A Nadir Compromise Programming for Supplier Selection Problem under Uncertainty

Supplier selection is one of the influential decisions for effectiveness of purchasing and manufacturing policies under competitive conditions of the market. regarding the fact that decision makers (dms) consider conflicting criteria for selecting suppliers, multiple-criteria programming is a promising approach to solve the problem. this paper develops a nadir compromise programming (ncp) model for decision-making under uncertainty on the selection of suppliers within the framework of binary programming. depending on the condition of uncertainty, three statuses are taken into consideration and a solution approach is proposed for each status. a pure deterministic ncp model is presented for solving the problem in white condition (certainty of data) and a solution approach resulted from combination of ncp and stochastic programming is introduced to solve the model in black (uncertainty of data) situation. the paper also proposes a ncp model under certainty and uncertainty for solving problem under grey (a combination of certainty and uncertainty of data) conditions. the proposed approaches are illustrated for a real problem in steel industry with multiple objectives. also, a simulation approach has been designed in order to examine the results obtained and also verifies capabilities of the proposed model.