The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-fresh Food Supply Chain (AFSC) is a relevant topic to SCND and this study aims to model a new AFSC for a real-world case study. Regarding the traditional AFSC, the geographically dispersed small farmers transport their product individually to market for selling. This leads to a higher transportation cost, which is the major cause of farmers’ low profitability. This paper formulates a traditional product movement model to represent the existing AFSC. The concept of sharing economic approach is employed by the aggregate and collaborative transportation of products to minimize transportation inefficiency. This paper proposes an aggregate product movement with the vehicle routing model to re-design an AFSC for a case study in Iran based on the data of ETKA Company-the largest domestic agri-fresh food supply chain. A four-echelon, multi-period, Mixed Integer Non-Linear Programming (MINLP) approach for the proposed location-inventory-routing model is formulated for perishable products via considering the clustering of farmers to minimize the total distribution cost. پرونده مقاله

This paper considers an accurate and efficient diabetes detection scheme via machine learning. It uses the science of data mining and pattern matching in its diabetes diagnosis process. It implements and evaluates 4 machine learning classification algorithms, namely Decision tree, Random Forest, XGBoost and LGBM. Then selects and introduces the one that performs the best towards its objective using multi-criteria decision-making methods. Its results reveal that Random Forest algorithm outperformed other algorithms with higher accuracy. It also examines the details of features that have a greater effect on diabetes detection. Considering that diabetes is one of the most deadly, disabling, and costly diseases observed today, its alarmingly increasing rates, and difficulty of its diagnosis because of many vague signs and symptoms, utilization of such approach can help doctors increase accuracy of their diagnosis and treatment schemes. Hence, this paper uses the science of data mining as a tool to gather and analyze existing data on diabetes and help doctors with its diagnosis and treatment process. The main contribution of this paper can therefore be its applied nature to an essential field and accuracy of its pattern recognition via several analytical approaches. پرونده مقاله