Open AccessSupplier evaluation and order allocation using fuzzy analytical hierarchy process and augmented epsilon constraint methods
Author(s) -
F. Farizal,
RifkaKarmila Dewi,
Djoko Sihono Gabriel
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/567/1/012035
Subject(s) - analytic hierarchy process , supplier evaluation , purchasing , procurement , computer science , operations research , supply chain , fuzzy logic , mathematical optimization , order (exchange) , constraint (computer aided design) , pareto principle , supply chain management , quality (philosophy) , production (economics) , operations management , business , engineering , mathematics , economics , microeconomics , finance , epistemology , marketing , artificial intelligence , mechanical engineering , philosophy
Environmental issues nowadays affect the way to run business. These issues push firms to have effective and efficient green supply chain management. One of critical aspect in green supply chain management is green supplier selection. Choosing suitable supplier is an important part in procurement activity. Almost 70% of the total production cost is derived from raw material purchasing cost. This research proposes two phase meta-model for supplier selection and order allocation that takes into consideration environmental criteria besides traditional criteria such as quality, cost and delivery. For the purpose, fuzzy set and analytical hierarchical process (AHP) were combined. AHP was used to allow uncertainties and vagueness due to human decision making and subjective criteria. For order allocation phase, multi-objective mathematical programming method (MOMP), the augmented ε-constraint (AUGMECON) method was used to find Pareto optimal solutions for multiple sourcing. These proposed methods were tested in one of tire manufacturing company in Indonesia. The results show that the methods gave a 0.16% of the total cost lower than the existing one as addition to fulfilling green criteria.