Workforce planning in service systems is essential for customer satisfaction and profitability. Typical decisions include hiring levels, shift design, break scheduling, movement of workers around facilities, and matching worker skills to the requirements of tasks. The complexity of these decisions grows when realistic factors such as spatiotemporal demand dynamics, system performance assessment, or skill learning are incorporated into planning. As a result, optimal (or near-optimal) workforce plans should utilize resources efficiently and achieve satisfactory levels of service. This dissertation provides models and solution algorithms for three problems in service system optimization, which include realistic workforce management planning, nonlinear dynamics, and scheduling of tasks. The second chapter studies an airport security screening process and prescribes a daily operational plan, including service rate (i.e., number of servers), scheduling, and resource allocation decisions. The non-stationary arrivals are predicted and known in advance. A discrete-time queuing model that relies on a simple approximation and flow conservation equations embedded in a mixed-integer programming formulation, where consecutive time periods are connected. A multi-step solution method is proposed to optimize various metrics, such as maximum allowed queue lengths and wait times. The third chapter extends the model from the second chapter. In this case, resources can move between different server locations, and their transit time is unproductive. Movement dynamics are captured via a multi-commodity flow model on a time-expanded network. A new discrete-time queue approximation scheme addresses the inaccuracies in existing methods stemming from server overload and underload fluctuations. Problem-specific valid inequalities are derived to improve the solution time, and a temporal decomposition algorithm is proposed to find initial feasible solutions. The last chapter focuses on a medium to long-term scheduling problem with embedded workforce management decisions. The classic formulation for multi-mode multi-skill resource-constrained project scheduling problem is extended to incorporate worker task learning and training dynamics simultaneously. A discretization scheme to model the nonlinear learning process resulting from job assignments is developed. Training of workers is enabled to acquire new skills. A mixed-integer programming formulation is introduced, and a sequential solution scheme is proposed. The trade-offs between project duration and workforce skill composition objectives are investigated.

Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.