Road accidents as a global challenge, imposing irreparable financial and human life losses in almost all countries, especially in developing countries, annually. According to world health organization (WHO), if this trend continues, road accidents will become the 7th cause of human death by 2030. Thus, road safety policy makers have been trying to use safety promotion and preventative actions. In this paper, the road safety performance of Iranian provinces is studied. To evaluate road safety efficiency scores, data envelopment analysis based on road safety (DEA-RS) method in two deterministic and non-deterministic situations is used. To consider the uncertainty in input and output data, this paper develops credibility DEA-RS (CreDEA-RS) model. In fact, the constraints of DEA-RS model are considered as credibility constraints and a counterpart credibility DEA-RS (CreDEA-RS) model is proposed for evaluating road safety of provinces of Iran. According to the results, provinces located in mountainous and forest areas such as Gilan had a much weaker performance than provinces in desert areas such as Yazd.

An appropriate supply chain design helps survival in competitive markets. Achieving maximum efficiency may also help decision makers have a better selection for the supply chain network. The purpose of this paper is to design an efficient supply chain model in terms of the distribution channels under uncertain conditions. The proposed study produces multi products using different materials by considering four layers of multiple suppliers, producers, storages and customers. There are two objectives of maximizing efficiency of distributers and minimizing total cost of supply chain management. The proposed model locates producers as well as suppliers and determines the amount of orders from different suppliers. In order to measure the relative efficiency, the study uses the method developed by Klimberg and Ratick (2008) [Klimberg, R. K., & Ratick, S. J. (2008). Modeling data envelopment analysis (DEA) efficient location/allocation decisions. Computers & Operations Research, 35(2), 457-474.]. In addition, to handle the uncertainty, the study uses the robust optimization technique developed by Molvey and Ruszczy?ski (1995) [Mulvey, J. M., & Ruszczy?ski, A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations research, 43(3), 477-490.]. The preliminary results indicate that the proposed model is capable of providing efficient solutions under various uncertain conditions.

This paper presents a supply chain management by considering efficiency in the system. The proposed study considers two objective functions where the first one maximizes the efficiency of the supply chain and the second one minimizes the cost of facility layout as well as production of different products. In order to measure the relative efficiency, the study uses the method developed by Klimberg and Ratick (2008) [Klimberg, R. K., & Ratick, S. J. (2008). Modeling data envelopment analysis (DEA) efficient location/allocation decisions. Computers & Operations Research, 35(2), 457-474.]. The study has been formulated as a mixed integer programming and the implementation of the proposed model has been demonstrated using some numerical example. The preliminary results indicate that it was possible to increase the efficiency of supply chain without increase the supply chain expenses.

In a pioneering way, this thorough research develops and tests a new mathematical programming framework for Human Factors and Ergonomics (HFE) optimization in hospitals. The main focus is a mixed-integer linear programming model that takes into account, at the same time, operating room scheduling on the basis of patient safety, staff well-being, operational efficiency, and resource allocation. By conducting very large computational trials through the MATLAB optimization toolbox, we show the model's ability to produce schedules that not only put critical clinical tasks first but also keep the staff workloads balanced. Our findings demonstrate different fundamental aspects: optimal solutions in a way prioritize high-risk procedures, disclose the natural capacities of the system, and point out where the workflow can be improved. The optimization has been able to assign all the critical tasks (appendectomy, intubation, and code blue) without workload imbalances among the clinical staff. Nonetheless, it also revealed that the resources indeed were not fully utilized and thus more patients could be treated. This study is a very reliable tool for the healthcare managers to make evidence-based scheduling decisions that are in the time period of the hospitals and are reconcilable with the other objectives in the complex clinical environment as well.