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In this article, we propose a bi-objective model for the pricing–queuing problem under a fuzzy environment. We consider two objectives: maximizing the profit function and minimizing the waiting time in queue. Imagine a firm which sells a product in a channel providing after sales services. The sales price and warranty length affect customer demand. We formulate the demand function as a fuzzy system considering the sales price and warranty length. The firm optimizes the sales price and warranty length, as well as waiting time, in the queue of after sales services, to maximize its revenues and minimize waiting time. To solve the derived model, we develop a hybrid solution method of a fuzzy system and a genetic algorithm. Finally, the numerical analysis is done to show the reasonable performance of the solution method and results.

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An electric power supply is the backbone of development in advanced as well as in developing economies. An integral part of ensuring a secure power supply system is a power communication system. Due to the high and sustained performance requirements of power communication systems, electric companies prefer to construct their own communication networks privately rather than relying solely on a public communication system. The focus of this paper is on the optimal topological design of a power communication network. Based on advanced optimization models in public communication networks, and taking into account the specific Quality of Service, as demanded by various applications, such as protection, SCADA, voice, etc., an optimization model (PC/ISO) has been developed. The PC/ISO requires tedious numerical processing. Hence, a Genetic Algorithm (GA) is proposed to solve the optimization problem. In order to demonstrate the application of the proposed model for a power system communication network design and in order to evaluate GA solver results, a case study on designing the optimal communication network topology of one of the Iranian Regional Electric Companies has been conducted. The results suggest that the PC/ISO model and its GA solver are entirely viable and offer a simple, accurate, and cost effective solution.

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The paper focuses on the shape optimization, based on the resultant aerodynamic forces, of a simplified X-43 aircraft. The equations of oblique shock waves and Prandtl-Meyer expansion waves, along with the improvement of the aircraft geometry using a genetic algorithm optimisation, will allow to shape and optimize the external dimensions of the bi-dimensional simplified aircraft design. The main purpose of the optimisation is the reduction of the drag force while maintaining a sufficient lift to hold the weight of the aircraft. The optimization is undertaken at four different altitudes, obtaining for each altitude the most favourable aircraft shape to maximize the aircraft efficiency. The study can be extended to various possible aircraft weight configurations.

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Power generation using renewable energy such as wind and solar energy depend on weather behavior. An accurate weather forecast improves the operation management of a Smart Grid. However, the environmental conditions vary with both time and space and, depending on the location have different classifications. The environmental conditions vary with time and depending on the country, also the changes between standard, local and summer time can result in days of 23 h, to obtain days of 24 h; the two adjacent hours are used to calculate the missing hour. In this paper is addressed a nonlinear symbolic regression (RS by its acronym in Spanish) model is used to make annual demand forecasts. This algorithm is assessment with real measurements creating models that conform to the load forecasting of 28 weather stations. The learning variables are loads of demand and temperatures; that are grouped by hours for a two-year time interval. The RS-DEEPSO algorithm is proposed to improve the forecast. This is used due to its evolutionary, self-adaptive qualities and the possibility of exploring new scenarios in search of optimum. The forecast obtained using the new hybrid RS-DEEPSO has an average error per day less than 3.9% and per hour is less than 5.62% during a period of 1 year.

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