首页|The customer-based supplier selection and order allocation problem based on the waste management and resilience dimensions: A data-driven approach
The customer-based supplier selection and order allocation problem based on the waste management and resilience dimensions: A data-driven approach
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NETL
NSTL
Elsevier
This work focuses on the Supplier Selection and Order Allocation (SSOA) problem with prominent features namely resiliency, circular economy, and customer-based dimensions. In this regard, this work proposes a novel data-driven approach based on the data mining and decision-making methods. In the current work, in the first step, the weights of the indicators are computed using the Fuzzy Best-Worst Method (FBWM). Then, the performance of the potential Raw Material Providers (RMPs) is assessed using the Decision Tree Regressor (DTR), and Data Envelopment Analysis (DEA) methods. In the next step, the best RMPs is identified and also the number of orders is specified by proposing a multi-objective mathematical model. In the next step, to deal with the mixed uncertainty, a hybrid data-driven method by combining the Robust Possibilistic-Stochastic Optimization (RPSO) and Prophet methods is proposed. Finally, to achieve the optimal solution, a new method called the Lexicographic Chebyshev Multi-Choice Goal Programming with Utility Function (LCMCGP-UF) is proposed. The achieved outputs demonstrate that cost, quality, waste management, service level, and robustness are determined as the most desirable indicators. The proposed model determines the best suppliers and also specifies the optimal location to establish the facilities. Additionally, the results confirm the efficiency and validity of the developed data-driven approach. Moreover, the results of the sensitivity analysis show that the total cost and non-resiliency of the supply chain have increased by increasing the demand parameter while the service level has decreased.
Supplier selection and order allocationResiliencyCircular economyWaste managementDecision tree regressorProphet method
Borna Rezaie、Nikbakhsh Javadian、Mohammad Kazemi
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Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran
NETL
NSTL
Elsevier
