توسعه بهینه تجهیزات ساخت و ساز با استفاده از برنامه نویسی خطی با ضریب فازی

Decisions made by the experts in the construction industry are usually approximate and contain some sort of imprecision. Classical linear programming (LP) model optimize the decision making situation in a crisp environment. It is difficult to get an optimum decision with imprecise information of the project environment using LP. In the construction industry, identifying optimum number of construction pieces of equipment require experts' knowledge. When certain degree of flexibility needs to be incorporated in the given model to get more realistic results, fuzzy LP is used. But when the parameters on constraints and objective function are in a state of ambiguity then the extension principle is best suited, which is based on personal opinions and subjective judgments. The objective of this paper is to identify the optimum number of pieces of equipment required to complete the project in the targeted period with fuzzy data. A realistic case study has been considered for optimization and LINGO6 has been used to solve the various non-linear equations.

Decision making in construction industry is very complex and requires deep knowledge of various construction management techniques. Operations Research (OR) techniques are widely used under such circumstances through appropriate mathematical models. Of all the models of OR Linear Programming (LP) is widely used in the construction industry. In LP models, all the information pertaining to the problem is expressed in terms of linear constraints on the decision variables where the data is precise. Many project managers arrive at feasible decisions using this model. The construction industry is clearly affected by market conditions, i.e. by ups and downs in construction activity and by the size and the type of the construction projects undertaken. It is also affected by technological innovation in fields such as materials, metallurgy, mechanical systems, electronic sensing and hydraulic controls. The industry focuses on the continuous improvement of its products by introducing advanced technology [1]. In addition, the success of any construction project depends on the efficiency and economy achieved in the construction phase of the project. The economy of the project is dependent on accurate and elaborate analysis in early stages of construction. But in real project, activities must be scheduled under limited resources, such as limited crew sizes, limited equipment amounts, and limited materials [2]. The presence of large number of interacting variables creates a problem for optimization. Decisions are mainly based on the conceptual understanding of the project by the experts and are usually vague. Therefore, consideration of imprecise and vague information becomes an important aspect in the decision making process. In view of uncertain environment prevailing in the construction industry, the ability to arrive at an optimal decision is most important for its success. Hence, decisions in the construction industry are to be taken only after evaluating the feasibility of an alternative with respect to various criteria affecting its outcome. The traditional quantitative methods of assessing the feasibility of an alternative such as payback period, rate of return, and benefit cost analysis evaluate the project from the aspect of monitory costs and benefits. But many non-quantitative factors and approximate numbers such as availability of labor, weather conditions, and number of equipments also influence the construction project. The above methods fail to incorporate the necessary qualitative parameters and uncertainty in decision making and thus it is difficult to get an optimum decision in construction industry for optimal deployment of machinery. These uncertainties can be accommodated into the analysis using Artificial Intelligence techniques such as fuzzy sets, neural networks, and expert systems. The successful application of fuzzy logic reflects the true situation of the real world, where human thinking is dominated by approximate reasoning. Hence to obtain optimality, hybrid optimization techniques are used for incorporating flexibility in decision making. Fuzzy LP makes it possible to accommodate these intangible factors in a most systematic way. The objective function is characterized by its membership value and so are the constraints. In fuzzy LP, the decision maker establishes a satisfaction criterion rather than just maximizing or minimizing the objective function. Here, each of the constraints is modeled as a fuzzy set with their respective membership values. The aim of this paper is to introduce the approximate numbers into the analysis for optimal decisions. This is done by incorporating flexibilities in the coefficients of the objective function and constraints for an optimal value. The approach described in this paper is intended to illustrate the practicability of applying fuzzy LP with fuzzy parameters to civil engineering problems and the potential advantages of the resultant information.مجموعه های فازی،