Sustainable supplier selection and order allocation: Distributionally robust goal programming model and tractable approximation

摘要

A challenge of planning real-life sustainable supplier selection and order allocation (SS/OA) problems for purchasing companies is to gather the extensive data and exact distributions of input data. Motivated by this challenge, this paper addresses imprecise probability distributions of uncertain per unit cost, CO_2 emissions, demand, supply capacity and the minimum order quality and characterizes these distribution uncertainty by ambiguity sets including the true distributions. Moreover, the purchasing company decisions to be optimized simultaneously include four conflicting objectives regarding cost, CO_2 emissions, society and suppliers' comprehensive value while considering risk measures incurred by cost and emissions to achieve company sustain-ability. To help decision makers formulate practicable policy, a novel distributionally robust sustainable SS/OA goal programming model is developed with expected constraints and joint chance constraints. The proposed optimization model can balance multiple conflicting objectives, and effectively solve our sustainable SS/OA problem. More importantly, we structure ambiguous distributions sets, and thus derive the computationally tractable approximation form of the proposed practical model. Finally, we illustrate our optimization method through a case study about a steel company, conduct a thorough inquiry into the effect of uncertainty and summarize the management implications of the results.