Jean-François Cordeau

The procurement of freight services is an important element for the supply chain management of a shipper (i.e., a manufacturer or retailer) that sources transportation services from the third-party logistics market. Motivated by a practical freight procurement problem faced by shippers, we provide a holistic approach to designing freight procurement strategies for transportation-inventory systems that captures the interconnections among freight procurement, transportation, and inventory management. In view of supply and demand uncertainties, we consider the problem on the basis of a multistage decision process with uncertain data. To handle instances of realistic size, we propose an enhanced stochastic dual dynamic programming solution approach. We conduct extensive numerical experiments to test the performance of the approach. The results demonstrate that our approach scales to very large instances with up to [Formula: see text] scenarios and that the proposed enhancement strategies significantly improve its performance. Compared with methods commonly adopted to solve similar problems, our approach achieves significant cost improvements based on generated instances calibrated from established benchmark instances and stochastic simulations. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72301230, 72288101, and 72322022]; the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant 25223223]; and the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China [Grant JYB2025XDXM211]. 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.2025.1141 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1141 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The routing-and-driving problem for plug-in hybrid electric vehicles (PHEVs) is an extension of the vehicle routing problem with time windows, where routing involves determining optimal routes and recharging decisions for a fleet of PHEVs, whereas driving involves choosing the speed and operating mode on each road segment traveled by a vehicle. Specifically, four driving modes are considered: fuel only, electricity only, a combination of fuel and electricity, and energy recuperation, which returns energy to the battery. We consider two variants of the problem where the speed variables are either continuous, which results in a nonlinear model, or discrete, which represents the case when speeds are chosen from a predetermined set. To solve these two models, we propose a set of valid inequalities to strengthen the continuous linear programming relaxation, and we use a tailored branch-and-cut algorithm. Extensive computational experiments demonstrate the efficiency of the proposed solution methods, which can optimally solve instances with a realistic number of customers and recharging stations. In addition, we show that incorporating speed optimization can significantly reduce the energy consumption costs of a PHEV fleet compared with using fixed speeds. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the GuangDong Basic and Applied Basic Research Foundation [Grant 2026A1515030009]. 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.2024.1016 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.1016 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper The Routing-and-Driving Problem for Plug-in Hybrid Electric Vehicles by Fuliang Wu, Yossiri Adulyasak, Jean-François Cordeau. This snapshot corresponds to the version used in the published paper.

Rolling stock scheduling and crew scheduling are two fundamental problems that arise in the planning of urban rail operations and that are especially important in the case of flexible operations in real-world networks. These problems are often solved separately and sequentially in different planning stages, resulting in limited options to adjust crew schedules after rolling stock decisions have been made. To better adjust these two decision-making processes and achieve better solutions, this paper studies a joint rolling stock and crew scheduling problem in urban rail networks. A novel optimization model is formulated with the aim of reducing the operational cost of rolling stock units and crew members. In addition, the multi-train composition mode is considered to adequately match different frequency requirements and rolling stock transport capacities. To solve the model, a customized branch-and-price-and-cut solution algorithm is proposed to find the optimal schedule schemes, in which Benders decomposition is used to solve the linear programming relaxation of the path-based reformulation. Two customized column generation methods with label correcting are embedded to solve the master problem and pricing subproblem for generating paths (columns) corresponding to rolling stock units and crew groups, respectively. Finally, a branch-and-bound procedure with several acceleration techniques is proposed to find integer solutions. To demonstrate the computational performance and the robustness of the proposed approaches, a series of numerical experiments are performed in real-world instances of the Beijing urban rail network under different settings. The computational results confirm the high efficiency of the solution methodology and the benefits of the flexible operation schemes based on the solutions found by the proposed methods. Funding: This work was supported by National Natural Science Foundation of China [Grants 72288101, 72322022, 72371015]. The first author sincerely thanks the China Scholarship Council for supporting his visiting PhD program [Grant 202407090173]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2024.0905 .

This paper studies a flow hub location problem (FHLP) stemming from recent trends in network design for e-commerce businesses. Specifically, e-commerce companies are flexible and agile in reoptimizing their logistics networks, including supplier (origin) and customer zone (destination) decisions. Furthermore, a large number of commodities (flows) and a relatively small sales volume for each product incentivize e-commerce retailers to lease warehouse spaces as hubs, yielding a large number of hub location candidates. As such, the proposed FHLP determines the origin and destination of each flow simultaneously with the hub location and flow routing decisions in contrast to the classical hub location problems, where the origins and destinations of all flows are predetermined. To solve this large-scale optimization problem, we propose an optimization algorithm that combines Lagrangian relaxation and Benders decomposition. Novel acceleration techniques, such as a clustering-empowered multicommodity Benders reformulation, learning-empowered elimination tests, and variable reduction techniques, are further developed to improve the performance and convergence of the algorithm. The efficiency of the proposed algorithm is evaluated via extensive computational experiments. The numerical results show that when compared with five other benchmark methods, the proposed algorithm can achieve optimal solutions faster for small-sized test instances and reduce optimality gaps for large-sized ones. For example, the proposed method achieves optimal solutions for a set of 10 test instances, with node sizes ranging from 225 to 450, within 20 minutes on average. In comparison, the automatic Benders decomposition method implemented in the commercial CPLEX solver achieves an average optimality gap of 2% within one hour. History: Accepted by Russell 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.2023.0367 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0367 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

In this paper, we study the intermodal hub network design problem with probabilistic service-level constraints ensuring that total service time requirements of customers’ orders are satisfied with a minimum probability. The intermodal network is modeled as a Jackson queueing network with [ ] queues for the hubs and [ ] queues for transport operations. We characterize the total service time distribution and propose a cutting-plane algorithm that exploits the characteristics of this distribution. We show that the α-level sets of the total sojourn time distribution for a transport path including two or more hubs are homothetic with some homothetic center. This characteristic allows for the derivation of valid inequalities leading to significant reductions in the solution time. We propose a worst-case renewal approximation considering [ ] queues to extend our analysis to non-Jackson networks. We prove that the properties of the total sojourn time distribution derived for Jackson networks also hold for this renewal approximation, allowing the application of the derived cutting-plane approach to the general case. Extensive computational experiments are performed on the Australian Post and Colombian data sets to assess the performance of the proposed formulations and solution algorithms. Funding: The authors acknowledge support from the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0657 .