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.

Environmental problems are more abundant because of the rapid increase in urbanization, climate change, and population growth leading to the depletion of natural resources and endangerment of some species. The availability of infrastructure as well as socio-economic factors facilitate the illicit trade of wildlife through supply chain networks, adding further threats to species. Ecosystem conservation and protection of wildlife from illegal trade and poaching is fundamental to guarantee the survival of endangered species. Conservation efforts require a landscape approach that incorporates spatial features for the effective functionality of the selected reserve. This dissertation studies combinatorial optimization problems with application to two classes of societal problems: landscape conservation and disruption of illicit supply chains. The first and second chapter propose a mixed-integer formulation to model the reserve design problem with budget and ecological constraints. The first uses the radius of the smallest circle enclosing the selected areas as a metric of compactness. An extension of the model is proposed to solve the multi reserve design problem and the reserve expansion problem. The solution approach includes warm start heuristic, separation problem and cuts to improve model performance. The enhanced model outperforms the linearized and the equivalent nonlinear model. The second chapter uses the Reock’s metric as a metric of compactness. The solution approach includes warm start heuristic, knapsack based separation problem to inject solutions, and cuts to improve model performance. The enhanced model outperforms the default model. The third chapter proposes an integer programming model to solve the wildlife corridor design problem with minimum width requirement and a budget constraint. A separation algorithm is proposed to identify boundary patches and violations in the corridor width. A branch-and-cut approach is proposed to induce the corridor width and is tested on real-life landscape. The fourth chapter proposes an integer programming formulation to model the disruption of illicit supply chain problem. The proposed model enforces that at least x paths must be disrupted for an Origin-Destination pair to be disrupted and at least y arcs must be disrupted for a path to be disrupted. The proposed model is tested on real-life road networks.

The stable and efficient operation of the transmission network is fundamental to the power system’s ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Furthermore, they contribute to the growing frequency and ferocity of devasting weather events. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider flexible investment options (e.g., transmission switching, capacity expansion, etc.) to improve the functionality and efficiency of the grid. Increased flexibility, however, requires many discrete choices, rendering fully accurate models intractable. This dissertation derives several classes of structural valid inequalities and employs them to accelerate the solution process for each of the proposed expansion planning problems. The valid inequalities leverage the variability of the cumulative capacity-reactance products of parallel simple paths in networks with flexible topology, such as those found in transmission expansion planning problems. Ongoing changes to the climate and weather will have vastly differing impacts a regional and local scale, yet these effects are difficult to predict. This dissertation models the long-term and short-term uncertainty of rising temperatures and severe weather events on transmission network components in both stochastic and robust mixed-integer linear programming frameworks. It develops a novel test case constructed from publicly available data on the Arizona transmission network. The models and test case are used to test the impacts of climate and weather on regional expansion decisions.

This study aims to combine the wisdom of crowds with ML to make more accurate stock price predictions for a select set of stocks. Different from prior works, this study uses different input elicitation techniques to improve crowd performance. In addition, machine learning is used to support the crowd. The influence of ML on the crowd is tested by priming participants with suggestions from an ML model. Lastly, the market conditions and stock popularity is observed to better understand crowd behavior.

Computational social choice theory is an emerging research area that studies the computational aspects of decision-making. It continues to be relevant in modern society because many people often work as a group and make decisions in a group setting. Among multiple research topics, rank aggregation is a central problem in computational social choice theory. Oftentimes, rankings may involve a large number of alternatives, contain ties, and/or be incomplete, all of which complicate the use of robust aggregation methods. To address these challenges, firstly, this work introduces a correlation coefficient that is designed to deal with a variety of ranking formats including those containing non-strict (i.e., with-ties) and incomplete (i.e., unknown) preferences. The new measure, which can be regarded as a generalization of the seminal Kendall tau correlation coefficient, is proven to satisfy a set of metric-like axioms and to be equivalent to a recently developed ranking distance function associated with Kemeny aggregation. Secondly, this work derives an exact binary programming formulation for the generalized Kemeny rank aggregation problem---whose ranking inputs may be complete and incomplete, with and without ties. It leverages the equivalence of minimizing the Kemeny-Snell distance and maximizing the Kendall-tau correlation, to compare the newly introduced binary programming formulation to a modified version of an existing integer programming formulation associated with the Kendall-tau distance. Thirdly, this work introduces a new social choice property for decomposing large-size problems into smaller subproblems, which allows solving the problem in a distributed fashion. The new property is adequate for handling complete rankings with ties. The property is leveraged to develop a structural decomposition algorithm, through which certain large instances of the NP-hard Kemeny rank aggregation problem can be solved exactly in a practical amount of time. Lastly, this work applies these rank aggregation mechanisms to novel contexts for extracting collective wisdom in crowdsourcing tasks. Through this crowdsourcing experiment, we assess the capability of aggregation frameworks to recover underlying ground truth and the usefulness of multimodal information in overcoming anchoring effects, which shows its ability to enhance the wisdom of crowds and its practicability to the real-world problem.