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Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach
Affiliation:1. Department of Business and Management, Universidad Europea de Madrid, 28670, Madrid, Spain;2. Department of Mechanical Engineering, MCKV Institute of Engineering, West Bengal, India;3. Research Institute of Smart Building Technologies, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saul?tekio al. 11, LT-1022, Vilnius, Lithuania;4. Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box 1585-4413, Tehran, Iran;1. Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620015, TamilNadu, India;2. NUS Business School, National University of Singapore, Singapore;1. Business School, Cardiff University, Aberconway Building, Colum Dr, Cardiff, CF10 3EU, UK;2. School of Engineering, Cardiff University, Queen Building, The Parade, Cardiff, CF24 3AA, UK;3. School of Engineering, University of Portsmouth, Anglesea Building, Portsmouth, PO1 3DJ, UK;4. Department of Engineering Design and Mathematics, University of the West England, Bristol, BS16 1QY, UK;1. Department of Industrial and Management Engineering, Incheon National University, 22012, South Korea;2. Department of Industrial and Manufacturing Systems Engineering, Iowa State University, IA 50011, USA;3. Department of Industrial and Systems Engineering, Mississippi State University, MS 39762, USA;1. Department of Information Systems and Supply Chain Management, Neeley School of Business, Texas Christian University, 2900 Lubbock Ave, Fort Worth, TX 76109, USA;2. Marketing, Operations and Analytics Department, The Bill Munday School of Business, St. Edward’s University, 3001 South Congress Austin, TX 78704-6489, USA
Abstract:This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.
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