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Packing problems are optimization problems that are concerned with searching a good arrangement of multiple items in a 2d regions. The usual objective of the allocation is to maximise the material utilization and to minimize the wasted area. This is of particular interest to industries involved with massproduction as small improvements in the layout can result in savings of material and a considerable reduction in production costs. The goal in the cutting stock problem is to determine the optimal plan to cut a 2 dimensional sheet to satisfy a set of customers demands. Cutting stock problems may involve a variety of objectives, minimizing trim loss, minimizing the number of cutting lines, maximizing profit, and so on. In order to solve the cutting stock problem, we use a cutting pattern optimizer and mathematical programming. In general, the cutting stock problem is reduce to an integer programming application. Because of its complexity, solutions to the 2 dimensional cutting stock problem have often been generated using genetic algorithms. This is due, in part, to the fact that the 2 dimensional cutting stock problem may also be reduced to a binpacking problem. The rectangular cutting stock problem is to determine how to cut a number of rectangular pieces out of a given stock of rectangular sheets. Most variants of the nesting problem is the problem of packing shapes within some regions without overlap. The cutting stock problem asks for a minimization of the area of a rectangular region. In the cutting industry a multitude of additional constraints are very often necessary. The shapes or regions can have different quality zones or even holes. The nesting problem occurs in a number of industries and it seems to have many names. In the clothing industry it is called marker making, while the metal industry call it simply nesting. In a theoretical context the problem is most often called the twodimensional irregular cutting stock problem. The 2 dimensional cutting stock problem is a classic combinatorial optimization problem in which a number of parts of various lengths must be cut from an inventory of 2d material. The twodimensional cutting stock problem may be applied in a number of industries, glass, shoeleather cutting, furniture, machinebuilding, etc. The problem is as follows : a set of rectangular pieces is to be placed on a given area of a stock material with minimum of trimloss. The cutting pattern has no overlaps of the pieces and meets all technological requirements. The stock cutting problem has gained a lot of attention in many industrial sectors. Stock Cutting Problems is essential in many industries. These problems are treated in different fields. The reduction of scrap may not only affects cost of materials used but may also reduce the costs of handling and labor. A great number of problems are essentially based on the same logical structure of the Cutting and Packing problems. The stock cutting problem is an example of a large scale optimization problem. This means that this problem requires a computing effort that increases exponentially with the problem size. Since the stock cutting problem is an efficient approximation algorithms, namely, algorithms that do not produce optimal but rather closetooptimal solutions, Cutting and packing problems are encountered in many industries. The wood, glass and paper industry are mainly concerned with the cutting of regular figures, whereas in the textile and leather industry irregular, arbitrary items are to be packed. In this kind of applications all cuts have to be accomplished from one edge of the rectangle to the opposite one. The cut has to be of a guillotine type. For such applications the problem can be formulated as a mathematical optimization program and the optimal solution can be found in terms of material yield and production costs. The quality of a cutting pattern is determined by the cutting ratio, which is defined as the ratio between the total area of the placed pieces and the total area of the stockmaterial. Single pass packing strategies involve taking the pieces in order and placing them on the sheet according to a given placement policy. This may be repeated several times for different orderings or different placements and the best solution chosen. The process will continue until shiftingdown, shiftingleft or rotation is no longer possible. In other words, the algorithm continues pushing pieces downwards and leftwards until the pieces reach stable positions. Cutting out the material in the most effective way result in reducing its stock holding which can make additional savings through improved cash flow. Although the normal motivation for effective stock cutting is financial, companies may have other objectives in implementing efficient stock cutting procedures. For example, there may be a requirement to meet certain orders within a given time. In the nesting problem it is necessary to place a number of parts into a larger sheet. In doing so, the parts must not overlap and they must stay within the sheet. The usual objective is to minimize the waste of the larger sheet. The algorithms makes use of the search procedures when deciding where pieces should be placed. The location of the next piece is calculated using the search procedures. Once the best placement has been found the piece is added to the optimization list and the next piece is placed. The packing problem consists of packing a collection of pieces onto a rectangular sheet while minimizing the unused space. The packing process has to ensure that there is no overlap between the pieces. There are thousands of different ways in which just a few different pieces can be cut out of a stock sheet. Most patterns would be extremely wasteful, and many would be difficult to cut. The Genetic algorithms are search and optimization procedures, where the search is guided towards improvement using the searching of the best item. This is achieved by extracting the most desirable items from a temporary solution list and combining them to form the next solution list. The quality of each solution is evaluated and the best items are selected for the reproduction process. Continuation of this process will result in optimal or near optimal solutions. 