We consider the optimization problem of determining schedules for shipments on known paths within a freight transportation network in order to minimize vehicle transportation costs. We refer to this problem as the freight transportation network scheduling problem and present two mixed integer programming formulations of that problem. The first is based on the classical idea of a time-expanded network. The second formulation is based on sets of shipment consolidations. We show both analytically and computationally that the consolidation-based formulation is the superior of the two when all consolidations can be enumerated. However, we also show computationally that its enumerative nature renders it ineffective for instances with large numbers of shipments. Thus, we also present a column generation-based algorithm for solving the consolidation-based formulation that relies on solving relaxations that are integer programs. We demonstrate the superior performance of this algorithm with a computational study wherein we compare it against applications of state-of-the-art approaches from the literature. We also perform a detailed computational analysis of the performance of the algorithm in different settings. History: Accepted by Russel Bent, Area Editor for Network Optimization: Algorithms & Applications. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0435 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0435 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

We study the problem of determining how much finished goods inventory to source from different capacitated facilities in order to maximize profits resulting from sales of such inventory. We consider a problem wherein there is uncertainty in demand for finished goods inventory and production yields at facilities. Further, we consider that uncertainty in production yields is endogenous, as it depends on both the facilities where a product is produced and the volumes produced at those facilities. We model the problem as a two stage stochastic program and propose an exact, Benders-based algorithm for solving instances of the problem. We prove the correctness of the algorithm and with an extensive computational study demonstrate that it outperforms known benchmarks. Finally, we establish the value in modeling uncertainty in both demands and production yields. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: Software that implements the algorithms found in this paper, as well as the instances used in the computational study, can be found at Hewitt and Pantuso (2024) . The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0067 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0067 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Abstract This paper considers the integrated planning of goods transportation through a multi‐echelon supply chain consisting of a nationwide network and regional distribution system. The previously studied Service Network Design and Routing Problem considered similar planning decisions, albeit with multiple restrictions regarding the transportation of goods that can eliminate the opportunities for transportation savings. It also does not explicitly model the opportunity to increase vehicle utilization by having vehicles serve multiple purposes within the supply chain. We propose a mathematical model of the problem we consider that is inspired by the operations of an industrial partner. We present an adaptation of the Dynamic Discretization Discovery algorithm to solve this problem and illustrate its computational effectiveness on instances derived from the operations of a retail distribution network in France. Finally, we illustrate the potential savings enabled by solving the proposed model.

Abstract The transportation science research community has contributed to numerous practical and intellectual innovations and improvements over the last decades. Technological advancements have broadened and amplified the potential impacts of our field. At the same time, the world and its communities are facing greater and more serious challenges than ever before. In this paper, we call upon the transportation science research community to work on a research agenda that addresses some of the most important of these challenges. This agenda is guided by the sustainable development goals outlined by the United Nations and organized into three areas: (1) well‐being, (2) infrastructure, and, (3) natural environment. For each area, we identify current and future challenges as well as research directions to address those challenges.

Bidirectional dynamic programming is an algorithm that searches for paths in a network from both the starting and the ending nodes that optimize a given objective function. In recent years, bidirectional dynamic programming has been shown to be an effective means for solving resource‐bounded shortest path problems. While many researchers have observed that bidirectional A⋆ approaches perform poor computationally, we exploit the presence of resource constraints to overcome the source of these computational challenges. Our main contribution in this paper is an exact bidirectional A⋆ algorithm for resource‐constrained shortest path problems (RCSPPs) that is capable of solving large‐sized instances that challenge the state‐of‐the‐art in the literature. We also analyze, both computationally and theoretically, the sensitivity of the algorithm's performance to its inputs.

We develop an exact algorithm for integer programs that uses restrictions of the problem to produce high-quality solutions quickly. Column generation is used both for generating these problem restrictions and for producing bounds on the value of the optimal solution. The performance of the algorithm is greatly enhanced by using structure, such as arises in network flow type applications, to help define the restrictions that are solved. In addition, local search around the current best solution is incorporated to enhance overall performance. The approach is parallelized and computational experiments on a classical problem in network design demonstrate its efficacy.

We develop a solution approach for the fixed-charge network flow (FCNF) problem that produces provably high-quality solutions quickly. The solution approach combines mathematical programming algorithms with heuristic search techniques. To obtain high-quality solutions, it relies on neighborhood search with neighborhoods that involve solving carefully chosen integer programs derived from the arc-based formulation of FCNF. To obtain lower bounds, the linear programming relaxation of the path-based formulation of FCNF is used and strengthened with cuts discovered during the neighborhood search. The solution approach incorporates randomization to diversify the search and learning to intensify the search. Computational experiments demonstrate the efficacy of the proposed approach.