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One important step to achieve a sustainable transportation system is to control the impact and evaluate the effect of various influencing factors toward three dimensions of sustainability. Within this context, diverse analytical approaches have been developed to assess the sustainability level of various transportation systems, however, sustainability evaluation based on fuzzy multiple criteria decision-making approaches are still limited. In current research activity, an integrated quantitative evaluation technique is proposed to narrow the identified gap. The developed decision-making approach is consisted of two main phases. Firstly, fuzzy analytic hierarchy process is utilized to weigh the sustainability dimensions resulting in the incorporation of the experts’ knowledge along with the evaluation process. Then, a proposed fuzzy inference mechanism is proposed to provide an indication on the performance of an evaluated road transportation system. The developed approach is applied on a real-world case study. Finally, future works are presented together with some concluding remarks.

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Developing realistic models for the evaluation of sustainable supply chains has turned into a major challenge facing managers. The decision-making approaches proposed here consist of two stages. At the first stage, a dynamic-network data envelopment analysis (DNDEA) model is established for the first time, wherein the current efficiency of a business can be influenced by its prior social and environmental activities, as two main dimensions of sustainability. The second stage correspondingly presents, for the first time, a model in which total efficiency is calculated based on the value of historical data. Sensitivity analysis is exploited to determine the more effective factors of sustainability in efficiency evaluations. To validate the model, it is used to assess the sustainability of the suppliers of an auto spare parts manufacturer. The study results reveal that the model is well-able to evaluate the performance of dynamic network structures, with a very high discriminating power. Following the implementation of this model, only the supplier(KARAN) is found to reach the efficiency limit, and  SIRIN S.N. is recognized as the most inefficient supplier with an efficiency score of 0.6409. The sensitivity analysis outcomes demonstrate that the least amount of efficiency change is related to the economic pillar; however, the rising trend in wage costs, compared with other economic factors, brings a better effect on augmenting the efficiency of some inefficient suppliers. The highest efficiency changes during sensitivity analysis are further observed in both social and environmental dimensions. Therefore, it is claimed that investing in these two pillars can have a significant impact on the efficiency of suppliers.
 

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Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. First, suppliers' efficiency values in a supply chain are determined, using the dynamic network data envelopment analysis (DNDEA) model developed for this purpose. Then, a heuristic method is presented based on the fuzzy interpretive structural modeling (FISM) to find a common set of weights (CSWs) for the variables involved. Depending on the data conditions, two approaches, viz. centralized and decentralized, are proposed for efficiency measurement. To illustrate the model's capability, the proposed methodology is further applied to the real data of a company, named Nirou Moharekeh Industries (NMI). The results of a study on 12 suppliers within the DNDEA model accordingly reveal that one unit (i.e. KARAN) obtains an efficient value, but an inefficient score is observed in 11 units, whose technical efficiency value is in the range of 0.6409 to 0.9983. After utilizing the weights gained from the heuristic method, the efficiency value of the most inefficient supplier (that is, SIRINS.N.) dwindles from 0.6409 to 0.6319.

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Abstract
Objective: From economic, environmental and social perspectives, the sustainability of the supply chain can give a competitive advantage to organizations. By designing a hybrid discrete event agent-based simulation model based on the simulation-optimization approach and meta-heuristic algorithms, this study has sought to evaluate the sustainability of the supply chain and improve the economic, environmental and social objectives of the supply chain.
Method: First, by identifying supply chain agents, an agent-based simulation model is developed. After designing the hybrid simulation model, the verification and validation phases are performed. By combining the simulation model with meta-heuristic algorithms and using the simulation-optimization approach, the optimal/near-optimal values of the components affecting the sustainability of the supply chain are finally extracted.
Findings: In addition to being able to reflect all the complexities of supply chains, the hybrid simulation optimization approach can also improve the key components affecting the sustainability of the supply chain.
Results: Implementation of sustainable supply chain components without optimizing the key variables of the supply chain can lead to the deterioration of performance and sustainability of the supply chain. The components of the maximum levels of product and inventory maintenance and how to implement environmental and social aspects in all the elements of the supply chain have a direct effect on the chain performance and should have appropriate values in different scenarios.