The software and data in this repository are a snapshot of the software and data that were used in the research reported in the paper Branch-and-Price for the Team Orienteering Problem with Time Windows and Bivariate Time-Varying Profit by Y. Hu, X. Luo, X. Jiang, R. Yan, and Z. Zhang.
Machine learning and data science techniques have been widely used in practice and have received significant attention. In research settings, business scholars have begun to adopt machine learning as methodological tools. This paper reviews the literature that leverages machine learning-generated measurements to support empirical analysis and yield business insights, where machine learning can reduce reliance on human coding or enable new theoretical perspectives. We summarize the workflow, constructs, and measure structures in these machine learning measure-supported empirical studies. We believe machine learning holds substantial potential to advance business research. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the Shanghai Pujiang Program, Shanghai Municipal Human Resources and Social Security Bureau [Grant 23PJC097]; Research Grants Council, University Grants Committee [Grants 11500625 and 11501722]; and National Natural Science Foundation of China [Grants 72301067 and 72502212]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2024.0918 .
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/ .
We study the optimal pricing problem for operations at a seaport terminal within a multimodal transport network, where the terminal operator sets differentiated prices for processing cargoes arriving via and transferred to various modes of transport. The problem incorporates two critical features: (i) the influence of port congestion on cargo suppliers’ terminal selection and (ii) the hierarchical choice structure that predicts how cargo suppliers select transport modes and routes for their cargoes. To capture these features, we formulate the problem as a nonconvex, nonlinear program involving a nested logit choice model and an equilibrium constraint reflecting the congestion effect, which is challenging to solve or analyze. To address this difficulty, we apply a change of variables to reformulate the price-based model as a flow-based model, where cargo flows, rather than prices, are the decision variables, thereby making the model more amenable to analysis. By exploiting the structure of the flow-based model, we derive an optimality equation whose dimension scales only with the number of origin-destination (O/D) pairs, which effectively reduces the computational complexity by avoiding directly handling the numerous variables associated with transport routes. This approach not only applies to seaport revenue management problems that often involve multiple transport modes and a substantial number of routes, but also contributes a structure-exploiting method for addressing a broader class of nonlinear programs involving equilibrium constraints and customer choice models. We show that, for each O/D pair, the optimal mode-dependent prices decompose into a component common across modes plus a mode-specific adjustment. We also establish analytical bounds on the revenue loss induced by misspecifying cargo suppliers’ choice structure. We evaluate the performance of the algorithm by comparing it with several widely used benchmark methods through numerical analysis. Our findings indicate that our algorithm outperforms the benchmark methods in both revenue maximization and runtime. Moreover, we find that accurately modeling the choice structure has a larger impact on revenue than explicitly modeling congestion. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: X. Wang received financial support from the National Science Foundation [Award CMMI-2127779]. 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.2025.1238 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1238 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
The problem of covering a two-dimensional region with identical circles of minimal radius (CRICMR) is a classic optimization problem with many relevant real-world applications. However, due to the nonconvexity and the multiply-connected nature of the region to be covered, designing an efficient formulation and solution approach for the problem is very challenging. In this study, we first propose a unified optimization model for two classic variants of the problem (i.e., the CRICMR problems with and without center placement constraints), and then present an effective heuristic algorithm for solving both variants. The proposed model is based on the clipped Voronoi diagram and converts the intractable region coverage problem into the much easier vertex covering problem with respect to the clipped Voronoi polygons. The proposed algorithm incorporates several key components: a two-phase monotonic basin-hopping method; a subregion-based local optimization method; two perturbation operators, and a radius adjustment method for the covering circles. Computational experiments on popular benchmark instances demonstrate the efficiency of our approach, which significantly outperforms state-of-the-art methods by improving the current best-known solutions for nearly all benchmark instances. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 72571149, 72588101, and 72471100]. 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.2025.1649 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1649 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
