The Layout of a graph is finding a linear order for the vertices of the graph such that each of its edges is assigned to exactly one of the stacks (resp. queues) based on the properties of the stack (resp. queue). In this paper, we find a relationship between the stack Layout and the queue Layout of a graph. In particular, we provide an algorithm for converting a 1-stack Layout of any graph into a 2-queue Layout of the same graph, and we prove the correction of our algorithm. This method obtains the queue Layout from a given stack Layout without considering the graph itself and its properties. We hope by generalizing and developing this method one would achieve a queue number (i. e., the smallest number of the queues required by the queue Layout of the graph) for some graph categories with almost the same upper and lower bound on their stack number.

In this paper a dynamic Layout optimization of a construction site is performed using genetic algorithm (DSL-GA). In this method considering the time table and the possibility of replacing the facilities in different time periods, Layout design is performed. The formation of a proper and logical chromosome matrix in relation with dynamic Layout is one of the main characteristics of the present model.For comparison of the performance of the present model, the problem of the dynamic site Layout of the Rosenblatt which is a special case of the DSL-GA is considered. The results of the present method are compared to those of the Rosenblatt model. These results show that the high capability of the present model in performing the dynamic Layout optimization.

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In today's economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in the product mix and demand. [1] Layout design has a significant impact on manufacturing efficiency. Initially, it was treated as a static decision but due to improvements in technology, it is possible to rearrange the manufacturing facilities in different scenarios. The Plant Layout affects on the total cost in the industry. Nowadays Dynamic Layout is becoming an important issue. Dynamic Layout is the different Layout at different time periods to satisfy the needs of industry, due to change in product, or reduced product life cycle, or change in demand. Layout problem is a quadratic assignment problem, and for larger size problems it becomes impossible to be solved. So, for solving this problem Meta heuristic algorithms are used. In this paper, Dynamic Layout problem is solved using Genetic algorithm. This Dynamic Problem is restricted up to two-time periods only.

Introduction: According to the urban green space development, water resources reduction, and other various factors, it seems necessary to use of sprinkler irrigation in irrigating the urban green space. There is no clear and principled method to determine the sprinkler system Layout of green spaces. Sprinkler Layout usually has a triangular or square pattern and is estimated automatically. In this paper, using genetic algorithm, the location of Sprinkler system installation is determined. In order to provide the maximum distribution uniformity.