Open AccessIntegrated Fuzzy Criteria Evaluation with Metaheuristic Optimization for Green Supplier Selection and Order Allocation
Author(s) -
T. Niranjan,
B. Singaravel,
Saraswati Raju
Publication year - 2021
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/1057/1/012074
Subject(s) - fuzzy logic , supply chain , operations research , computer science , vagueness , metaheuristic , multiple criteria decision analysis , sustainability , supplier evaluation , order (exchange) , product (mathematics) , topsis , mathematical optimization , supply chain management , business , engineering , marketing , mathematics , artificial intelligence , ecology , geometry , finance , biology
In the present growing competitive environment, it’s essential for a company to select the best supplier because company performance not only depends on its internal efforts but also on supplier’s performance. Due to growing environmental issues & increased awareness among public, and stricter environmental laws imposed by the government made the companies consider environmental sustainability matters while selecting suppliers. If in case suppliers have any capacity constraint or any other constraints, then complexity in selecting suppliers who fulfill both company’s demand and standards increases. Information from the literature review and through the opinion of experts, the effort has been made to find essential criteria for the evaluation and at the same time for the selection of a green supplier. For the same an integrated decision-making tool, on the basis of fuzzy TOPSIS and fuzzy DEMATEL has been presented to show the procedure for the selection of the best supplier. Due to vagueness in human judgment fuzzy concept has been used. A MILP (Mixed Integer Linear Programming) has been proposed which shows how order can be allocated in multiple sourcing environments. Modeling a supply chain network for multi product flow in different demand scenarios and solving the problem using a novel meta-heuristic algorithm like Teacher-learner-based-optimization (TLBO) is the major contribution of this work.