S Angammal, G Hannah Grace
School of Advanced Sciences, Department of Mathematics, Vellore Institute of Technology Chennai, Vandalore, Chennai, 600127, India.
Heliyon. 2024 Aug 17;10(16):e36166. doi: 10.1016/j.heliyon.2024.e36166. eCollection 2024 Aug 30.
Agriculture impacts a country's social and economic growth. Crop allocation, crop combinations, and crop production processes are all necessary to achieve optimal results during various growing seasons. To maximize farm earnings, proper farm planning and resource allocations are necessary. In agriculture, land allocation problems involve several uncertainties and unpredictable variables, includes water supply, labour demands, fertility use, and food requirements. The objective of this study is to propose novel bi-level programming approaches to overcome such issues and obtain optimal land allocation for medium-sized farmers. The current study examines a bi-level, TOPSIS-based neutrosophic programming approaches in two cases, including non-interactive and interactive approaches with linear, exponential, and hyperbolic membership functions to maximize net profit and minimize the expenditure. The proposed methods are compared to other approaches, such as the Torabi & Hassini approach, the Fuzzy Optimization Technique (FOT), and the Intuitionistic Fuzzy Optimization Technique (IFOT) and are found to be more effective than the existing ones.
农业影响一个国家的社会和经济增长。作物分配、作物组合以及作物生产过程对于在不同生长季节实现最佳效果都是必不可少的。为了使农场收益最大化,进行适当的农场规划和资源分配是必要的。在农业中,土地分配问题涉及若干不确定性和不可预测的变量,包括供水、劳动力需求、肥力利用和食物需求。本研究的目的是提出新颖的双层规划方法来克服此类问题,并为中型农户获得最优土地分配。当前研究在两种情况下考察了基于TOPSIS的双层中智规划方法,包括具有线性、指数和双曲线隶属函数的非交互式和交互式方法,以实现净利润最大化和支出最小化。将所提出的方法与其他方法进行了比较,如托拉比和哈西尼方法、模糊优化技术(FOT)和直觉模糊优化技术(IFOT),发现它们比现有方法更有效。