With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

The contradiction between industrial development and ecological environment pressure has been becoming progressively severe. Under this circumstance, more attention has been paid to the balance between industrial economic development and environmental deterioration and resource consumption. Thus, this study takes the development of industry and ecological environment change as an interactive system consideration, and comprehensively evaluates the changes of the industrial–environment system on resilience perspective with innovation. Accordingly, this paper establishes a comprehensive evaluation model. The Environmental Performance Index (EPI) and Industrial Structure Entropy (ISE) were applied to analyze the current environment pressure and industrial conditions. Then, the catastrophe theory was used to evaluate the reasonably established index system for the impact of various factors in the industrial–environment system on the resilience change. Next, the adaptive cycle model was used to analyze the evaluation results and reveals the dynamic change law of the system in the resilience range. Finally, Chengdu was selected as the research area to verify the validity of the whole study. It was found that the resilient change process of Chengdu industry–environmental system accord with the four-stage theory of adaptive cycle model. The resilient level of the city was also improved during the cycle. The result of the study can be useful to future plans and decisions. What is more, understanding the characteristics of each stage will be helpful to determine the reasonable implementation time of each key factor and improve its feedback ability.

Under the increasing pressure of global emission reduction, prefabricated buildings are becoming more and more popular. As prefabricated building manufacturers and assemblers are emerging in the market, how do they make decisions of pricing, ordering, and emission reduction? In this paper, game theory is used to make the decisions for the prefabricated building supply chain with flexible cap-and-trade and different power structures, i.e., using prefabricated building manufacturers as the leader, using the vertical Nash equilibrium, and using prefabricated building assemblers as the leader. The two-part tariff contract is designed to coordinate the supply chain and to improve the supply chain performance. Moreover, we discuss the influence of different power structures and the two-part tariff contract on the optimal decisions and profits. Finally, numerical analysis is used to verify the conclusions. This indicates that the supply chain leaders will gain a higher profit and that the power structure has a significant influence on the two-part tariff contract, which will result in an unfair distribution of profit. High carbon trading prices benefit carbon emission reduction. Consumer low-carbon awareness has a positive effect on carbon emission reduction and supply chain performance.

A renewable energy (RE) project has been brought into focus in recent years. Although there is quite a lot of research to assist investors in assessing the economic feasibility of the project, because of the lack of consideration of consumer utility, the existing approaches may still cause a biased result. In order to promote further development, this study focuses on the economic feasibility analysis of the RE project on the basis of consumer utility in the whole life cycle. Therefore, an integrated approach is proposed, which consists of triangular fuzzy numbers (TFNs), an analytic hierarchy process (AHP) and data envelopment analysis (DEA). The first step is to determine the comprehensive cost index weights of DEA by TFN–AHP. Secondly, to solve the problem, the first DEA model, which is proposed by A. Charnes, W. W. Cooper and E. Rhodes (C²R), is established to calculate the DEA effectiveness. Then, the third task involves designing a computer-based intelligent interface (CBII) to simplify realistic application and ensure performance efficiency. Finally, a solar water heater case study is demonstrated to validate the effectiveness of the entire method’s system. The study shows that this could make investors’ lives easier by using the CBII scientifically, reasonably and conveniently. Moreover, the research results could be easily extended to more complex real-world applications.