Lean and Green Supplier Selection Problem: a Novel Multi Objective Linear Programming Model for an Electronics Board Manufacturing Company in Turkey

Abstract

Even though various studies are proposed to evaluate and determine the best suppliers, studies generally consider similar factors such as cost, quality, delivery time etc. in the supplier selection problem. Nowadays, some lean and green issues such as on time (JIT) delivery, waste reduction, avoidance and treatment of hazardous materials, energy efficiency, occupational health and safety, corporate social responsibility, etc. started to be addressed together with the supplier selection process. Lean and Green Supply Chain Management (L-GSCM) is a dynamic field for companies to gain advantages in the competitive business market place. In the light of these concepts a new supplier selection process is developed for an electronics board manufacturing company in Turkey. We present an integrated approach which consists of three stages. In the first stage, the Fuzzy Analytic Hierarchy Process (FAHP) method is applied in order to determine the weights of the lean and green criteria with respect to different experts’ judgments using linguistic terms. At the second stage, linguistic judgments related to qualitative drivers are provided from decision makers. Then these fuzzy, judgements are aggregated by means of fuzzy group decision making and deffuzzified to crisp scores. The outputs of this stage are the quantitative data of a Multi-Objective Linear Programming (MOLP) model. In the third stage, a novel Fuzzy Weighted Additive Max-Min approach with Group Decision-Making (F-WAMG) is proposed and applied to guarantee that satisfaction degrees of each objective functions in the MOLP model are greater than or equal to their own weights. The proposed model and solution approach is applied to an electronics board manufacturing company in Turkey. The results point out that the proposed F-WAMG approach yields consistent satisfaction degrees with respect to weights of objectives that represent decision makers’ preferences and judgements. © 2019, Springer Nature Switzerland AG.