چكيده به لاتين
In recent years, the competitive environment of business and the limited resources of organizations have made the completion of projects ahead of time with the highest level of quality and spending the least cost to the main concern of project managers and since the achievement of desirable goals in contradictory factors time cost and Quality requires a balance between them. Therefore, the time balance issue of project quality has become one of the most important issues of the day of project management science; the way and the way of problem design, the parameters considered, and how to deal with the uncertainty of matters of importance. Solving these issues is.
In this study, the relationship between the parameters is considered as a discrete and multivariate one, and the quality is also calculated from historical data and the use of regression for each mode.
Considering that optimization of the main goals may lead to fluctuations in demand for resources, in this study, the coefficient of demand curve of resources as the fourth objective function in the problem is considered and this way, the leveling of resources is also done for each answer. To deal with the uncertainty, the game theory has been assisted and has been attempted to reduce them from the initial program, which is one of the reasons for uncertainty, by reducing the parameter of the coefficient of performance for each of the executors.
In this study, in this first phase, a multi-objective genetic algorithm has been used to determine the optimal set of programs in the form of Pareto optimal responses and using the Topsis method to prioritize the optimal Pareto results obtained from the first phase for decision making (Thus, the set of optimal Pareto answers as selective options and the objectives of the problem are also considered as decision criteria.)
The results of the implementation of the designed model in this study suggest that considering the resource curve coefficient as a method for leveling the resources, in addition to realizing its main goal, which in effect preventing oscillation in the resource demand function, has had a positive effect on other main objective functions. Thus, the resource curve coefficient improved from [0.58, 0.64, 0.57] to [0.79.0.75.0.88], and subsequently Pareto optimal responses (28; 1490.70; 0.82; 0.81), respectively, were the cost, quality and coefficient of demand curve of weight) Are also relatively more in favor of other goals, and the simulation graphic graphs represent this effect.
