javad mohammadghasemi

The supplier evaluation process is a systematic approach used by organizations to determine and choose the best vendors to construct an optimal supply chain network. In other words, in supply chain management, identifying the suitable suppliers can play a key role in the success of the supply chain networks. To this end, different researchers have developed various approaches to evaluate and select the best suppliers. The current study provides a novel mathematical data envelopment analysis (DEA) approach to evaluate the suppliers and select the best efficient supplier among the set of efficient suppliers. The proposed approach solves only one mixed integer DEA model to determine the best efficient supplier. The approach not only determines the best efficient supplier, but also finds and ranks all efficient suppliers. Moreover, the presented model considers the decision maker preferences about the relative importance of supplier evaluation factors. We provide a real-life numerical example to illustrate and show the applicability and efficacy of the new approach.

This paper focuses on modeling a sustainable electricity industry supply chain network under uncertainty. The aim of presenting this supply chain network is to meet customer demands for solar panels to generate clean energy.

A mixed-integer linear programming model, including facility location, supplier selection, optimal flow allocation, and determination of the optimal price of solar panels in the network, is considered. The sustainability objectives of the model include maximizing the profit of the supply chain network, minimizing greenhouse gas emissions, and maximizing reliability. A robust optimization method is also considered to control uncertain parameters, and precise and innovative techniques are used to solve the model.

The results of the model show that with an increase in network reliability, the current net value in the network decreases, and greenhouse gas emissions in the network increase. Additionally, the analysis of the results shows that with an increase in the network's uncertainty rate, the network's current net value and reliability decrease, and greenhouse gas emissions increase. Finally, the statistical test results also show that there was no significant difference between the averages of the number of practical solutions, the maximum spread, and the metric distance between the two algorithms, and only a significant difference exists between the solution times of the two algorithms. The results of the presented solution methods demonstrate their high efficiency in solving the sustainable electricity industry supply chain model.

Originality/Value:

 In the proposed model, essential decisions such as supplier selection, establishment of production centers, optimal product flow allocation, and pricing of solar panels are made. On the other hand, further analyses of 15 numerical examples show the high efficiency of the MOALO and MOWOA algorithms compared to the epsilon-constraint method.