor skill sets interact with each other. The decision-support tool, which is incorporated in Chapter 5, can help off-site construction industry practitioners, without a relevant academic background, to staff and schedule a workforce to achieve their production objective

Optimal crew routing for linear repetitive projects using graph theory and entropy maximization metric

Construction projects that contain several identical or similar units are usually known as repetitive or linear projects. Repetitive projects are regarded as a wide umbrella that includes various categories of construction projects and represents a considerable portion of the construction industry, and contain uniform repetition of work. CPM has been proved to be inefficient in scheduling linear projects because CPM does not address two key aspects, which are maintaining crew work continuity, and achieving a constant rate of progress to meet a given deadline. Line-of-balance (LOB) is a linear scheduling methodology that produces a work schedule in which resource allocation is automatically performed to provide a continuous and uninterrupted use of resource. The fundamental principles of LOB have several shortfalls that raise many concerns about LOB, which need to be attuned and improved in order to suit the nature of construction projects. Hence, this thesis proposes a hybrid approach for scheduling linear projects that stresses on the limitation of LOB scheduling technique. To meet the physical limitation of resources in linear projects, this study presents a flexible optimization model for resolving resource constraint dilemma in linear scheduling problems .The proposed model utilizes a MATLAB code as the searching algorithm to automate the model formulation. The novelty of this model is supplementing a new optimization engine and a decision supporting system that formulate the optimal crews routing between different activities in different units and guarantee the optimal crew distribution for cost efficiency. This model investigates the mechanics of allocating a multi- task skilled workforce between different activities in different units that can lead to increased productivity, flexibility, and work continuity; besides, this model has the capability of accurately identifying the critical path in linear projects. Furthermore, to avoid day-to-day fluctuation in resource demands, this study encompasses a simulation model for handling the resource leveling in linear construction projects. The proposed model was implemented using crystal ball ribbon based on an entropy maximization metric. The entropy-maximization method accounts for such possibility of allowing activity duration to be stretched or crunched relying on activity type without affecting total completion date of a project and provides more optimized resource allocation solutions. A case study for a 4-km sewage pipeline is used to demonstrate the capability of the proposed models, which illustrates the implementation of the proposed models in construction projects