This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a...

This paper is concerned with the oil & gas assets portfolio. A multi-objective portfolio model of studied from two perspectives—scale and revenue. Considering nonlinear integer constraints in model, a class mixed programming established. The weight solved by support vector machine model. hybrid genetic algorithm, which uses position displacement strategy particle swarm optimizer as mutation...

In this paper, a genetic algorithm and constriction factor based particle swarm optimization technique are proposed for solving the short term variable head hydrothermal scheduling problem with transmission line losses. The performance efficiency of the proposed techniques is demonstrated on hydrothermal test system comprising of two thermal units and two hydro power plants. the simulation resu...

Neural networks (NN) architectures can be effectively used to classify, forecast and recognize quantity of interest in, e.g., computer vision, machine translation, finance, etc. Concerning the financial framework, forecasting procedures are often used as a part of the decision making process in both trading and portfolio strategy optimization. Unfortunately training a NN is in general a challen...

Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The beltline moulding process is difficult task due to its low defects, making the material sensitive to reject. The efficient beltline moulding process involves the optimal selection of operating parameters to maximize the number of production while maintaining the requi...

The main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorith...

differential steering of in-wheel electric vehicle provides the functions of both active steering and power assisted steering with the coupling control of force and displacement transfer characteristic of system. a collaborative optimization model of the differential power-assisted steering system of in-wheel electric vehicle is built, with steering economy as the main system optimization goal,...

This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game the...

In conventional mean-variance model of portfolio optimization problem the expected return is taken as the mean of the past returns. This assumption is not correct and hence the method leads to poor portfolio optimization performance. Hence an alternative but efficient method is proposed in which the mean and variance of expected return are first predicted with a low complexity functional link a...

With the improvement of complex and uncertain finance environment, the difficulty of portfolio problem is increasing. Whether or not the projects is successfully selected, directly affects the development of the investment companies. This paper firstly talks about the finance conditions in single term investment and then extends the investment from one term to many terms. After that, a multi-pr...