The spread of the coronavirus has reduced the value of stock indexes, depressed energy and metals commodities prices including oil, and caused instability in financial markets around the world. Due to this situation, investors should consider investing in more secure assets, such as real estate property, cash, gold, and crypto assets. In recent years, among secure assets, cryptoassets are gaining more attention than traditional investments. This study compares the Bitcoin market, the gold market, and American stock indexes (S&P500, Nasdaq, and Dow Jones) before and during the COVID-19 pandemic. For this purpose, the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets. Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin, gold, and stock markets. In particular, we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period. This paper provides practical impacts on risk management and portfolio diversification.

Inventory and routing are the two most important elements to company’s survival in supply chain environments. Hence, solution approaches of inventory routing problem (IRP) should assure adequate inventory level and also provide an efficient route. In this case, hybrid approaches can empower researchers to solve the IRP. The aim of this study is to develop a new hybrid methodology that includes two phases to provide a generic framework for IRP. In Phase I, genetic algorithm-based simulation optimisation is used to dynamically perform inventory control and routing decisions. In Phase II, artificial intelligence (AI)-based simulation in which the lead time of supply chain members is predicted is employed to extend the functionality of the method in Phase I. The proposed hybrid methodology gives insights into the cross-fertilisation of AI, simulation, and optimisation for researchers. Therefore, this integration can be applied to different supply chain problems by using similar methods.

In this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.

Routing is one of the most important components of logistics and has a vital role in economic growth. Inefficient routing has also contributed to troubling pollutants that have adverse environmental effects worldwide. Over the years, operations research practitioners have therefore drawn considerable attention to the routing problem. In this paper, a pollution routing problem (PRP) with time window constraints and capacitated vehicles is considered to integrate environmental issues in routing for pollution reduction. This work introduces a simulation optimization methodology that includes the realization of important parameters in real-world applications to minimize the total lateness cost and fuel consumption. The proposed method was developed with Simio simulation software and non-dominated sorting genetic algorithm-II (NSGA-II). It evaluates the dynamic system operations over time and performs a comprehensive analysis. In addition, a two-step hierarchical multi-criteria decision-making (MCDM) method is employed to choose the best strategy for PRP. Results of the two-step hierarchical MCDM method demonstrated that the strategy considering the total cost of supply chain is the best alternative for the proposed system. The use of MCDM methods after simulation can be a more robust and effective way to find solutions to such complex problems.

The development of Turkey's industry is contributing to a significant rise in electrical energy demand. Also, electricity is one of the critical elements in the household sectors. Therefore, the planning and managing of electrical energy is of great importance to support economic growth. In addition, effective prediction of market-clearing prices (MCP) is critical topic to meet the increasing energy demand and provide basis for decision making process. In this paper, MCP is predicted using artificial neural network (ANN), convolutional neural network (CNN), and also three boosting algorithms including extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and adaptive boosting (AdaBoost). Various performance metrics are employed to evaluate the prediction performance of proposed methods. The results showed that proposed methods provide reasonable prediction results for energy sector. Hence, producers and consumers can use these methods to determine the bidding strategies and to maximize their profits.