Browsing by Subject "Transportation"

Across multiple industries (e.g., railroads, airlines, on-demand air taxi services), there are growing investments in future automated transportation systems. Even with these investments, there are still significant human-systems engineering challenges that require deeper investigation and planning. Specifically, fleets that include new levels of automation may require new concepts of how to design and staff network operations centers. Network operations centers have existed for over a century in the railroad and airline industries, where dispatchers have played a central role in safely and efficiently managing networks of railroads and flights. With operators in such safety-critical and time-sensitive positions, workload is the key indicator of their performance in terms of accuracy and efficiency. Yet, there are few tools available for decision-makers in these industries to explore how increasing levels of automation in fleets and operations centers may ultimately affect dispatcher workload.

Thus, this thesis presents a model of dispatcher workload. While automation may be the most pressing change in transportation industries, 10 variables related to configurations of the fleet and the operations center and how those variables interact to influence dispatcher workload were defined. These ten variables come from fleet conditions, strategic design factors, tactical staffing factors, and operational factors. A discrete event simulation was developed to computationally model dispatcher workload with over 10^18 possible configurations of these variables. Additionally, using time-based metrics and integrating results from a prior human reliability assessment, the simulation predicts human error on tasks.

A multi-level validation strategy was developed to build internal, external, and general confidence in using the dispatcher workload model across different domains with data from freight railroad, commuter railroad, and airline operations. In the process of developing and validating the workload model, several other research contributions were made to the field. Eighty-five probability density functions of dispatcher task inter-arrival and service time distributions were generated in the three domains. A data collection tool, Dispatcher’s Rough Assessment of Workload-Over Usual Times (DRAW-OUT), was designed to gather empirical dispatcher-generated estimates of utilization, the proxy for workload, throughout their shifts.

Using the model, experiments were conducted to analyze the sensitivity of dispatcher workload and performance to changes in different parameters. The size of the fleet a dispatcher managed was found to be the most significant factor out of all the other internal parameters. On the other hand, shift schedule, environmental conditions, and operator strategy were the parameters found to have the smallest influence on dispatcher performance. The model was also used to investigate future scenarios that managers could not previously explore due to limitations of time and resources. Results show that the general model is applicable for use in simulating dispatcher workload in both freight and commuter railroad operations as well as airline operations, including short- and long-haul flights, in present-day and future cases.

General confidence was built in the workload model and the Simulator of Humans & Automation in Dispatch Operations (SHADO) was developed as an online platform to provide open access to the underlying discrete event simulation. SHADO is a novel tool that allows stakeholders, including operational managers, to rapidly prototype dispatch operations and investigate human performance in any transportation system. With several theoretical and practical contributions, this work establishes the foundation for future research in the growing field of advanced transportation network operations.

This dissertation studies the optimal design and performance evaluation of large-scale supply chain systems with graphical structures such as assemble-to-order (ATO) systems and e-commerce fulfillment networks. It consists of three essays.

The first essay studies the design of effective operational policies in the assemble-to-order (ATO) systems. We consider continuous-review ATO systems with general bills of materials (BOM) and general leadtimes. First, we characterize the asymptotically optimal policy for the M-system. The policy consists of a periodic review priority (PRP) allocation rule and a coordinated base-stock (CBS) replenishment policy. We then construct heuristic policies using insights from the asymptotically optimal policy. In particular, we adopt the PRP allocation rule and develop a decomposition approach for inventory replenishment. This approach decomposes a general system into a set of assembly subsystems and constructs a linear program to compute the optimal policy parameters. However, both the CBS and the assembly decomposition approach are limited to simple systems. We then consider a second approach, which decomposes a system into a set of distribution subsystems and each subsystem has a straightforward optimal solution, which is similar to the newsvendor problem. Finally, in a numeral test, we find that the assembly decomposition is very effective but computationally expensive and thus only good for small-scale systems; the distribution decomposition performs as effective as the optimal independent base-stock (IBS) policy, but is highly scalable than finding the optimal IBS policy for large-scale systems.

The second essay focuses on optimizing the operational decision at one layer of the supply chain network when some operational decisions at another layer are unknown to the decision-maker. More specifically, we consider the transportation network design problem for the e-commerce marketplace. A salient feature in this problem is decentralized decision-making. While the middle-mile manager decides the network configuration on a weekly or bi-weekly basis, the real-time flows of millions of packages on any given network configuration (which we call the flow response) are controlled by a fulfillment policy employed by a different decision entity. Thus, we face a fixed-cost network design problem with unknown flow response. To meet this challenge, we first develop a predictive model for the unknown response leveraging observed shipment data and machine learning techniques. Apart from the most natural network-level predictive model, we find that the more parsimonious destination-level and arc-level predictive models are more effective. We then embed the predictive model to the original network design problem and characterize this transformed problem as a c-supermodular minimization problem. We develop a linear-time algorithm with an approximation guarantee that depends on c. We demonstrate that this algorithm is scalable and effective in a numerical study.

The third essay investigates how to use the Graph Neural Network (GNN) model to predict the operational performances of supply chain networks. GNN is a newly developed machine learning tool to leverage the graphical structure information. It has demonstrated good prediction accuracy in various contexts, including social, bioinformatics and citation networks. Surprisingly, GNNs have not received much attention in supply chain systems despite the fact that many systems exhibit a graphical structure, such as assemble-to-order systems and process flexibility networks. To the best of our knowledge, we are the first to explore the application of GNN to supply chain problems. As operational performances of the entire network can often be decomposed into node-level or edge-level performances, we study both node-level and edge-level predictions. We find that while the existing GNN model can generate reasonable node-level predictions, special tailoring is needed for edge-level predictions of supply chain networks. A key contribution of our research is to develop a novel graph transformation approach, which allows an edge to learn from its neighborhood edges. Tested on different synthetic datasets from two different supply chain systems, we implement the GNN model with our proposed graph transformation and several benchmark methods, including an existing GNN model and the traditional machine learning methods, such as the convolutional neural network and random forest. The results indicate that our approach significantly outperforms the benchmarks in edge-level prediction. We also observe the importance of utilizing the graphical structure and edge directions. Our comparison reveals that it is beneficial to start with node-level or edge-level predictions and then aggregate them together for the graph-level prediction, instead of the direct graph-level prediction commonly used in other applications.

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