Many Important problems In Artificial Intelligence can be defined as Constralnt Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constralnts on the values of those variables (these problems are also called Consistent Labelling Problems (CLP), in which "Labeling" nuans assigning a value to a variable.) Solution to these problems is a set of unique values for variables such that all the problem constralnts are satisfied. Several search algorithms have been proposed for solving these problems, som of which reduce the need for bacJctracklng by doing some sort of looking to future, and produce more efficient solutions. These are the so-called Forward Checking (FC), Partialiy Lookahead (PL), and Fully Lookahead (FL) algorithms. They are different In terms of the amount of looking to the future, number of backtracks thaJ are performed, and the quality of the solution that they find. In this paper, wepropose a new search algorithm we call Modified Fully Lookahead (MFL) which is Shown to be more efficient than the original Fully Lookahead algorithm

The goal of theory of constraints (TOC) is to maximize output, which is achieved by identifying and managing the critically constrained resources. To manage the constraints, Goldratt proposed five focusing steps (5FS). If we increase constrained output, the output of system will be increased. In this paper, we focus on step four of the 5FS and use the remained capacity of nonconstraint to elevate the system’s constraint.

In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization; and for finding the pareto optimal line, Epsilon-constraint Method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

Different optimization Methods are available for optimum design of structures including; classical optimization techniques and meta-heuristic optimization algorithms. However, engineers do not generally use optimization techniques to design a structure. They attempt to decrease the structural weight and increase its performance and efficiency, empirically, by changing the variables and controlling the constraints. Based on this professional engineering design philosophy, in this paper, a simple algorithm, termed the constraint Control Method (CCM), is developed and presented whereby optimum design is achieved gradually by controlling the problem constraints. Starting with oversized sections, the design is gradually improved by changing sections based on a ‘ control function’ and controlling the constraints to be below the target values. As the constraints move towards their targets, the design moves towards an optimum. The general functionality of the proposed algorithm is first demonstrated by solving several linear and nonlinear mathematical problems which have exact answers. The performance of the algorithm is then evaluated through comparing design optimization results of three, 2D steel frame benchmark problems with those from other, metaheuristic optimization solutions. the proposed Method leads to the minimum structural weight while performing much smaller number of structural analyses, compared to other optimization Methods.

Constrain is the condition of driven by necessity. The principle of constraint determines the duties of the person constrained. In jurisprudence, it includes a range of activities from cleanness to blood compensation. However, jurisprudents have discussed it mainly with regard to those that can be eaten and drunk, and presented three different interpretations: fear of losing one"s life; fear in general, including fear of losing one"s life, or one"s limb, etc.; and fear of things not related to oneself but to others. The roots of constraint are compulsion, precautionary concealment, loss and necessity. The present article deals with a kind of constraint whose source is necessiy. The Qur"an has explicitly excused the person constrained from duties provided that the oppressor shows no inclination to sin. Tradition says that "there is so unlawful act that God Almighty has not made it permissible under constraint." The intellect also knows it permissible to give up necessary duties and to do the unlawful acts under constraint. Finally, the writer presents the conditions under which the application of the principle of constrain is made permissible.

In this paper, a constrained shortest path problem (CSP) in a network is investigated, in which some special plans for each link with corresponding pre-determined costs as well as reduction values in the link travel time are considered. The purpose is to find a path and selecting the best plans on its links, to improve the travel time as most as possible, while the costs of conducting plans do not exceed the available budget. Using the Lagrangian relaxation approach, some constraints of the problem are relaxed and the Lagrangian dual problem is decomposed into two smaller sub-problems. Then, by applying the sub-gradient algorithm, a near optimal solution is determined for the original problem. Finally, by considering the proposed model on a small-sized network and on Khorasan state network, solutions for different origin-destination pairs with different parameters are determined.

Gravity data inversion is one of the important steps in the interpretation of practical gravity data. The inversion result can be obtained by minimization of the Tikhonov objective function. The determination of an optimal regularization parameter is highly important in the gravity data inversion. In this work, an attempt was made to use the active constrain balancing (ACB) Method to select the best regularization parameter for a 3D inversion of the gravity data using the Lanczos bidiagonalization (LSQR) algorithm. In order to achieve this goal, an algorithm was developed to estimate this parameter. The validity of the proposed algorithm was evaluated by the gravity data acquired from a synthetic model. The results of the synthetic data confirmed the correct performance of the proposed algorithm. The results of the 3D gravity data inversion from this chromite deposit from Cuba showed that the LSQR algorithm could provide an adequate estimate of the density and geometry of sub-surface structures of mineral deposits. A comparison of the inversion results with the geologic information clearly indicated that the proposed algorithm could be used for the 3D gravity data inversion to estimate precisely the density and geometry of ore bodies. All the programs used in this work were provided in the MATLAB software environment.

Project planning and scheduling are one of the most important issues in construction engineering and management. It is being crucial to progress developed countries. One of the major challenges in construction project management is time and cost management. The traditional view lonely is not able to meet the needs of this field. Therefore, the use of modern management approaches can be greatly helpful. Time and cost are among the important objectives of each project. Cost and time trade-off is among the major important issues in projects planning and control and the main of solving this problem is actually analysis of interaction of different types of project costs and time of the project. In this paper, a time and cost trade off project scheduling problem under budget constraint is studied. For this purpose, a meta-heuristic genetic algorithm is developed to find the optimal solution in MATLAB. Completion time sensitivity analysis is done according to different budget level. In order to validate proposed algorithm, problem is solved by GAMS and the outputs between them are compared. The results show that proposed meta heuristic algorithm is able to solve problem optimally so that differences between samples solution were zero in different budget level. In addition, by providing some other examples of small, medium and large dimensions, the ability to solve problems accurately and the accuracy of the proposed genetic algorithm was examined. The output obtained showed that the solution was the same in two Methods in small dimensions. Also, it is not possible to solve the exact problems in large dimensions and can only be solved using the proposed meta heuristic algorithm.