Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain

Abstract

With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.

Integrating agricultural sustainability into policy planning: A geo-referenced framework based on Rough Set theory

“…RST has also been applied in agricultural science as a tool for data reduction. It was applied specifically as an element in multi-stage models for multivariate and complex problem solving, like best partner selection in supply chains (Guo and Lu, 2013), agricultural topic tracking (Zhang et al, 2011), evaluation of soil fertility (Chen and Ma, 2011), and agricultural Big Data management (Shi et al, 2012;Liu et al, 2009). Finally, three relevant applications of RST regard detecting cause-effect links in forecast modeling for rural area depletion and agricultural water demand (Li et al, 2010), and as a knowledge acquisition step in expert system formulations for agricultural problem solving (Li et al, 2013).…”

Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review

“…Thirdly, the RST technique is used in 3 articles [24]- [26] characterized by analyzing information acquired basically by experience in an objective way. This technique was the first to be used, but, since 2013, no publications were found in the agri-food sector.…”

“…The research considers the advantage of the method that it does not require knowledge of the distribution of parameters or complex intermediate models. [26] applies the NN technique that allows it to estimate or approximate functions that may depend on a large number of inputs. These techniques are also used in conjunction with other techniques to improve their effectiveness.…”

“…Considering the approaches to model risk and uncertainty for supplier selection, seven papers employ fuzzy set theory to incorporate inherent uncertainty into the decision model [26], [27], [29]- [33]. Within these papers, techniques such as fuzzy analytical hierarchy process, fuzzy multi-objective mathematical programming [27] or fuzzy DEMATEL [32] are used.…”

“…The main criteria analyzed focus on the efficient response and opportunity of the supplier (responsiveness/velocity, redundancy, rerouting) in a reactive sense and allowing an efficient and fast response when a disruptive event in the supply chain is generated. Other criteria that promote prevention capacity in the supply chain [15], [16]: risk reduction plan, customization, visibility, and market adaptation were not considered by the articles despite their relevance, especially in the agri-food sector where their characteristics generate a lot of uncertainty in operations [26].…”