Search

The destructions caused by natural disasters have made human societies well aware of the importance of logistical preparedness and precise human resource planning both in the pre- and post-disaster phases. Temporary Medical Centers (TMCs) are one of the most important problems for disaster response. TMCs are established in safe places to treat the injured in times of crisis. In this paper, the optimal locations for the establishment of TMCs are determined using a mixed-integer mathematical programming model. Furthermore, an optimal hierarchical structure is developed for assigning the injured from urban areas to the TMCs, clinics, and hospitals in the event of a disaster. A simulation-based optimisation model is employed to simulate the conditions at the moment the disaster occurs and the ensuing response operations. The findings indicate that the proposed hierarchical location-allocation model reduces the congestion of the injured and minimises total costs and patients' treatment time.The destructions caused by natural disasters have made human societies well aware of the importance of logistical preparedness and precise human resource planning both in the pre- and post-disaster phases. Temporary Medical Centers (TMCs) are one of the most important problems for disaster response. TMCs are established in safe places to treat the injured in times of crisis. In this paper, the optimal locations for the establishment of TMCs are determined using a mixed-integer mathematical programming model. Furthermore, an optimal hierarchical structure is developed for assigning the injured from urban areas to the TMCs, clinics, and hospitals in the event of a disaster. A simulation-based optimisation model is employed to simulate the conditions at the moment the disaster occurs and the ensuing response operations. The findings indicate that the proposed hierarchical location-allocation model reduces the congestion of the injured and minimises total costs and patients' treatment time. [ABSTRACT FROM AUTHOR]

The data envelopment analysis method is commonly used to measure efficiency. An estimate of the relative efficiency of this model is derived by calculating the ratio between inputs and outputs. Data envelopment analysis models can also be applied to network structures due to the extension of these models. Supply Chain Management (SCM) is a novel approach that governed production management in recent years. In complex and dynamic environments, the petrochemical industry requires an investigation system similar to those used by other organizations to inform about its activity's desirability, especially in complex and dynamic environments. This research focused on the petrochemical company supply chain. Laboratory studies, experts, and visits to petrochemical sites were used to identify production processes and determine indicators. After that, they were evaluated with an envelopment model and a coefficient corresponding to the identified petrochemical supply chain structure. The aggregate and componentwise efficiency of the studied units in petrochemical were also examined from 2016 to 2019. [ABSTRACT FROM AUTHOR]

The increasing frequency of natural disasters and the necessity of proper planning to minimize the impact and casualties of such crises have always been matters of great concern to human societies. In this study, a hybrid mathematical-simulative location-allocation model is proposed to carry out disaster management (DM) efforts with maximum coverage in the immediate aftermath of an earthquake. The proposed model consists of two phases: determining the optimal location of the temporary emergency stations (TECs), followed by optimal and hierarchical allocation of casualties to said temporary medical centers (TMCs). Given the contradictory nature of the model's two objectives, that is, minimizing the cost of setting up TMCs and the time taken to transfer casualties to TMC. In the second phase, a simulation-based optimization approach is employed to simulate casualties' behavior at the onset of the disaster and to determine the optimal capacity of the medical centers. The findings indicate that the costs and distance traveled by casualties during the earthquake have been reduced by 15%. [ABSTRACT FROM AUTHOR]

The efficiency evaluation of a network opens the "black box" and focuses on the internal structures and innermost interactions of the system. In this paper, we have made efforts to consider a three-stage system, comprising of six sub- DMUs, in combination with additional inputs and undesirable outputs. The proposed models are simulations of a factory, with a production area and three warehouses for goods and two delivery points. Hence, an authentic example of production planning and inventory control in a factory for a year and within duration of 24 periods was taken under consideration as a dynamic structure. In this simulation, all costs were considered, including, production costs, setup cost, maintenance costs of the products, warehouse reservation costs, transportation costs, delay penalty costs and the profit obtained from the sale of products. We utilized the multiplicative DEA with a double-frontier approach to measure the efficiency of a general system and improve the accuracy of efficiencies. Moreover, a heuristic technique was used to convert non-linear models into linear models. The ranking results of the 24 time periods indicate that the time periods 24 and 1 respectively are the best and poorest periods in terms of efficiency. Finally, we suggest using a k-means method to cluster DMUs into several groups with similar characteristics based on double-frontier Standpoint. [ABSTRACT FROM AUTHOR]

There are numerous models for solving the efficiency evaluation in data envelopment analysis (DEA) with fuzzy input and output data. However, because of the limitation of those strategies, they cannot be implemented for solving fully fuzzy DEA (FFDEA). Furthermore, in real-world problems with imprecise data, fuzziness is not sufficient to consider, and the reliability of the information is also very vital. To overcome these flaws, this paper presented a new method for solving the fully fuzzy DEA model where all parameters are Z-numbers. The new approach is primarily based on crisp linear programming and has a simple structure. Moreover, it is proved that the only existing method to solve FFDEA with Z-numbers is not valid. An example is also presented to illustrate the efficiency of our proposed method and provide an explanation for the content of the paper. [ABSTRACT FROM AUTHOR]