m. a. vahdatzad

Restructuring and deregulation are one of the most significant developments in the world electricity market. In this market, Generation Companies (GenCos) in an oligopolistic game with incomplete information participate in a sealed auction and offer their bids in a certain period according to the market demand. Choosing the best bid to maximize profits and minimize risks in dynamic competition with other players is one of the most important issues for GenCos. The dynamic nature of this problem can help GenCos make the best decision based on learning from the past. Using reinforcement learning and considering risk aversity of the GenCos, this paper provides an agent-based simulation of the bidding behavior of Iran's electricity market. In this simulation, the GenCos optimize their bids using a learning process based on previous bids. Although a few studies have been conducted on the modeling of risk-averse behavior of GenCos under learning conditions, risk-averse analysis based on a history of profits and losses, or sever losses, has not been focused. Thus, in this paper, the learning behavior of the GenCos is modeled by the Q-learning reinforcement learning algorithm and their risk aversion behavior is modeled by the conditional value at risk measure and risk of missed opportunities in terms of the number of auction failures (missed auction opportunities). To validate the functionality of the proposed approach, it was applied to the real data of the electricity market of Yazd province, including five GenCos with the total nominal power of 2550 MW. The results were compared for different learning conditions, risk behaviors of companies, and pay as bid and uniform pricing. The results demonstrate that learning all GenCos leads to increased competition and promoted social welfare. Also, the level of risk aversion of GenCos and the type of clearing mechanism have a direct effect on the GenCos profitability and social welfare. The results can help power plants determine the bidding strategy in competitive conditions by considering their risk level. Likewise, these results assist regulators in designing market rules in line with the actual behavior of GenCos.

Transportation planning and inventory management are among the key problems at the various levels of a supply chain. The integration of transportation and inventory decisions is known as the inventory routing problem (IRP) in the literature. Building upon the reviewed literature, this paper expands an operational combination of the classical routing-inventory problem in the form of a two-level supply chain that includes a multi-period, multi-product inventory-routing coupled with various kinds of fleets with different available capacities called Fleet Size and Mix Vehicle Routing Problem (FSMVRP). In this model, stock out is not allowed. Additionally, two practical and significant features of routing are taken into consideration including: 1- ``backhauls'' in which the Distributor (Vendor) is supposed to provide services for two groups of the customers (linehaul and backhaul costumers), assuming that each one of the vehicles is first unloaded to satisfy the demands of the linehaul customers, and it is later loaded by collecting the loads from the backhaul customers; 2- ``Split delivery'' strategy according to which there is a possibility to provide the services to each customer by at least one vehicle. Due to the varying demands of the customers, it is quite possible for the demands of some customers to be more than the available capacity of one vehicle. Particularly, in the case of urban transportation, several vehicle transitions occur at a demand point. Thus, the split services can help minimize the number of the vehicles used, which in turn will increase environmental sustainability. Therefore, first, a new mathematical model, i.e., a mixed-integer programming (MIP) formulation, is presented for the problem. This problem is a non-deterministic polynomial-time hard (NP-hard). Then, according to the literature on routing-inventory problems, a bat optimization algorithm, whose performance is evaluated by an efficient genetic algorithm, is developed for the first time. At the end, the numerical results obtained by this algorithm are analyzed using the randomized test problems.

The failure probability and establishment risk of startups is high due to the required high technical knowledge and added value, new product developments, lack of financial sources and need for specialists. By the use of risk management approaches, one can systematically evaluate the risks related to the decisions and improve the success probability by studying the corresponding opportunities and treats. In this paper, we consider the development phase of Nanotechnology startups as a project and propose a quantitative framework for analyzing the risk response strategies based on the PMBOK standard and a combination of FMEA, TOPSIS, and optimization model. Using FMEA, we can study the effect of risks and risk responses on the startup goals. TOPSIS helps to analyze the impact of risk responses on all the risk factors. The bi-objective optimization model is formulated subject to the prevailing constraints such as budget, time, production capacity and incompatibility of strategies. The proposed optimization model is for determining the Pareto frontier of risk and profits that as a decision tool may help managers to select the appropriate risk response strategies. The validation and sensitivity of proposed framework is analyzed applying it for a Nanotechnology startup in Yazd science & technology Park. Although the complete enumeration can be used to solve the small and medium instances, we propose the Epsilon constraint method successfully in order to solve the large-sized problems. The validation of framework confirm that it can apply to identify, evaluate and select of optimal risk response strategies. Also, the sensitivity analysis results show that the estimation quality of the model parameters particularly costs has a great impact of the outcomes. Therefore, developing the quantitative methods such artificial neural network to estimate the key parameters such as costs, revenues and risk levels is as a direction for the future study.