Shrimp closed-loop supply chain network design

Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP-hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings

Vertically Self-Gravitating ADAFs in the Presence of Toroidal Magnetic Field

Force due to the self-gravity of the disc in the vertical direction is considered to study its possible effects on the structure of a magnetized advection-dominated accretion disc. We present steady-sate self similar solutions for the dynamical structure of such a type of the accretion flows. Our solutions imply reduced thickness of the disc because of the self-gravity. It also imply that the thickness of the disc will increase by adding the magnetic field strength.Comment: Accepted for publication in Astrophysics and Space Science

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

© 2018, Springer-Verlag London Ltd., part of Springer Nature. In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN

Experimental Investigation of Several Different Types of Soil Erosion Protection Systems

Most unprotected slopes face stability problems due to erosion. Generally, an unstable slope issue arises when erosion begins on its surface. Several erosion protection methods provide a solution to protect the slope surface by dividing a large slope area into many small cells, hence increasing the stiffness of the soil surface. In this research, a total of 964 tests were performed on unconfined slopes and slopes with three types of confinement systems varying in three different sizes. The experiments were conducted based on various rainfall intensities, rainfall durations, and slope angles. Furthermore, the experimental results for the unconfined and confined slopes were compared to indicate the effectiveness of the soil confinement system. The confined slope showed significantly lower soil loss compared to the unconfined slope. Additionally, the results revealed that the triangular type was the most effective confinement system, as the lowest soil loss mass was recorded. In general, the introduced erosion protection system shows a promising way of preventing slope failure due to erosion at an early stage

Metaheuristic optimizers to solve multi-echelon sustainable fresh seafood supply chain network design problem: A case of shrimp products

This is the final version. Available from Elsevier via the DOI in this record. Seafood products are sought-after among communities all over the globe and are the main sources of essential nutrition for humans. Recently, the seafood supply chain networks have encountered obstacles that new sustainability regulations and the pandemic have brought forward. In this study, a novel supply chain network is developed for fresh seafood, considering sustainability aspects, to ideally balance the financial aspect of the network while enhancing the recycling of waste products. Moreover, four metaheuristics are employed to conquer the computational complexity of exact solution methods. To evaluate the performance of the algorithms in addressing the complexity of the proposed seafood supply chain model, some numerical examples in three different scales are designed. The obtained results from metaheuristic optimizers are assessed based on five effective measures. To facilitate the statistical analysis process, each measure is normalized using the relative deviation index indicator. According to the results obtained from the implementation of metaheuristic algorithms, it can be concluded that the multi-objective grey wolf and multi-objective golden eagle optimizers outperform the other two solution methods in terms of quality of solutions. Therefore, they can be applied efficiently in solving real-world seafood supply chain network problems