Showing 5 results for Search
S. Ketabi,
Volume 20, Issue 1 (7-2001)
Abstract
In this paper the problem of minimum cost communication network design is considered where the costs are piecewise linear concave. Several methods are compared: Simulated Annealing method, a heuristic based on the method proposed by Minoux, and a lagrangian method based on lower bounding procedure.
K. Eshghi and S. Pasalar,
Volume 20, Issue 2 (4-2001)
Abstract
Multicommodity distribution problem is one of the most interesting and useful models in mathematical programming due to its major role in distribution networks. The purpose of this paper is to describe and solve a special class of multicommodity distribution problems in which shipment of a commodity from a plant to a customer would go through different distribution centers. The problem is to determine which distribution centers to use so that all customer demands are satisfied, production capacities are not exceeded, and the total distribution cost is minimized.The proposed problem is formulated as a mixed integer linear program and a solution technique based on tabu search is developed, implemented and successfully applied to the test problems.
Keywords: Commodity Distribution Systems, Tabu Search, Mathematical Programming
G. Ghassem-Sani and M. Namazi,
Volume 23, Issue 1 (7-2004)
Abstract
Many important problems in Artificial Intelligence can be defined as Constraint Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constraints on the values of those variables (these problems are also called Consistent Labeling Problems (CLP), in which “Labeling" means assigning a value to a variable.) Solution to these problems is a set of unique values for variables such that all the problem constraints are satisfied. Several search algorithms have been proposed for solving these problems, some of which reduce the need for backtracking by doing some sort of looking to future, and produce more efficient solutions. These are the so-called Forward Checking (FC), Partially Lookahead (PL), and Fully Lookahead (FL) algorithms. They are different in terms of the amount of looking to the future, number of backtracks that are performed, and the quality of the solution that they find. In this paper, we propose a new search algorithm we call Modified Fully Lookahead (MFL) which is Shown to be more efficient than the original Fully Lookahead algorithm
M. E. Hamedani Golshan, S. A. Arefifar, and Gh. Moslehi,
Volume 25, Issue 1 (7-2006)
Abstract
Introducing distributed generation into a power system can lead to numerous benefits including technical, economic, environmental, etc. To attain these benefits, distributed generators with proper rating should be installed at suitable locations. Given the similar effects of distributed generators and capacitor banks on operation indices of a distribution system, simultaneous assignment of best locations and sizes to both will not only lead to greatest benefits from distributed generators but also to lower reactive power capacity requirements. In this paper, a new combined planning problem involving distributed generation and Volt/VAr control means planning is formulated and solved in which the quantity of distributed generators and reactive power sources are simultaneously assigned to buses in a distribution system. Also tap positions of voltage regulators are computed such that with a given distributed generation under peak load conditions, power losses and the reactive power capacity required are minimized. Like many other problems in power network planning, the problem formulated here is a nonlinear combinatorial one. Hence, we employ the tabu search algorithm to solve the optimization problem. The results from applying the algorithm to distribution networks with 6, 19, and 33 buses are presented and compared with those obtained from employing the second order method.
Gh. Moslehi and H. Ghahar,
Volume 25, Issue 2 (1-2007)
Abstract
This paper deals with resource unconstrained project scheduling problems with the objective of maximizing the net present value (NPV) of project cash flows. Here we present a heuristic algorithm named as differential procedure (Dif_AOA).
In order to evaluate the efficiency of this algorithm, networks with node numbers between 10-1000 and network complexity coefficients between 1.3-6.6 have been generated. We have compared both the total time for solving the problem and NPV of the Dif_AOA with those of the recursive search procedure. Computational results show that the Dif_AOA performs very effectively.
Extensive analysis have been performed to evaluate the node number, complexity network coefficients(CNC), and deadline.
