A model for agribusiness supply chain risk management using fuzzy logic. Case study: Grain route from Ukraine to Poland
本文开发了一个基于模糊逻辑的模型，用于预测谷物运输中的交货时间，考虑风险因素，并通过乌克兰至波兰的三条路线案例验证了模型的有效性。
In order to establish new logistics routes, it is necessary to address several technical and organizational issues, among others. One of the most important criteria for evaluating the performance of a supply chain is the delivery time, proactive consideration of potential hazards and associated uncertainties that may occur along the route. However, the existing solutions are often passive and reactive, based on statistics, thus not leaving much room for proactive risk mitigation measures. Therefore, there is a need for a foreseeing modern approach to account for the impact of anticipated hazards on delivery time. The aim of this study is to develop a model for determining delivery time considering expected risk factors (RF), based on mathematical tools of fuzzy logic and actual background knowledge elicited from the literature and experts. The paper identifies primary technical and operational hazards that occur during loading and transport and converts them into risk factors. The risk factors are then quantified and fed into a fuzzy model developed with the Matlab Fuzzy Logic Toolbox and assembled in the Simulink environment. The application of the model is demonstrated in three case studies reflecting three potential grain supply chains (SC) from Ukraine to Poland: classical transport by rail grain hoppers (SC1); transport by containers on railway platforms (SC2); transport by bulk grain trucks (SC3). The resulting travel time for the analysed SCs is between 49 and 71 h for SC1, between 45 and 62 h for SC2 and between 42 and 62 h for SC3. In addition, the outliers of the travel time values beyond the 1.5 quantiles were defined according to the uncertainty band. The results of the fuzzy model were compared with the results of the deterministic approach in the concurrent validation and a good agreement was found. This proves the appropriateness of the fuzzy model calculations and the possibility of using alternative SCs in grain delivery. The main benefit of the proposed model is a new universal tool based on a holistic and active approach to risk assessment using fuzzy logic.