System Dynamics & Modeling, Idaho National Laboratory, Idaho Falls, ID 83415, USA.
Operations Research and Analysis, Idaho National Laboratory, Idaho Falls, ID 83415, USA.
Sensors (Basel). 2023 Feb 22;23(5):2421. doi: 10.3390/s23052421.
A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.
可持续的生物质供应链不仅需要一个具有减少的碳足迹和成本的高效流畅的运输系统，还需要良好的土壤特性以确保持久的生物质原料供应。与现有的方法不同，这种方法不仅考虑了经济因素，还考虑了生态因素，以开发可持续的供应链。为了可持续地供应原料，需要有足够的环境条件，这需要在供应链分析中加以考虑。我们使用地理空间数据和启发式算法，提出了一个集成框架，通过运输网络分析来模拟生物质生产的适宜性，通过生态指标来模拟经济方面。通过考虑生态因素和道路运输网络，使用分数来估计生产适宜性。这些因素包括土地覆盖/作物轮作、坡度、土壤特性（生产力、土壤质地和侵蚀因子）和水的可用性。该分数确定了具有最高分数的田地的优先顺序的仓库的空间分布。通过图论和聚类算法提出了两种仓库选择方法，以从两者中获得上下文化的见解，并有可能获得对生物质供应链设计更全面的理解。通过聚类系数，图论有助于确定网络中的密集区域，并指示仓库的最合适位置。聚类算法通过 K-means 帮助形成集群，并确定这些集群中心的仓库位置。在南大西洋美国皮埃蒙特地区的一个案例研究中应用了这一创新概念，确定了行驶距离和仓库位置，对供应链设计产生了影响。该研究的结果表明，与使用聚类算法方法获得的 2 个仓库的设计相比，使用图论方法获得的具有 3 个仓库的更分散的基于仓库的供应链设计可以更经济和环保。在前一种情况下，从田地到仓库的距离总计为 801,031,476 英里，而在后一种情况下，距离总计为 1,037,606,072 英里，这意味着原料运输的距离增加了约 30%。
