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.

Urbanization has become a main challenge all over developing countries in the 21st Century. However, decision making should take into account the different national situations with their complex factors to achieve sustainable development. As standards of living have risen in urban areas, local/neighbor urbanization has become a coming trend in China. With this in mind, the paper focuses on the optimization of nearby gathered village locations in Population Migration (PM) with consideration of both qualitative and quantitative criteria. Therefore, an integrated multiple objective decision making approach (MODM) under a bi-uncertain environment is proposed to solve this problem, which is based on the comprehensive Economy-Society-Ecology-Resource-Religion (ESERR) urbanization concept. The first step is to establish a bi-uncertain multiple objective programming model orienting the problem. Secondly, the model process is composed of fuzzy random variable transformation and the expected value model based on a new fuzzy measure, which is given accordingly to obtain the equivalent model. Thirdly, in order to describe the model efficiently, the Multi-Objective Adaptive Global Local Neighbor Particle Swarm Optimization (MOAGLNPSO) with three-dimensional Pareto optimal judgment criteria is designed. Finally, a case study is tested to validate the effectiveness and to illustrate the advantages of the whole approach. This novel approach can help optimize sustainable urbanization strategies and ensure their realistic application.

Poverty alleviation is a critical concern in developing countries. China launched the Targeted Poverty Alleviation (TPA) program and implemented a series of anti‐poverty initiatives, aiming to reduce poverty and enhance the well‐being of rural households. The TPA program has been recognized as significantly promoting rural development, but whether it can benefit all poor households equally has been underexplored. This study systematically examines the impact of the TPA program on poor households with different causes of poverty and empirically analyzes income differences across household and village types under different income groups using a linear quantile mixed model (LQMM), based on survey data from a high‐altitude deep‐poverty county in southwest China. The results indicate an income imbalance among poor households with different causes of poverty, whereas households in poor and non‐poor villages exhibit equivalent development. Differences are more pronounced at higher income quantiles, suggesting that the policy provides relatively uniform benefits to poorer households. Analysis of income sources reveals disparities between wage and transfer incomes, while benefits from business income remain consistent. These findings underscore the need for sustained policy support to enhance the stability and sustainability of poverty alleviation and income transformation among low‐income households. This study contributes to a deeper understanding of the TPA program, and the recommendations derived from the results can serve as references for poverty alleviation in rural areas of developing countries.