Most of the major projects in the construction industry are faced with earthwork operations, so that one can rarely find a construction project that does not have such operations. For infrastructure projects, ground operations are one of the major areas of the project, and a wide range of construction machinery is used to carry out earthwork operations. Soil operations are one of the most important and costly parts of construction projects and projects for harvesting materials from surface mines. One of the important factors in the success of large projects and projects such as dam, road, tunnel and more is the role of machinery and, consequently, the way of selecting and managing it properly. Due to the large scale of construction projects, even a small improvement in operation operations can save a lot of work. One of the major costs of infrastructure is the cost of ground operations. Therefore, the purpose of this study is to use the GENETIC algorithm to optimize heavy soil operations for proposing a method for developing a method to optimize large-scale earth-borne operations for the lowest cost of land operations.

Structural optimization is a process by which the optimum design is aimed while satisfying all the defined constraints. In recent years, using laminated composite materials in fabrication of mechanical, airspace, marine and machine industries are of major attention, due to their high strength and light weight. One of the objectives of the present paper is seeking the optimum weight and cost of a laminated composite plate, while undergoing the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill is used as the failure criterion. The multiobjective function introduced here consists of weight, cost and failure loading. Therefore, one realizes that in this work a minimal-maximal objective function is to be optimized, thus, the weight and the cost will be minimized while the failure load for all the laminated plies is to be maximized. The design variables could be any combination of thickness, orientation of fibers and the material for each layer. The thickness of the layers could be considered continuous whereas the cost and the material for each layer to be discrete. With regard to the problem in hand, a decision was made to employ a newly introduced type of optimization technique called the GENETIC ALGORITHMS, based on a new type of selection, to handle the optimization process. The theory of analysis was based on the Classical Lamination Theory (CLT). Therefore, attempts were made to produce software for the analysis and the optimum design of laminated composite plates under any combination of design parameters. A number of problems were solved under two different models. First, a multi-objective optimization procedure under a new approach was introduced, where the problem is considered unconstrained. The second model, namely the constrained optimization problem, consists of secondary valued terms which were defined as constraints, while the objective function contained only the major term, as selected by the user. The verification of the results was made satisfactorily, as a consequence of which some benchmark examples were also attempted and recorded.

1. Introduction: The steel frames with variable cross sections in large span bridges and industrial buildings are preferred due to economic considerations. In these frames, the distribution of internal forces depends on the assumed initial dimensions for cross sections of members. Based on the determined internal forces from initial structural analysis, ratio of the demand to capacity and the allowable story drift, section dimensions of the members are corrected. New analysis and design of the frame is performed with the dimensions for cross sections of members. Analysis and design are iterated until designed and assumed cross sections become the same. The aim of the trial and error method in assuming the initial dimensions, analyzing and designing of the structure is a safe structure design with minimum structural weight. Therefore, final plan is prepared after numerous iterations. The plan may be resulted in non- optimized design in some cases because of the large number of design restraints and variables, complicated distribution of internal forces and initial assumption. Therefore, it is necessary to devise an optimization method for designing such frames.

In the recent years heuristic ALGORITHMS such as GENETIC ALGORITHMS, ant colony ALGORITHMS and simulated annealing have found many applications in optimization problems. The essence of these ALGORITHMS lies in the fact that they do not depend on the specific search space to which they are applied and consequently this extends their generality. In this paper, GENETIC and ant colony ALGORITHMS are used to find the collapse load factor of two dimensional frames and their efficiency is compared to a direct approach. It is shown that when these ALGORITHMS are tuned finely and their parameters are adjusted carefully, very good results can be obtained. Four examples are presented to illustrate the efficiency of ALGORITHMS.

A model was developed for the optimal layout and design of a multiple sub-unit pressure irrigation system. An essential step in the design of a water distribution system is the determination of the layout of the links as well as the optimum size of components. The objective of this study was to develop a mathematical optimization scheme to develop optimum connections between nodes (layout) and the optimum components of a drip irrigation system. GENETIC ALGORITHMS (GA) were utilized as the main optimization approach. The optimum design of sub-units of drip irrigation on the basis of a maximum pressure variation of 20% in the manifold and laterals was carried out using an enumeration approach. The GA model minimizes the sum of capital cost plus the present value of operating cost.

This paper presents a GENETIC algorithm (GA) for solving a generalized model of single-item resource constrained aggregate production planning (APP) with linear cost functions. APP belongs to a class of production planning problems in which there is a single production variable representing the total production of all products. We linearize a linear mixed-integer model of APP subject to hiring/firing of workforce, available regular/over time, and inventory/shortage/subcontracting allowable level where the total demand must fully be satisfied at end of the horizon planning. Due to NP-hard class of APP, the real-world sized problems cannot optimality be solved within a reasonable time. In this paper, we develop the proposed GENETIC algorithm with effective operators for solving the proposed model with an integer representation. This model is optimally solved and validated in small-sized problems by an optimization software package, in which the obtained results are compared with GA results. The results imply the efficiency of the proposed GA achieving to near optimal solutions within a reasonably computational time.

This paper presents a mathematical model for designing cellular manufacturing systems (CMSs) solved by GENETIC ALGORITHMS. This model assumes a dynamic production, a stochastic demand, routing flexibility, and machine flexibility. CMS is an application of group technology (GT) for clustering parts and machines by means of their operational and / or apparent form similarity in different aspects of design and production. Most previous researches carried out in CMSs have been embodied in static production and deterministic demand states. Due to real situations of a CM model, if includes a great number of variables and restrictions requiring a long period of time, memory, and process power in order to be solved using available software packages and current optimal methods. Therefore, most researchers pay attention to novel methods. One of these methods is GENETIC ALGORITHMS (Gas). GA is a class of stochastic search techniques used for solving the NP-complete problems, such as CMSs. In this paper, a nonlinear integer model of CMS is designed in dynamic and stochastic states. Then, GENETIC algorithm is used to solve the problem and finally computational results are compared to existing optimal solutions in order to validate the efficiency of the proposed algorithm.

In this paper, GENETIC and weed ALGORITHMS are used to solve constrained mean-semi variance portfolio problem. Then AR model and simple average are compared to predict expected return of stocks.23 active stocks from June 22, 2014 to June 21, 2016 are used as our sample. The results indicate that, weed algorithm despite its longer time consuming has better performance than GENETIC algorithm. And AR (2) model has more accurate prediction than simple average in predicting expected rate of return. Finally, we compare expected and real efficient frontier, the results indicate that, in lower risk, AR model has better prediction accuracy. So in that area, we can allocate our asset with higher certainty.