Carbon regulation differences are the main obstacle to global carbon emissions reductions. To alleviate the global environmental pressures from carbon emissions, high-carbon-regulated countries are seeking to impose carbon tariffs on imported products. In response, low-carbon-regulated countries can choose to allocate carbon quotas on manufacturers (H strategy) or provide subsidies to reduce their emissions reduction costs (S strategy). Therefore, it is worth exploring the joint impact of carbon tariffs and the associated coping strategies. In this context, this paper explores optimal manufacturing production and emissions reductions, analyses the impact of carbon tariffs on carbon emissions and social welfare, and examines strategy selection in low-carbon regulated countries. It is found that both the production and profits for manufacturers with relatively low carbon emissions reduction costs can increase with carbon tariffs. When a carbon tariff is fixed, the response strategy in low -carbon-regulated countries can either increase or decrease global carbon emissions. Carbon tariffs always have a negative impact on the social welfare of low-carbon-regulated countries. Interestingly, carbon tariffs may also harm the social welfare of high-carbon-regulated countries, especially when their manufacturers have high emissions reduction costs. Finally, as for social welfare in low-carbon-regulated countries, high-carbon-regulated countries, and the global community, both H and S strategies can be win-win strategies when emissions reduction cost subsidies are moderate. However, when emissions reduction cost subsidies are high, H strategy is found to be a win-win strategy.

In the current globalized business environment, multinational competition has become the norm for companies. This paper considers technology spillovers among manufacturers and develops a global supply chain network equilibrium model. Firstly, the optimal decision-making behaviors of manufacturers, retailers, and demand markets are characterized separately. Secondly, based on the variational inequality theory, the optimal decision-making behaviors of global supply chain members are transformed. Finally, the model is solved and analyzed using the Euler algorithm. The primary objective is to explore the impact of research and development (R&D) subsidies and intellectual property protection (IPP) strategies on manufacturers’ research and development technological levels. Furthermore, the study delves into their effects on the production and transactions of the global supply chain network and social welfare. The following conclusions are drawn: (1) Technology spillovers have a positive effect on the technological level achieved by manufacturers through research and development investment and social welfare. However, intense technological competition may harm manufacturers’ profits. (2) Under the symmetric subsidy policy, higher subsidies may lead to a decrease in social welfare. (3) Under symmetric intellectual property protection policies, increasing the intensity of intellectual property protection benefits manufacturers but is detrimental to retailers and social welfare. However, under an asymmetric intellectual property protection strategy, implementing high-intensity intellectual property protection by high-technology countries is advantageous for retailers and social welfare. This conclusion has contributed to the technical research and development and production operation decision making of global supply chain members, as well as government policy formulation, and has also provided a new perspective for theoretical research in the field of global supply networks.

With increased environmental protection awareness, sustainability has been incorporated into supply chain management. Sustainable supplier selection and evaluation have become an acritical part of supply chain management. They can significantly improve the supply chain’s operational performance and enhance enterprises’ competitiveness. Based on trapezoidal interval type-2 fuzzy numbers (TIT2FNs) and cloud probability dominance relations (PDR), manufacturers can make more tangible and environmentally friendly decisions in the SSSE process. In this paper, a SSSE indicator system is first established using the necessary economic, environmental, and social factors. The importance of the indicators described in linguistic terms is transformed into TIT2FNs, and the weight of each indicator is calculated. In order to prevent candidate suppliers from promoting performance maliciously, different weights are given according to the impact of the enterprise’s historical performance on the present. Finally, the cloud PDR method is used to determine the optimal sustainable supplier. A case study and analysis are provided to show the feasibility and superiority of the proposed method.

This paper studies the multi-level supply chain network equilibrium optimization problem of multi-energy-efficiency products under different government subsidies and demand scales. In the equilibrium optimization problem, manufacturers determine the production volume of the energy-saving products; retailers decide the transaction volume with manufacturers, distribution volume for markets, and marketing efforts of energy-saving products; markets determine the transaction price. Firstly, the optimal decision-making behaviors of manufacturers, retailers, and markets are described. Simultaneously, the global optimization problem is transformed into a finite-dimensional variational inequality formulation. Then, the equilibrium conditions of the whole supply chain network are derived by the Euler method. Finally, a case study verifies the effectiveness of the proposed method. Interestingly, we found that energy-saving subsidies and demand scales were negatively correlated with the marketing efforts of the subsidized retailers for high energy-efficient products and positively correlated with the marketing efforts of non-subsidized retailers for high energy-efficient products in the same market; the development of retailers in the same market tended to be consistent, and the differentiation of the demand scale eliminated the retailers without a competitive advantage.

