Major League Baseball (MLB) is a 30-team North American professional baseball league and Minor League Baseball (MiLB) is the hierarchy of developmental professional baseball teams for MLB. Most MLB players first develop their skills in MiLB, and MLB teams employ scouts, experts who evaluate the strengths, weaknesses, and overall potential of these players. In this dissertation, we study the problem of designing a scouting network for a Major League Baseball (MLB) team. We introduce the problem to the operations research literature to help teams make strategic and operational level decisions when managing their scouting resources. The thesis consists of three chapters that aim to address decisions such as how the scouts should be assigned to the available MiLB teams, how the scouts should be routed around the country, how many scouts are needed to perform the major scouting tasks, are there any trade-off s between the scouting objectives, and if there are any, what are the outcomes and insights. In the first chapter, we study the problem of assigning and scheduling minor league scouts for Major League Baseball (MLB) teams. There are multiple objectives in this problem. We formulate the problem as an integer program, use decomposition and both column-generation-based and problem-specific heuristics to solve it, and evaluate policies on multiple objective dimensions based on 100 bootstrapped season schedules. Our approach can allow teams to improve operationally by finding better scout schedules, to understand quantitatively the strategic trade-offs inherent in scout assignment policies, and to select the assignment policy whose strategic and operational performance best meets their needs. In the second chapter, we study the problem under uncertainty. In reality we observe that there are always disruptions to the schedules: players are injured, scouts become unavailable, games are delayed due to bad weather, etc. We presented a minor league baseball season simulator that generates random disruptions to the scout's schedules and uses optimization based heuristic models to recover the disrupted schedules. We evaluated the strategic benefits of different policies for team-to-scout assignment using the simulator. Our results demonstrate that the deterministic approach is insufficient for evaluating the benefits and costs of each policy, and that a simulation approach is also much more effective at determining the value of adding an additional scout to the network. The real scouting network design instances we solved in the first two chapters have several detailed complexities that can make them hard to study, such as idle day constraints, varying season lengths, off days for teams in the schedule, days where some teams play and others do not, etc. In the third chapter, we analyzed a simplified version of the Single Scout Problem (SSP), stripping away much of the real-world complexities that complicate SSP instances. Even for this stylized, archetypal version of SSP, we find that even small instances can be computationally difficult. We showed by reduction from Minimum Cost Hamiltonian Path Problem that archetypal version of SSP is NP-complete, even without all of the additional complexity introduced by real scheduling and scouting operations.

With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide quality of services in maritime transportation are of great importance to both academia and the industry. This research investigates an optimization problem using evolutionary algorithms and big data analytics to address an important challenge in maritime disruption management, and illustrates how it can be engaged with information technologies and Internet of Things. Accordingly, in this thesis, we design, develop and evaluate methods to improve decision support systems (DSSs) in maritime supply chain management. We pursue three research goals in this thesis. First, the Vessel Schedule recovery Problem (VSRP) is reformulated and a bi-objective optimization approach is proposed. We employ bi-objective evolutionary algorithms (MOEAs) to solve optimization problems. An optimal Pareto front provides a valuable trade-off between two objectives (minimizing delay and minimizing financial loss) for a stakeholder in the freight ship company. We evaluate the problem in three domains, namely scalability analysis, vessel steaming policies, and voyage distance analysis, and statistically validate their performance significance. According to the experiments, the problem complexity varies in different scenarios, while NSGAII performs better than other MOEAs in all scenarios. In the second work, a new data-driven VSRP is proposed, which benefits from the available Automatic Identification System (AIS) data. In the new formulation, the trajectory between the port calls is divided and encoded into adjacent geohashed regions. In each geohash, the historical speed profiles are extracted from AIS data. This results in a large-scale optimization problem called G-S-VSRP with three objectives (i.e., minimizing loss, delay, and maximizing compliance) where the compliance objective maximizes the compliance of optimized speeds with the historical data. Assuming that the historical speed profiles are reliable to trust for actual operational speeds based on other ships' experience, maximizing the compliance of optimized speeds with these historical data offers some degree of avoiding risks. Three MOEAs tackled the problem and provided the stakeholder with a Pareto front which reflects the trade-off among the three objectives. Geohash granularity and dimensionality reduction techniques were evaluated and discussed for the model. G-S-VSRPis a large-scale optimization problem and suffers from the curse of dimensionality (i.e. problems are difficult to solve due to exponential growth in the size of the multi-dimensional solution space), however, due to a special characteristic of the problem instance, a large number of function evaluations in MOEAs can still find a good set of solutions. Finally, when the compliance objective in G-S-VSRP is changed to minimization, the regular MOEAs perform poorly due to the curse of dimensionality. We focus on improving the performance of the large-scale G-S-VSRP through a novel distributed multiobjective cooperative coevolution algorithm (DMOCCA). The proposed DMOCCA improves the quality of performance metrics compared to the regular MOEAs (i.e. NSGAII, NSGAIII, and GDE3). Additionally, the DMOCCA results in speedup when running on a cluster.

We provide a mathematical formulation of flight-specific delay cost functions that enables a detailed tactical consideration of how a given flight delay will interact with all downstream constraints in the respective aircraft rotation. These functions are reformulated into stochastic delay cost functions to respect conditional probabilities and increasing uncertainty related to more distant operational constraints. Conditional probabilities are learned from historical operations data, such that typical delay propagation patterns can support the flight prioritization process as a part of tactical airline schedule recovery. A case study compares the impact of deterministic and stochastic cost functions on optimal recovery decisions during an airport constraint. We find that deterministic functions systematically overestimate potential disruption costs as well as optimal schedule recovery costs in high delay situations. Thus, an optimisation based on stochastic costs outperforms the deterministic approach by up to 15%, as it reveals ‘hidden’ downstream recovery potentials. This results in different slot allocations and in fewer passengers missing their connections.