This paper proposes an innovative procedure for designing efficient biomass-biofuel logistics networks (BBLNs). This procedure is based on the two-stage network data envelopment analysis (TSN-DEA) models that have been developed to provide specific process guidance for the managers to improve the efficiency of the decision-making unit (DMU) with the TSN process. The crucial issue of the TSN-DEA is that the overall efficiency score depends on the DMUs under evaluation. Thus, the rankings for the DMUs generated by the TSN-DEA model are inconsistent. As a result, the TSN-DEA-based ranking methods are limited. The TSN-DEA’s inconsistency frequently makes it difficult for decision-makers to select the top-rated DMUs. We develop the transformed TSN (T-TSN) DEA method by applying the multi-criteria DEA model to overcome this issue. The proposed method transforms the DMUs with any number of inputs, intermediate measures, and outputs in the TSN process, through the multi-objective programming model with a minimax objective approach, into the DMUs with two inputs and one output in the single-stage network (SSN) process. Then, the well-known DEA methods for the SSN, such as the cross-efficiency and super-efficiency DEA methods, can be applied to evaluate and rank the transformed DMUs more consistently. We exhibit the applicability of the proposed approach for the BBLN design problem. A case study of South Carolina in the USA demonstrates that the proposed method performs well in identifying efficient BBLN schemes more consistently than the traditional TSN-DEA.

Cause-related marketing (CRM), as an organic combination of marketing and corporate social responsibility (CSR), has been widely used in the supply chain. However, the existing literature rarely studies the CRM strategy the in the supply chain. This paper explores the pricing decisions and CRM strategy of supply chain members by examining a supply chain system consisting of a manufacturer and a retailer, where the manufacturer produces two quality differentiated products. By developing a Stackelberg model for three scenarios, including the No CRM strategy, CRM strategy for the high-quality product, and CRM strategy for the low-quality product, this paper finds that the CRM strategy will result in higher wholesale and sales prices for the cause-related product. In addition, consumers’ pro-sociality and the degree of product quality differentiation are critical to the manufacturer’s choice of CRM strategy. When the quality difference differs significantly, the manufacturer should implement CRM for the high-quality product in a market with low consumer pro-sociality and for the low-quality product in a market with high consumer pro-sociality; when the product quality difference is slight, the manufacturer should choose to implement CRM for the low-quality product regardless of consumer’s pro-sociality. Furthermore, the model is extended to that the retailer implements the CRM strategy and a retailer-led supply chain. The results indicate that CRM strategy in the supply chain is not influenced by the implementing entity or the supply chain leader.

Due to its harmful nature, any incident associated with hazardous material (hazmat) may cause tremendous impacts on the surrounding people and the environment. Focusing on the incident involving this specific type of good, we develop a reliable and robust emergency logistics network that considers both demand uncertainty and possible unavailability of particular links. A time-based risk measure is carefully designed upon the traditional risk assessment to reflect the stakeholder’s sensitivity to risk over response time. The disruption and uncertainty are modeled as two sets of scenarios which are integrated into a bi-objective robust model to evaluate the trade-offs between risk and cost. The effectiveness of the emergency response can be assured by expenditures that add extra capacities to certain links or establish additional facilities that aid recovery from incidents. We apply our model and approach to a real-world network in Guangdong China. Analytical results reveal the necessity of embedding consideration of uncertainty and unreliability into emergency network design problems; outline the importance of hedging against unpredictability by system redundancies; and indicate the impact of stakeholder’s orientation towards cost and risk on the location, allocation, and routing decisions in hazmat emergency response.

As the main channel for people to obtain information and express their opinions, online media generate a huge amount of unstructured news documents every day and make it difficult for people to perceive major societal events and grasp the evolution of events. Previous studies on storyline generation are generally based on document clustering without considering event arguments and relations between events. Event-centric knowledge graph has been used to facilitate the construction of news documents to form structured event representation. Although some studies have attempted to construct timelines based on event-centric knowledge graphs, it is difficult for timelines to depict the complex structures of event evolution. In this paper, we try to represent news documents as an event-centric knowledge graph, and compress the whole knowledge graph into salient complex events in temporal order to generate storylines named narrative graph. We first collect news documents from news platforms, construct an event ontology, and build an event-centric knowledge graph with temporal relations. Graph neural network is used to detect events, while BERT fine-tuning is leveraged to identify temporal relations between events. Then, a novel generation framework of narrative graph with constraints of coherence and coverage is proposed. In addition, a case study is implemented to demonstrate how to utilize narrative graph to analyze real-world event. The experiment results show that our approach significantly outperforms the baseline approaches.

Many firms have already been actively or passively involved in sustainable supply chain management with the objective of improving the triple bottom line (TBL). But whether the limited funds should be allocated to both community responsibility activities (e.g., corporate philanthropy) and environmental protection activities (e.g., recycling) is a confusing question. This paper provides deep insights into the combination strategy of two corporate social responsibility (CSR) types in a two-tier sustainable supply chain by modeling analysis. The decision models in eight scenarios with different combinations of CSR types are proposed and applied for the determination of equilibrium scenarios. The paper’s findings highlight: under certain conditions, (1) the supply chain with two types of CSR is the equilibrium scenario and improves the TBL; (2) counter-intuitively, balancing short- and long-term benefits, firms are more willing to cooperate with partners with relatively low consumers sensitivity of CSR activities; (3) it is wise for the manufacturer to allocate more funding to environmental responsibility than to community responsibility. In addition, considering both short- and long-term benefits, comparing with the manufacturer, the retailer has a stronger incentive to improve recycling efficiency.

With the rapid development of the economy and industry and the improvement of pollution monitoring, how to accurately predict PM2.5 has become an issue of concern to the government and society. In the field of PM2.5 pollution forecasting, a series of results have emerged so far. However, in the existing research field of PM2.5 prediction, most studies tend to predict short-term temporal series. Existing studies tend to ignore the temporal and spatial characteristics of PM2.5 transport, which leads to its poor performance in long-term prediction. In this paper, by optimizing previous PM2.5 deep learning prediction models, we propose a model GAT-EGRU. First, we add a spatial modular Graph Attention Network (GAT) and couple an Empirical Modal Decomposition algorithm (EMD), considering the temporal and spatial properties of PM2.5. Then, we use Gated Recurrent Unit (GRU) to filter spatio-temporal features for iterative rolling PM2.5 prediction. The experimental results show that the GAT-EGRU model has more advantages in predicting PM2.5 concentrations, especially for long time steps. This proves that the GAT-EGRU model outperforms other models for PM2.5 forecasting. After that, we verify the effectiveness of each module by distillation experiments. The experimental results show that each model module has an essential role in the final PM2.5 prediction results. The new model improves the ability to predict PM2.5 after a long time accurately and can be used as a practical tool for predicting PM2.5 concentrations.