Gui-Hua Lin

1. Confronting the grave and escalating challenge of climate crisis, nations are increasingly adopting diverse policy instruments to regulate carbon emissions, promote green technology development ...

Low-carbon transformation requires substantial investments, challenging capital-constrained manufacturers to adopt carbon emission reduction (CER) technologies. While external financing alleviates capital shortages, it cannot address potential profit imbalances that trigger fairness concerns. We investigate a low-carbon supply chain where a capital-constrained manufacturer adopts CER technologies via a preferential bank loan and sells to a capital-abundant retailer. Unlike prior studies treating CER investments as one-time costs, we model CER technology as a quadratic per-unit royalty licensing fee. We find that, given consumers’ willingness to pay for low-carbon products, financing encourages CER upgrades but creates profit disparities unfavorable to the retailer. Incorporating the retailer’s fairness concerns, results show that compared to the non-fairness scenario, the manufacturer sets a lower wholesale price and cannot earn more. Conversely, the retailer strategically maintains or increases its order quantity, attaining higher profits. Furthermore, the optimal CER level remains invariant regardless of fairness preferences. Finally, supply chain coordination is achievable under specific conditions, yielding a win–win outcome where the manufacturer adopts CER technologies and the retailer’s fairness concerns are accommodated. The quadratic per-unit technology licensing fee we investigated maintains the manufacturer’s motivation and ensures the retailer’s fairness, contributing to the stable and sustainable evolution of low-carbon supply chains.

Post-disaster relief distribution is frequently challenged by uncertain demands, disrupted road networks, adverse weather conditions, and dynamically changing regional emergency. To cope with these challenges, we propose a stochastic optimization framework for a priority-driven truck-drone collaborative rescue system. Firstly, a Spatio-Temporal Deep Attentional Embedded Graph Clustering method is constructed, which integrates spatial network topology and exogenous delay deprivation index to achieve dynamic identification of priority levels of the affected areas. Based on the classification results, a two-stage stochastic programming model is formulated to jointly minimize operational costs and relief shortage losses by considering fairness. To solve large scale problem involving multiple scenarios, an $L_1$-penalty Progressive Hedging Algorithm is proposed to avoid mixed integer quadratic sub-problems caused by traditional quadratic penalty method and an adaptive penalty update mechanism is introduced to improve convergence efficiency and stability. A case study based on the 2010 Yushu earthquake verifies the applicability of the proposed framework. The results show that the proposed algorithm may obtain high-quality solutions and demonstrates superior scalability compared to directly by the CPLEX solver. Sensitivity analysis further validates the impact of key parameters on the algorithm performance and rescue decisions.

This paper considers a bilevel program. To solve this bilevel program, it is generally necessary to transform it into some single-level optimization problem. One approach is to replace the lower-level program by its KKT conditions to transform the bilevel program as a mathematical program with complementarity constraints (MPCC). Another approach is to apply the lower-level Wolfe/Mond-Weir/extended Mond-Weir duality to transform the bilevel program into some duality-based single-level reformulations, called WDP, MDP, and eMDP, respectively, in the literature. In this paper, inspired by a conjecture from a recent publication that the tighter feasible region of a reformulation, the better its numerical performance, we present five new duality-based single-level reformulations, called TWDP/TMDP/eTMDP/ETMDP/eETMDP, with tighter feasible regions. Our main goal is to compare all above-mentioned reformulations by designing some direct and relaxation algorithms with projection and implementing these algorithms on 450 test examples generated randomly. Our numerical experiments show that, whether overall comparison or pairwise comparison, at least in our tests, in terms of dominant cases and objective values, WDP/MDP/TWDP/TMDP/ETMDP were always better than MPCC, whereas eMDP/eTMDP/eETMDP were always the worst ones among eight duality-based reformulations, which indicates that the above conjecture is incorrect. In particular, for the relaxation algorithms, WDP/MDP/TWDP/TMDP performed four to five times better than MPCC, whereas eMDP/eTMDP/ETMDP/eETMDP performed at least 1.8 times better than MPCC in terms of dominant cases. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This research was supported by the National Natural Science Foundation of China [Grants 12571324, 72501169, and 72394365], the China Postdoctoral Science Foundation [Grant 2024M761920], and the Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation [Grant GZC20250524]. 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.2025.1159 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1159 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

This repository aims to provide the source code and numerical examples used to illustrate the effectiveness of the approaches developed in the paper "Theoretical and Numerical Comparison of Nine Single-Level Reformulations for Bilevel Programs."