Market fluctuations and harsh business environments are known to affect the performance of supply chain networks. To maintain an acceptable response time while coping with varying market fluctuations and potential business disruptions, agility of supply chain networks is desirable. The purpose of the research described here is to develop a mathematical model to measure the agility of members of any given supply chain network. The model is tested using stochastic simulation and a case study. The importance of this research lies in the fact that there is a huge gap in understanding of the concept of supply chain agility and how it can be measured.

This study aims to show how the oil industry may affects the Saudi oil exports. The oil price has been uneven in recent years. In this paper, several forecasting methods been evaluated to help augmenting oil export prediction accuracy. In this paper, several forecasting methods were applied to the data collected and errors been measured. As a result, based on Mean Forecast Error (BIAS), the linear regression method was the best because it has the lowest error value. This indicates that there will be variation in the demand which means the accurate prediction will help on oil production demand planning. The data used was collected from Aramco's annual report, the Saudi General Authority for Statistics, and online newspapers.

The tourism industry is one of the fastest growing economic sectors in the world, and the developments in the industry are linked to the operation of tourism enterprises and the products offered by them. Being one of the most competitive industries in the business world, the management of supply and demand is essential aspect of successful management in the tourism sector. A major issue that arises in the tourism sector is the management of crowds in many of the beautiful and renowned cities in the world. The massive flow of visitors is not only an issue for the travelers, but also imposes risks on the supply chain that operates within the tourism industry. The aim of this article is to gain an understanding of supply chain applications in the tourism industry and exploration of crowd management with regards to tourism industry. The article also explores the advantages of using supply chain for crowd management in tourism industry, particularly with reference to the Saudi Arabia’s tourism industry, along with suggestions to integrate crowd management strategies in supply chain.

Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.