مائده احمدي فرشمي

In recent years, we have seen unprecedented production of data in various fields. Also, the new opportunities that have been created by collecting and using data have encouraged many managers to change the way they make decisions (relying less on intuition and relying on data), and weare witnessing a huge leap towards a data-based attitude in decision-making sciences, because a comprehensive and multi-dimensional analysis of data can significantly improve the performance and efficiency of decision-making, and for this reason, data-driven decision-making has become a common concept. The supplier selection problem is an important part of the supply chain management process in organizations and has a strategic role in determining their success because one of the main factors of sustainability and survival in today's highly competitive environment is reducing product production costs. Choosing the right suppliers can significantly reduce production costs and increase the organization's competitiveness to provide customers with their products and services faster, cheaper and better than competitors. Considering the increasing importance of decisions related to the selection of suppliers and the willingness of organizations to review their purchase and eva‎luation strategies, in this research, a comprehensive and efficient decision-making method for ranking and selecting suppliers using machin learning and classification techniques is presented. that performs better than traditional methods in terms of accuracy and time, and then the result is used in a three-objective optimization model for multi-product order allocation. To achieve this goal, after determining the criteria using previous studies and implementing machine learning algorithms on past data and choosing the most suitable result according to the prediction accuracy and the confusion matrix, the ranking of suppliers will be determined after Using the historical weight of each supplier, which is the result of this process, in the three-objective optimization model of order allocation and solving it by the lp metric method, the optimal order value of each product to each supplier is determined.

انتخاب تأمين كنندگان , تخصيص سفارش , يادگيري ماشيني , طبقه بندي , تصميم گيري داده محور

Supplier selection , order allocation , machine learning , classification , data driven decision making

maedeh ahmadi forshomi

ahmad makui