The ONE-DIMENSIONAL CUTTING STOCK problem has so many applications in lots of industrial processes and during the past few years has attracted so many researchers’ attention all over the world. In this paper a metaheuristic method based on ACO is presented to solve this problem.In this algorithm, based on designed probabilistic laws, artificial ANTs do select various cuts and then select the best patterns. Also because of the problem framework, effective improvements have been made to problem solving process. The results of that algorithm in sample problems, show high efficiency of the algorithm in different levels of problems.

Nowadays, ONE-DIMENSIONAL CUTTING STOCK Problem (1D-CSP) is used in many industrial processes and recently has been considered as one of the most importANT research topic. In this paper, a metaheuristic algorithm based on the Simulated Annealing (SA) method is represented to minimize the TRIM LOSS and also to focus the TRIM LOSS on the minimum number of large objects. In this method, the ID-CSP is taken into account as Item-oriented and the authors have tried to minimize the TRIM LOSS concentration by using the simulated annealing algorithm and also defining a virtual cost for the TRIM LOSS of each STOCK. The solved sample problems show the ability of this algorithm to solve the ID-CSP in many cases.

Appropriate methods for prediction of future trends in capital markets lead to a better decision making for market participANTs. Classic methods don not perform well in prediction of financial markets due to the nonlinear and chaotic nature of these markets. Moreover, information extracted from data disappear quickly, so these method are not workable in the long run.The goal of this paper is using ANT COLONY optimization algorithm for prediction of Tehran STOCK Exchange's total return index (TEDPIX) data.First, we used the largest Lyapunov exponent to the consider chaotic nature of TEDPIX and then the ANT COLONY optimization paradigm we employed to analyze topological structure of the attractor behind the given time series and to single out the typical sequences corresponding to the different parts of the attractor. The typical sequences were used to predict the time series values.Eventually with respect to MSE, RMSE and MAE, ACO has lower error than GARCH and EGARCH models; however, Diebold Marino test shows that there is no difference if we use ACO or GARCH models for prediction; this represents that differences of error for different models in this article are very little. This article with detachment of typical sequences allows a structural method for prediction of chaotic data. So in prediction of data with many fluctuations and in long term, it can result to a better predictions. The algorithm of this paper is able to provide robust prognosis to the periods comparable with the horizon of prediction.

Location-Routing problems involve locating a number of facilities among can-didate sites and establishing delivery routes to a set of users in such a way that the total system cost is minimized. A special case of these problems is Hamiltonian p-Median problem (HpMP). This research applies the metaheuristic method of ANT COLONY optimization (ACO) to solve the HpMP. Modifications are made to the ACO algorithm used to solve the traditional vehicle routing problem (VRP) in order to allow the search of the optimal solution of the HpMP. Regarding this metaheuristic algorithm a computational experiment is reported as well.

In this paper an ANT COLONY (ACO) algorithm is developed to solve aircraft recovery while considering disrupted passengers as part of objective function cost. By defining the recovery scope, the solution always guarANTees a return to the original aircraft schedule as soon as possible which means least changes to the initial schedule and ensures that all downline affects of the disruption are reflected. Defining visibility function based on both current and future disruptions is one of our contributions in ACO which aims to recover current disruptions in a way that cause less consequent disruptions. Using a real data set, the computational results indicate that the ACO can be successfully used to solve the airline recovery problem.

CUTTING STOCK problems are within knapsack optimization problems and are considered as a non-deterministic polynomial-time (NP) -hard problem. In this paper, two-dimensional CUTTING STOCK problems were presented in which items and STOCKs were rectangular and CUTTINGs were guillotine. First, a new, practical, rapid, and heuristic method was proposed for such problems. Then, the software implementation and architecture specifications were explained in order to solve guillotine CUTTING STOCK problems. This software was implemented by C++language in a way that, while running the program, the operation report of all the functions was recorded and, at the end, the user had access to all the information related to CUTTING which included order, dimension and number of CUTTING pieces, dimension and number of waste pieces, and waste percentage. Finally, the proposed method was evaluated using examples and methods available in the literature. The results showed that the calculation speed of the proposed method was better than that of the other methods and, in some cases, it was much faster. Moreover, it was observed that increasing the size of problems did not cause a considerable increase in calculation time.In another section of the paper, the matter of selecting the appropriate size of sheets was investigated; this subject has been less considered by far. In the solved example, it was observed that incorrect selection from among the available options increased the amount of waste by more than four times. Therefore, it can be concluded that correct selection of STOCKs for a set of received orders plays a significANT role in reducing waste.