The demand for perishable products in emerging markets has been increasing. However, the perishability of products brings tremendous challenges for firms to build a sustainable supply chain. In this paper, we propose an integrated model of location-inventory-routing for perishable products, considering the factors of carbon emissions and product freshness. First, the economic cost, carbon emission levels, and freshness of the perishable products are analyzed. Second, with the goals of achieving the lowest economic cost and carbon emissions and the highest product freshness, a multi-objective planning model is developed, and constraints are established based on the actual location-inventory-routing situation. Third, the YALMIP toolbox is used to solve the model, and the optimal solution to this complex multi-objective problem is obtained. Finally, the effectiveness and feasibility of the proposed method are verified by the case study, as well as the sensitivity vehicle speed to the results. It is found that the integrated model proposed in this paper is able to significantly improve the efficiency of perishable goods supply chain management from the perspective of global optimization, and vehicle speed is able to significantly affect economic costs and carbon emissions.

Medical service quality is the foundation and core of the medical institution’s management. However, the abstraction of service quality makes it difficult to measure by general measurement approaches. This paper proposes a new fuzzy multi-attribute decision-making model to evaluate hospital service performance based on a grey relational analysis. First, the Delphi method is used to determine criteria and establish an evaluation system. Then, the existing uncertainty and ambiguity are quantified by the interval-valued intuitionistic trapezoidal fuzzy numbers. Second, the subjective and objective information is aggregated to determine criteria weights, and then the grey relational analysis method is proposed to evaluate alternatives. Finally, a case is provided to illustrate an application of the proposed model. In addition, comparative analysis and sensitivity analysis are performed to verify the feasibility and effectiveness. The results show that the proposed model is an effective tool for evaluating medical service quality.

The aim of this paper is to provide a method for selecting a green supplier in a dynamic environment, while considering the psychological behavior and the time factors of the decision maker from the manufacturer’s perspective. The supply selection method that is based on the Third Generation Prospect Theory (PT3) is proposed and an optimal ordinal number is obtained. First, the green supplier selection index system is established. Then, the indicators that are given by the manufacturer are used as reference points, and the income and loss matrices are established by calculating the gains and losses of the index values in the interval number relative to the reference points. Next, considering the time factor and calculating the variable weight based on the Gray correlation coefficient method and the time weight of the penalty mechanism method, the suppliers are chosen based on the comprehensive prospect value. Finally, the validity and the feasibility of the method are proven through a case analysis.

The precisely perception of key customer requirements (CRs) is critically important for customer collaborative product innovation (CCPI) design. A novel approach is proposed based on the Kano model, interval 2-tuple linguistic representation model, and prospect theory. First of all, a Kano model is constructed to preliminarily screen the relatively important product function attributes. For the uncertain and vague information of CRs, an interval 2-tuple linguistic representation model is proposed to determine the weight of CRs. Then, the comprehensive prospects value is utilized for sorting the innovative programs based on the prospect theory. Finally, a numerical example is given to verify the scientific and validity of the proposed method.

Low-carbon product design is an important way to reduce greenhouse gas emission. Customer collaborative product innovation (CCPI) has become a new worldwide product design trend. Based on this popularity, we introduced CCPI into the low-carbon product design process. An essential step for implementing low carbon CCPI is to clarify key low carbon requirements of customers. This study tested a novel method for perceiving key requirements of customer collaboration low-carbon product design based on fuzzy grey relational analysis and genetic algorithm. Firstly, the study considered consumer heterogeneity, allowing different types of customers to evaluate low carbon requirements in appropriate formats that reflected their degrees of uncertainty. Then, a nonlinear optimization model was proposed to establish the information aggregation factor of customers based on the genetic algorithm. The weight of customers was obtained simultaneously. Next, the key low carbon requirements of customer were identified. Finally, the effectiveness of the proposed method was illustrated with a case related to a low carbon liquid crystal display.

随着产品结构日益复杂化和定制生产的广泛发展，产品协同制造成为一种趋势，合作伙伴的选择将决定协同制造的成功与否。针对权重信息未知的情形，使用直觉模糊AHP(Analytic Hierarchy Process)来求解指标权重，同时将灰色关联系数引入TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)进行结合，提出一种改进的TOPSIS对合作伙伴进行选择。最后以算例和对比分析，论证了所述方法的科学有效性。