| Title |
A Hybrid Approach to Sustainable Supplier Selection and Order Allocation Considering Quality Policies and Demand Forecasting: A Real-Life Case Study |
| ID_Doc |
65446 |
| Authors |
Jafari-Raddani, MH; Asgarabad, HC; Aghsami, A; Jolai, F |
| Title |
A Hybrid Approach to Sustainable Supplier Selection and Order Allocation Considering Quality Policies and Demand Forecasting: A Real-Life Case Study |
| Year |
2024 |
| Published |
Process Integration And Optimization For Sustainability, 8.0, 1 |
| DOI |
10.1007/s41660-023-00350-x |
| Abstract |
Sustainability has become a significant business issue, and efforts to achieve a sustainable supply chain have been intensely considered. In order to manage a sustainable supply chain, it is essential to choose the appropriate suppliers and assign the right amount of orders among them. Uncertainty about future demand makes these matters a substantial concern, and despite their importance, it has received much less attention from researchers in supplier selection and order allocation problems. In this regard, this paper presents a three-stage method for sustainable supplier selection and order allocation. In the first stage, fuzzy AHP and fuzzy TOPSIS were used to weight the criteria and rank the sustainable suppliers, and suppliers with acceptable sustainability performance were selected. In the second stage, the future value of demand is forecasted by polynomial regression (PR). In the third stage, a mathematical programming model was formulated considering a novel quality standard policy. Efficient solutions were obtained by solving a novel multi-objective, multi-period stochastic mixed-integer model utilizing LP-metric. Also, a real-world case study for a small business is presented to validate the performance of the proposed method. A sensitivity analysis reveals the effect of changes in demand, suppliers' capacity, purchasing costs, and quality policy. |
| Author Keywords |
Sustainable supplier selection and order allocation; Quality standard policy; Polynomial regression; Fuzzy MCDM; Demand forecasting |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001025853700001 |
| WoS Category |
Green & Sustainable Science & Technology; Energy & Fuels; Engineering, Environmental; Engineering, Chemical; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Energy & Fuels; Engineering; Environmental Sciences & Ecology |
| PDF |
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