For several reasons, recent attention has focused on urban agriculture in the context of sustainable and smart agriculture. Most of the global population has relocated from rural to urban areas. The ecological impact of agriculture is a rising concern. Furthermore, food insecurity, particularly food availability, remains a significant issue. Satisfying rising food demand with minimal environmental impact is a significant barrier to more sustainable food production. In this article, we study a sustainable location for urban farming that optimally balances economic and environmental objectives. We formulate the problem as a multi‐objective linear program that considers maximizing the ecological benefit and crop production yield and minimizing transportation cost, sensor cost, and CO2 emissions. The proposed mathematical model is then solved using a two‐phase method. The first phase uses several multi‐objective optimization (MOO) methods (weighted sum, epsilon‐constraint, augmented epsilon) to generate a pool of compromise solutions. The second phase uses multi‐criteria decision‐making (MCDM) methods (MARCOS, VIKOR, and Possibility Degree) to rank compromise solutions. We performed a sensitivity analysis first by studying the effect of different criteria weights (balanced, environmentally, and economically oriented) and then by investigating the rank reversal. Furthermore, we developed a thorough validity test that examines the impact of the dynamic elements of rank reversal by changing the importance of the model's input parameters. To provide a fusion ranking of the MCDM rankings, we devised an aggregated ranking approach using the half‐quadratic (HQ) method.

中文翻译：

---

使用两阶段多目标和多标准决策方法的可持续城市农业

由于多种原因，最近的注意力集中在可持续和智能农业背景下的都市农业。全球大部分人口已从农村地区迁移到城市地区。农业对生态的影响日益受到关注。此外，粮食不安全，特别是粮食供应，仍然是一个重大问题。以最小的环境影响满足不断增长的粮食需求是实现更可持续粮食生产的重大障碍。在本文中，我们研究了城市农业的可持续发展地点，以最佳地平衡经济和环境目标。我们将该问题表述为一个多目标线性程序，考虑最大化生态效益和农作物产量，并最小化运输成本、传感器成本和二氧化碳2排放。然后使用两阶段方法求解所提出的数学模型。第一阶段使用多种多目标优化 (MOO) 方法（加权和、epsilon 约束、增强 epsilon）来生成一系列折衷解决方案。第二阶段使用多标准决策（MCDM）方法（MARCOS、VIKOR 和可能性度）对折衷解决方案进行排名。我们首先通过研究不同标准权重（平衡、环境和经济导向）的影响进行敏感性分析，然后研究排名反转。此外，我们开发了彻底的有效性测试，通过改变模型输入参数的重要性来检查排名反转的动态元素的影响。为了提供 MCDM 排名的融合排名，我们使用半二次 (HQ) 方法设计了一种聚合排名方法。