Jianhui Du

Mountain treelines are thought to be sensitive to climate change. However, how climate impacts mountain treelines is not yet fully understood as treelines may also be affected by other human activities. Here, we focus on “closed‐loop” mountain treelines (CLMT) that completely encircle a mountain and are less likely to have been influenced by human land‐use change. We detect a total length of ~916,425 km of CLMT across 243 mountain ranges globally and reveal a bimodal latitudinal distribution of treeline elevations with higher treeline elevations occurring at greater distances from the coast. Spatially, we find that temperature is the main climatic driver of treeline elevation in boreal and tropical regions, whereas precipitation drives CLMT position in temperate zones. Temporally, we show that 70% of CLMT have moved upward, with a mean shift rate of 1.2 m/year over the first decade of the 21st century. CLMT are shifting fastest in the tropics (mean of 3.1 m/year), but with greater variability. Our work provides a new mountain treeline database that isolates climate impacts from other anthropogenic pressures, and has important implications for biodiversity, natural resources, and ecosystem adaptation in a changing climate.

In 2013, Chinese President Xi Jinping advised to establish the “Silk Road Economic Belt” and the “21st Century Maritime Silk Road,” also referred as the Belt and Road Initiative or the New Silk Roads Policy. The intention is to promote international and regional trade as well as cooperation in and between Asia and Europe. Consequently, international maritime and terrestrial freight transport corridors are either established or strengthened and operated. The purpose of this paper is to reflect the Belt and Road Initiative from the perspective of logistics. The aim is to identify and formulate circumstances, expectations, opportunities, and peculiarities of logistics along the New Silk Roads. For this purpose, four corresponding challenges will be considered and outlined after an introduction to the Belt and Road Initiative. The four logistics challenges concern the awareness of new freight transport corridors and the assessment of possibilities for opening new transport relations and new markets, the implementation of new and the adaptation of existing supply chains to increase strategic logistics flexibility, the availability and use of digital infrastructure and connectivity for improved communication and coordination of logistical processes, and the willingness to consider regional and cultural differences in the preparation and realization of supply chain decisions.

The problem of the optimal location-allocation of processing factory and distribution center for supply chain networks under uncertain transportation cost and customer demand are studied. We establish a two-stage mean-risk stochastic 0-1 mixed integer optimization model, by considering the uncertainty and the risk measure of the supply chain. Given the complexity of the model this paper proposes a modified hybrid binary particle swarm optimization algorithm (MHB-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of the supply chain simultaneously. A case study of a bread supply chain in Shanghai is then presented to investigate the specific influence of uncertainties on the food factory and distribution center location. Moreover, we compare the MHB-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate.

In recent years, natural disasters occur frequently, and secondary disasters induced by major disasters will also cause huge losses. The diversity of secondary disasters makes humanitarian emergency logistics (HEL) more challenging but often overlooked by researchers. In order to solve the comprehensive HEL problem of major and secondary disasters, a three-stage mixed integer linear optimization (TS-MILO) model is proposed. Among them, the uncertainty of the demand for relief supplies is also extremely difficult to deal with. In order to resist the interference of uncertainty, based on robust optimization, the TS-MILO model is further transformed into a three-stage mixed integer robust optimization (TS-MIRO) model, which are respectively BTS-MIRO (Box set), PTS-MIRO (Polyhedral set), and ETS-MIRO (Ellipsoid set). The experimental results show that the TS-MILO model can provide the lowest cost but cannot solve the uncertainty problem. The improved TS-MIRO model will pay a robust price (increase by at least 10.05%), but maintains supply stability even in the worst-case scenario. Specifically, ETS-MIRO model has strong robustness, and its cost increase only accounts for 44.66% of BTS-MIRO model in the worst case. The service level of the three TS-MIRO models increases with the safety parameters, among which the service level in the ETS-MIRO model increases significantly from 88.53% to 96.44%. The research results can provide a strong support for the decision making of disaster relief management department.