Earth dam is a structure as homogeneous or non homogeneous forms for raising water level or water supply. Earth dam consist of different parts that one of the main parts is clay core. Choosing an optimal non permeable core which causes reduction of seepage through dam body and also being stable is neces(SA)ry. The objective of this research is to optimize the geometry of earth dam clay core such that, beside of reduction of seepage through dam body, the volume of core material is minimized. For access of this objective a consolidated model consist of a simple model which obtained by linear regression and (SA) algorithm were used. So the neces(SA)ry model has been written in Matlab 7.8 and using (SA) algorithm, to optimize the Birjand He(SA)r (SA)ngi dam. Optimal parameters such as seepage through dam body, hydraulic gradient and (SA)fety factor of stability access from model compared by the values access from the direct run of the software modeling that show a good agreement. Also the result of access by modeling have been compared by real dimensions of Birjand He(SA)r (SA)ngi dam, that cause reduction of material volume for construction core dam about 21% and shell dam about 8% with (SA)tisfactory stability.Result show that the consolidated model have successful operations and a general optimal plan design of clay core dimensions in stable condition can be achieved.

The set covering problem (SCP) is one of the location -allocation models. It s objective is to minimize the cost of facilities location-allocation in an optimal structure in such a way that each customer or zone has been covered by at least one facility. The above problem is a class of NP-Hard problems that cannot be solved by exact algorithms in a reasonable amount of computational time. Thus in this paper, a SIMULATED ANNEALING ((SA)) algorithm is proposed to solve the SCP. This algorithm is one of the efficient metaheuristics based on a neighbourhood search within solution space, acceptance of probability, and inferior solutions to scape from trap (i.e., local optimal solution). Ten test problems are solved in which associated results show the efficiency and validity: of the (SA) algorithm for solving the SCP. The validity comparisions of the results obtained the proposed algorithm are compared with the results reported by three methods named as Lingo 8 Software, Heragu's heuristic, and Genetic Algorithms (GAs).

In most service and production systems, there is a need of planning and scheduling the multi-shifts work. This paper presents a manpower scheduling problem in order to assign a work pattern (shift-day) considering system and workforce requirements. The objective is to minimize all cost incurred in the proposed mathematical model. This problem belongs to the class of NP-complete ones due to its time complexity function that it cannot be solved by traditional optimization methods in a reasonable computational time. For this reason, a number of heuristic methods have been proposed to solve such a hard problem. In this paper, a heuristic-based SIMULATED ANNEALING is designed and applied. A number of test problems are generated at random and the associated solutions are compared with the Lingo 6 software in order to evaluate and show efficiency of the proposed algorithm.      

It is a long time that the SIMULATED ANNEALING ((SA)) procedure has been introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard (SA) in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the ANNEALING schedules or the time schedule itself start. There are several parameters in the process of ANNEALING, the adjustment of which affects the overall performance. This paper focuses on the importance of initial temperature and then proposes a lower temperature with low energy to speed up the process, using an auxiliary memory to buffer the best solution. Such an ANNEALING indeed starts from a “mushy state” rather than a quite liquid molten material. The mushy state characteristics indeed depends upon the problems that (SA) is being applied to solve for. In this paper, the Mushy State SIMULATED ANNEALING (MS(SA)) is fully developed and then applied to the popular Traveling (SA)lesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of a Wise Traveling (SA)lesman, who starts from a randomly chosen city and seeks for the nearest one as the next, is also applied to initiate (SA) by a low-energy, low-temperature state. This fast method results in quite accurate solutions compared to the methods recently cited in the literature.

It is a long time that the SIMULATED ANNEALING ((SA)) procedure is introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard (SA) in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the ANNEALING schedules starts or the time schedule itself. There are several parameters in the process of ANNEALING, the adjustment of which affects the overall performance. This paper focuses on the importance of initial temperature and then proposes a lower temperature with low energy to speed up the process, using an auxiliary memory to buffer the best solution. Such an ANNEALING indeed starts from a "mushy state" rather than a quite liquid molten material. The mushy state characteristics depends on the problems that (SA) is being applied to solve for. In this paper, the Mushy State SIMULATED ANNEALING (MS(SA)) is fully developed and then applied to the popular Traveling (SA)lesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of a Wise Traveling (SA)lesman, who starts from a randomly chosen city and seeks for the nearest one as the next, is also applied to initiate (SA) by a low-energy, low-temperature state. This fast method results in quite accurate solutions compared to the methods recently cited in the literature.

In this paper we investigate the performance of SIMULATED ANNEALING ((SA)) and genetic algorithm (GA) in preventive part replacement for minimum downtime maintenance planning. Therefore some evaluation criteria are explained in order to analyze the performance of the algorithms. So it can be decided which algorithm is more suitable to apply in preventive part replacement.

Reservoir operation optimization is one of the most important issues in water resources management. In this study, reservoir operations of Doroodzan dam were carried out using (SA) optimization algorithm, yield model and Standard Operation Policy (SOP). For each method, a computer model was developed using FORTRAN. The decision variables in the (SA) model were the amount of water release in different months. The objective function is deficit of water requirement which was investigated as minimization form. Yield model was defined as an amount of water which in addition to supply drinking and industry demands, reservoir can supply agriculture water demand with a reliability of at least 80 percent. Results showed that all three methods fully supply drinking and industrial demands. The performances of (SA) algorithm and yield model in agricultural uses were better than the standard operation policy. Also the yield model supplies much more water for agricultural use than (SA) model. (SA) algorithm and yield model decreased water deficit 33.74 and 83.86 percent, respectively. Therefore, the yield model passes the best performance and the (SA) algorithm and the standard operation policy obtained the next positions, respectively.

The Markowitz issue of optimization can’t be solved by precise mathematical methods such as second order schematization, when real world condition and limitations are considered. On the other hand, most managers prefer to manage a small Portfolio of available assets in place of a huge Portfolio. It can be analogized to cardinal constrains, that is, constrains related to minimum and maximum current assets on Portfolios. This study aims to solve the problem of optimizing Portfolios with cardinality constrains, using SIMULATED ANNEALING algorithm. Therefore, by using the information of 50 companies which have been more active in Tehran’s exchange stock from April 2010 to April 2012, Portfolios’ efficient frontier has been supposed from 10 to 50. Results shows that first, SIMULATED ANNEALING algorithm has been successful in solving the above problem, and second, by selecting shares appropriately and determining suitable weights from it, smaller Portfolios with more suitable performances can be selected.