Search for: All records

We study highway-based shipping of preowned automobiles by auto carriers, an important although overlooked problem in the automobile shipping literature. The special structure associated with auto carriers implies many different ways of loading a set of automobiles to an auto carrier with different loading costs. Thus, in addition to vehicle routing decisions, loading decisions are essential in automobile shipping optimization. The objective of our problem is to maximize the total revenue minus the total routing and loading cost subject to time windows and loading constraints among others. Most existing automobile shipping studies treat loading and routing separately; some studies partially address the loading aspect in routing optimization but only check the loading feasibility without evaluating the quality of loading decisions. We, thus, contribute to the literature by fully integrating loading decisions into routing decision making. An integrated machine learning (ML) and optimization approach is proposed to solve the problem. The overall approach follows a column generation–based solution framework, in which an insertion heuristic is proposed to find new routes based on existing routes, and ML is employed to predict the loading feasibility and estimate the minimum loading cost of a given route without solving the complex loading optimization problem. The integration of the ML approach and the insertion heuristic enables us to find high-quality new routes quickly in each column generation iteration. Two variants of this integrated approach are evaluated against a benchmark sequential approach in which routing and loading are tackled separately and another benchmark approach in which routing and loading are optimized jointly without using ML. Computational experiments demonstrate that the proposed integrated ML and optimization approach generates significantly better solutions than the sequential benchmark approach with only slightly more computation time and similar solutions to the joint optimization benchmark approach but with significantly less computation time. The proposed solution approach can be adopted by automobile shipping companies. It can also be adapted for other joint optimization problems, such as those in aircraft load planning. Funding: Y. Sun is partially supported by the National Science Foundation [Grants 2332161, 2100745, and 2055347]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0712 . 

This paper seeks to improve an underutilized conventional bus route by converting it into a semiflexible transit system where passengers provide advance notice of their intended stops, allowing buses to skip downstream stops without demand by taking shortcuts. This approach increases stop density, reduces walking distances to and from bus stops, and maintains operational efficiency. To design this system, we develop optimization models that maximize the number of stops while adhering to tour duration and arrival time constraints. A case study in Allegany County, Maryland, demonstrates significant enhancements for routes that were both underutilized (where the probability of a stop lacking demand exceeded 45%) and had layouts conducive to substantial shortcuts. In these instances, the number of stops can be increased by up to 160%, with the actual improvement depending on route configuration, passenger demand, and advance notice requirements. Funding: Financial support from the the National Science Foundation [Grant 2055347] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0561 . 

In this paper, we aim to address a relevant estimation problem that aviation professionals encounter in their daily operations. Specifically, aircraft load planners require information on the expected number of checked bags for a flight several hours prior to its scheduled departure to properly palletize and load the aircraft. However, the checked baggage prediction problem has not been sufficiently studied in the literature, particularly at the flight level. Existing prediction approaches have not properly accounted for the different impacts of overestimating and underestimating checked baggage volumes on airline operations. Therefore, we propose a custom loss function, in the form of a piecewise quadratic function, which aligns with airline operations practice and utilizes machine learning algorithms to optimize checked baggage predictions incorporating the new loss function. We consider multiple linear regression, LightGBM, and XGBoost, as supervised learning algorithms. We apply our proposed methods to baggage data from a major airline and additional data from various U.S. government agencies. We compare the performance of the three customized supervised learning algorithms. We find that the two gradient boosting methods (i.e., LightGBM and XGBoost) yield higher accuracy than the multiple linear regression; XGBoost outperforms LightGBM while LightGBM requires much less training time than XGBoost. We also investigate the performance of XGBoost on samples from different categories and provide insights for selecting an appropriate prediction algorithm to improve baggage prediction practices. Our modeling framework can be adapted to address other prediction challenges in aviation, such as predicting the number of standby passengers or no-shows.