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Evaluation of the sustainability of supply chains is a complex decision-making problem. One of the techniques, which are used for assessing the sustainability of supply chains is data envelopment analysis (DEA). Conventional DEA models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration the internal interactions of DMUs. Also, they assume inputs and outputs are crisp. In this research, the network DEA (NDEA) model for assessing the sustainability of supply chains in the presence of fuzzy rough data is developed. The main contribution of this paper is to develop a novel NDEA model in the existence of internal and external uncertainties. To validate the proposed model, a case study for evaluating the sustainability of the supply chain in the pasta industry is presented. [ABSTRACT FROM AUTHOR]

Nowadays, product recovery and waste recycling are receiving more attention in order to reduce environmental pollution and production costs in the form of a closed-loop and sustainable supply chain. Also, designing a supply chain by considering resilience approaches can protect buyers against disruptions such as natural, human or technological disasters. On the other hand, efficient and effective financial supply chain management (FSCM) way is known as one of the main structures in line with the continuity and stability of the chain’s performance, and budget restrictions are of great importance considering the issue of scarcity of resources in the economy. This study has presented a multi-objective mixed integer linear programming (MOMILP) model of a single-period, multi-product and multi-level closed-loop supply chain, taking into account the dimensions of sustainability and resilience, emphasizing the balance between the initial budget and the cost of establishing facilities under uncertainty. subsequently, to eliminate the uncertainty of the demand parameters and costs, its robust counterpart was presented based on Pishvaee’s robust possibilistic programming (RPP) model. The augmented Epsilon Constraint method (AEC) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were used to solve and evaluate it. [ABSTRACT FROM AUTHOR]

Nowadays, the lack of natural resources and reserves of raw materials, besides the increased production costs and the problems with burying and destroying industrial waste, have attracted attention to product recovery and waste recycling in the framework of a closed-loop and sustainable supply chain, also able to create a competitive advantage. On the other hand, researches indicate that taking resilience approaches into account in supply chain design may protect buyers against disruptions such as natural, human, or technological disasters. The present paper presents a multi-objective mathematical model of a single-period, multi-product, and multi-level closed-loop supply chain, considering the dimensions of resilience and sustainability under conditions of uncertainty. Hence, a deterministic mixed integer linear programming model is initially presented; subsequently, to eliminate the uncertainty of the demand parameters and costs, its stochastic counterpart was presented based on Pishvaee’s robust possibilistic programming (RPP) model. In order to solve the model, the Augmented Epsilon Constraint method was employed in the GAMS software environment. Ultimately, the model was solved and evaluated by a numerical example. [ABSTRACT FROM AUTHOR]

With the expansion of human activities, the volume of waste and hazardous waste produced has increased dramatically. Increasing the volume of waste has created challenges such as transportation hazards, cleanup, disposal, energy consumption, and most important environmental problems. The difficulty of unsafe waste control is one of the critical studies topics. Finding the optimal location of hazardous waste disposal is one of the issues that, if done properly, can significantly reduce the aforementioned challenges. The increasing volume of information, the complexity of multivariate decision criteria, have led to the lack of conventional methods for finding the optimal location. Machine learning methods have proven to be effective and superior in many areas. In this paper, a new method based on machine learning for finding the optimal location of hazardous waste disposal is presented. In the proposed method, after applying clustering in the separation of the desired areas, the gray wolf algorithm optimization is used to find the optimal location of waste disposal. In order to apply the gray wolf optimization algorithm, a multivariate target function is defined. Cluster centers as were chosen as location of waste disposal. Proposed method is performed on collected data from the study area in Iran, Tehran province. Proposed clustering method is evaluated and compared with some metaheuristics algorithm. The simulation results of the proposed method show cost reduction in finding the desired locations compared to similar researches. Also, Xi and Separation index was used for evaluation of the proposed clustering method to select the best location. The number of best locations using Xi and Separation index claim the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In order to transport natural gas to long distances, it is economical that the natural gas is converted into the liquefied natural gas (LNG). Several countries look forward to conclude the contract for purchasing LNG. In the present research paper, it was assumed that a customer (country) would be provided LNG due to its high demand for a long period. Different contracts that include overall and incremental discount options have been offered to the customers by different vendors. In addition, the customer may consider meeting a certain part of the demand from the spot market. Three factors are important while concluding the LNG contracts: the evaporation rate, the quality, and the operational costs. In the present study, first, the best level of each factor was concluded by utilizing the LINMAP method, following which a robust model was presented in order to determine the amount of LNG that should be purchased from each contract as well as from the spot market. The mathematical model was tested through a numerical example which included 10 contracts with different options. [ABSTRACT FROM AUTHOR]

Accreditation means the systemic evaluation of healthcare centers. In this process, the opinions of a group of experts are noted to evaluate healthcare organizations and make decisions about granting them qualifications. In this study, the fuzzy DEMATEL technique was utilized to rank and weight the categories, subcategories, and standards of hospital accreditation. The present research is empirical in terms of purpose and descriptive-survey in terms of data collection. The statistical population included Iran's national accreditation standards. Eight experts, appraisers, senior appraisers, and policymakers of Iran's national accreditation have been selected. The fuzzy DEMATEL technique was employed to determine the internal correlation between the existing criteria (standards) and their weights in a bid to identify the influenced structure of effective criteria or standards. Regarding the categories, management and leadership affected other factors of hospital standards and improved them. According to the proposed ranking and weighting model obtained from the expert opinions, most standards of management, executive management leadership, and the main decision-making body were effective in hospitals. These standards can improve hospital weaknesses and strengths. Hence, hospitals can bring about positive changes by taking effective measures and paying special attention to the main body of management. [ABSTRACT FROM AUTHOR]