Pseudo-cost based branching is a popular branching strategy used by Mixed Integer Programming (MIP) solvers. This strategy relies on pseudo-cost updates from various parts of the search tree for making accurate branching decisions. Since such updates are not instantly available during a distributed computation, parallel MIP solver implementations that use pseudo-cost branching may not perform well when the underlying cluster is scaled horizontally. To address this issue, we propose integrating a repository of pre-calculated pseudo-costs into a parallel implementation of CPLEX. Although all the facilities needed for such an implementation are currently not available, experiments with hard-to-solve instances indicate that the proposed implementation can help limit the number of nodes explored during the distributed computation.
WEEE recycling is critical for global environmental governance and sustainable resource development, especially in developing countries where the coexistence of weak formal sectors and rampant informal recycling leads to resource waste and severe pollution. Existing research predominantly focuses on government subsidies for formal recyclers, yet it overlooks the integration of governance of informal sectors. This study constructs a dual-channel (formal/informal) price competition model incorporating consumer environmental awareness, systematically evaluating the effects of government subsidy policies and integration governance policies on recycling volumes, resource reuse efficiency, and environmental benefits. The findings indicate that government subsidy policies are constrained by a threshold: their effectiveness is significantly limited if subsidy intensity fails to bridge the processing cost gap between formal and informal sectors. Conversely, the implementation of integration governance policies requires that informal recyclers' profits under such policies are not lower than those under government subsidies. Policy selection depends on dynamic matching between government investment and consumer environmental awareness: with low investment, integration governance outperforms in regions with high environmental awareness; with sufficient investment, subsidies yield more comprehensive benefits. Furthermore, an extended "subsidy + integration" synergistic policy is proposed to leverage the advantages of both approaches, achieving full resource utilization and stable environmental benefits.
The rapid development of e-commerce has prompted firms with logistics advantages to establish self-run logistics. However, ambiguity remains regarding whether firms with logistics disadvantages should join shared logistics alliances. To address this issue, this study investigates the optimal logistics choices of two competing firms through a game theory model. The results indicate that TPL has an incentive to enhance green technology levels and reduce logistics capacity constraints. Furthermore, when the TPL's green technology level is low, the firm with logistics advantages is more likely to establish self-run logistics. Under certain conditions, the firm with logistics disadvantages joins the shared logistics alliance, leading to a win-win outcome for both parties. Notably, when the firm with logistics advantages has a low (high) unit environmental impact, the firm with logistics disadvantages choosing to join the shared logistics alliance will result in the smallest (largest) environmental impact, with social welfare reaching the highest (lowest) level. Therefore, the government must encourage firms with logistics advantages to invest in green technologies to reduce unit environmental impact and promote the establishment of shared logistics alliances to enhance environmental sustainability and social welfare. This research aims to provide theoretical guidance for firms' logistics choices and the government's policy guidelines.
Setting research and development (R&D) output targets is a critical managerial task. Data envelopment analysis (DEA), a classical tool for constructing production possibility sets and frontiers, can support target setting. However, conventional DEA is designed for ex-post analysis and lacks predictive capability, which limits its suitability for forward-looking R&D target setting. In addition, DEA typically follows an efficiency-maximization principle, often producing overly ambitious targets that are difficult to achieve in the short term. To address these limitations, this paper integrates artificial neural network (ANN) with DEA to enhance predictive capability and enable ex-ante analysis. It also introduces a gradual efficiency improvement principle, recognizing that efficiency gains in practice are usually incremental rather than immediate. This principle allows the degree of efficiency improvement to be flexibly controlled, making target setting more realistic and manageable. The proposed approach is applied to 3,660 Chinese listed companies. When 2022 data are used to predict the R&D output targets required to achieve different efficiency-improvement levels in the following year, the results demonstrate that the approach provides effective support for practical R&D target setting.
