A Robust Possibilistic Programming Approach to Drug Supply Chain Master Planning

The provision of an efficient master plan which is able to integrate the procurement, production and distribution plans is a critical need in the way of achieving the competitive advantage in today’s marketplace. In this paper, a supply chain master planning problem of a drug supply chain is taken into account. The considered drug supply chain includes multiple suppliers, one manufacturer and multiple distribution centers. In this paper, a multi-objective possibilistic mixed integer linear programming model (MOPMILP) which minimizes the total logistics cost and maximizes the total value of supplier selection aggregate function is developed. It should be noted that both economic and environmental criteria are considered in the supplier selection objective function to support the green and sustainable purchasing approach. Then to cope with the input parameters tainted with high degree of uncertainty, a new effectual robust possibilistic programming (RPP) model is elaborated. The proposed robust possibilistic programming model is able to appropriately adjust the degree of feasibility and optimality robustness of output decisions against business-as-usual uncertainty. Also the proposed robust optimization model can be appropriately applied in the cases in which reliable and sufficient historical data is not available for imprecise parameters (i.e., most of the real-life problems). To show the usefulness and effectiveness of the proposed robust possibilistic programming model numerical and comparative experiments are provided. The numerical results endorse the validity and practicability of the rendered model as well as presenting the efficiency and felicity of the developed approach.