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

Optimal Personnel Deployment Strategy for Self-Perform Maintenance on Wind Farms

Wind turbine maintenance is a major cost factor and key determinant of wind farm productivity. Many companies outsource critical maintenance procedures while others perform these tasks in-house, referred to as self-perform maintenance. While expected to reduce time to profit on asset investment, self-perform requires an efficient personnel deployment strategy to implement. In this thesis, a partial solution to the optimization of wind turbine maintenance personnel team assignment is presented. A holistic framework is established, through analysis of historical work orders, for defining metrics that evaluate the performance of technicians. These metrics are further transformed into interpretable proficiency coefficients to be incorporated into an application of the team assignment problem. A case study of a large wind farm owner and operator is presented to illustrate the potential benefits and caveats of the proposed metrics and evaluation strategy. Additionally, the practicality of the data-derived metrics and proficiencies is illustrated. Key improvement strategies in data quality and metric aggregation are detailed, as well as discussion of a potential formulation of the task-to-team assignment problem, to be modeled through a standard maximin approach and solved through an integer programming technique

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

Optimal Personnel Deployment Strategy for Self-Perform Maintenance on Wind Farms

Wind turbine maintenance is a major cost factor and key determinant of wind farm productivity. Many companies outsource critical maintenance procedures while others perform these tasks in-house, referred to as self-perform maintenance. While expected to reduce time to profit on asset investment, self-perform requires an efficient personnel deployment strategy to implement. In this thesis, a partial solution to the optimization of wind turbine maintenance personnel team assignment is presented. A holistic framework is established, through analysis of historical work orders, for defining metrics that evaluate the performance of technicians. These metrics are further transformed into interpretable proficiency coefficients to be incorporated into an application of the team assignment problem. A case study of a large wind farm owner and operator is presented to illustrate the potential benefits and caveats of the proposed metrics and evaluation strategy. Additionally, the practicality of the data-derived metrics and proficiencies is illustrated. Key improvement strategies in data quality and metric aggregation are detailed, as well as discussion of a potential formulation of the task-to-team assignment problem, to be modeled through a standard maximin approach and solved through an integer programming technique

Scheduling in Queueing Systems with Specialized or Error-prone Servers

Consider a multi-server queueing system with tandem stations, finite intermediate buffers, and an infinite supply of jobs in front of the first station. Our goal is to maximize the long-run average throughput of the system by dynamically assigning the servers to the stations. For the first part of this thesis, we analyze a form of server coordination named task assignment where each job is decomposed into subtasks assigned to one or more servers, and the job is finished when all its subtasks are completed. We identify the optimal task assignment policy of a queueing station when the servers are either static, flexible, or collaborative. Next, we compare task assignment approaches with other forms of server assignment, namely teamwork and non-collaboration, and obtain conditions for when and how to choose a server coordination approach under different service rates. In particular, task assignment is best when the servers are highly specialized; otherwise, teamwork or non-collaboration are preferable depending on whether the synergy level among the servers is high or not. Then, we provide numerical results that quantify our previous comparison. Finally, we analyze server coordination for longer lines, where there are precedence relationships between some of the tasks. We show that for static task assignment, internal buffers at the stations are preferable to intermediate buffers between the stations, and we present numerical results that suggest our comparisons for one station systems generalize to longer lines. The second part of this thesis studies server allocation when the servers can work in teams and the team service rates can be arbitrary. Our objective is to improve the performance of the system by dynamically assigning servers to teams and teams to stations. We first establish sufficient criteria for eliminating inferior teams, and then we identify the optimal policy among the remaining teams for the two-station case. Next, we investigate the special cases with structured team service rates and with teams of specialists. Finally, we provide heuristic policies for longer lines with teams of specialists, and numerical results that suggest that our heuristic policies are near-optimal. In the final part of this dissertation, we consider the scenario where a job might be broken and wasted when being processed by a server. Servers are flexible but non-collaborative, so that a job can be processed by at most one server at any time. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the buffer, where the thresholds depend on the service rates and defect probabilities of the two servers. For larger systems, we provide a partial characterization of the optimal policy. In particular, we show that the optimal policy may involve server idling, and if there exists a distinct dominant server at each station, then it is optimal to always assign the servers to the stations where they are dominant. Finally, we propose heuristic server assignment policies motivated by experimentation with three-station lines and analysis of systems with infinite buffers. Numerical results suggest that our heuristics yield near-optimal performance for systems with more than two stations.Ph.D

The Team Size Paradox: Knowledge Transfer and Process Loss Effects on Team Formation

Teamwork has become a popular operational strategy applied as part of workforce management plans in manufacturing and service industries. Teamwork has been commonly used in organizations to pursue different goals such as increase business operations agility, boost company productivity, improve quality in operations, increase company flexibility, promote collaborative learning, hasten the learning process of novice workers during training, promote employee’s motivation, or in some cases as a necessary tool to perform specific operations that cannot be performed by individual workers. Previous work has shown that the application of a teamwork strategy as part of the organization workforce management plan can positively impact the organization outcomes via creativity, innovation, motivation and learning. However, there is also evidence that a teamwork strategy can have a significant negative impact on organizational outcomes if the strategy is not properly designed and implemented because of greater demands on cooperation, limitations in communication, conflicts among workers, and cognitive biases. The impact of the benefits and relative costs of teamwork as part of workforce management plans are mostly determined by the design and implementation of a teamwork strategy and by the specific task and organization in which the teamwork strategy is applied. Thus, the informed design of a teamwork strategy is necessary in order to obtain the maximum benefit of its implementation in organizations. The literature on teamwork has mainly focused on observational studies that facilitates the understanding of teamwork dynamics based on team composition and how these affect individuals and team performance. However, the translation of findings in these studies to operation research strategies for workforce management application has been limited. Specifically, studies that address workforce allocation when is applied a teamwork-based strategy implementation in organizations has received little attention in literature, while the consideration of team dynamics from both perspectives, gains and losses, simultaneously is still a gap in the literature of operation research for the study and development of workforce management plans. The goal of this dissertation is to reduce this gap, addressing the design of workforce management plans that considers the implementation of a teamwork strategy accounting for the gains and losses that arise from team dynamics. This work presents the exploration of the team formation process from the perspective of team size for learning-productivity environments. Workforce heterogeneity is considered through the modeling of individuals’ productivity as function of individual learning parameters. Team dynamics are incorporated in an individual productivity model by including learning by knowledge transfer, which accounts for the benefits in individual productivity that can be gain though the interaction of workers within the team, and by including process loss, which accounts for the losses in individual productivity that arise from demand in coordination, conflicts, motivation losses and communication challenges that arise in teams. The methodology used in the study centers on the use of simulation and explicit mathematical representations based on models in the literature. This dissertation 1) explores the jointly effects of human and organizational factors on system performance and their relevance to the worker-cell assignment problem, which demonstrates the value of considering these factors as part of the workforce planning process in a cellular manufacturing setting; 2) investigate the joint effects of knowledge transfer and the process loss on team performance resulting from the incorporation of additional workers into the work team and its implication on the optimal team size when considering different type of task structures; and 3) explores the team formation problem with the aim of characterizing optimal team size in a multiple work-team setting. This dissertation will serve as basis for the development of mathematical models to address the team formation problem as part of operation research applications considering individuals productivity gain and losses which results from team dynamic and provide insight to guide managers in making decisions about team design and teamwork strategy implementation