An Integrated Approach of Fuzzy Quality Function Deployment and Fuzzy Multi-Objective Programming Tosustainable Supplier Selection and Order Allocation

Subject Areas : Executive Management

Amir Hossein Azadnia ¹ , Pezhman Ghadimi ²

1 - Assistant Professor, Department of Industrial Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
2 - Assistant Professor, School of Mechanical and Materials Engineering, University College Dublin, Ireland

Received: 2016-06-14 Accepted : 2017-11-20 Published : 2018-03-01

Keywords: Sustainability, Order allocation, Fuzzy Inference System, sustainable supplier selection, Fuzzy multi-objective non-linear programming,

Abstract :

The emergence of sustainability paradigm has influenced many research disciplines including supply chain management. It has drawn the attention of manufacturing companies’ CEOs to incorporate sustainability in their supply chain and manufacturing activities. Supplier selection problem, as one of the main problems in supply chain activities, is also combined with sustainable development where traditional procedures are now transformed to sustainable initiatives. Moreover, allocating optimal order quantities to sustainable suppliers has also attracted attention of many scholars and industrial practitioners, which has not been comprehensively addressed. Therefore, a practical model of supplier selection and order allocation based on the sustainability Triple Bottom Line (TBL) approach is presented in this research article. The proposed approach utilizes Fuzzy Analytical Hierarchy Process combined with Quality Function Deployment (FAHP-QFD) for reflecting buyer’s sustainability requirements into the preference weights that are then exerted by an efficient Fuzzy Assessment Method (FAM) to assess the suppliers to obtain their sustainability scores. Thereupon, these scores are utilized in a fuzzy multi-objective mix-integer non-linear programming model (MINLP) for allocating orders to suppliers based on the manufacturer’s sustainability preference. A real-world application of food industry is presented to show the practicality of the proposed approach.