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With the use of computers in every field, the usage intensity of processors is increasing day by day. In order to cope with this intensity, processors need to be constantly updated and improved. Various scheduling algorithms are being developed every day in order to continuously improve and accelerate the processors and to cope with the processing intensity. The main purpose of the scheduling algorithms is to increase the efficiency of the processor. For this, the processor needs to make a planning between the pending processes. Planning can be done by giving different priorities or using different methods. In order to increase the efficiency of the processor, in this paper, existing algorithms were examined. Round Robin Algorithm was improved using Genetic Algorithm. It is aimed to find the optimum values by selecting the quantum value according to the pending processes and their parameters. When the developed new method compared with the existing standard algorithms, it is observed that the waiting time and the turnaround time were shorter than the other algorithms. [ABSTRACT FROM AUTHOR]

Most studies on permanent magnet synchronous motors (PMSMs) focus on either interior type or surface mounted magnet configurations and their design changes. This paper deals with the improvements on PMSMs' efficiency and performance after Halbach Array implementation by using meta-heuristic optimization algorithms. Analytical and numerical modeling of the motor are achieved and finite element method (FEM) is used for the numerical models. Particle swarm optimization & genetic algorithm were performed for the optimal design of the geometrical parameters of the permanent magnet (PM). The analytical and numerical results of the proposed design are compared to the analytical and/or numerical results of the reference motor. The improvements in efficiency, power/weight ratio, and cost are achieved with the optimized motor structure for the same motor requirements. [ABSTRACT FROM AUTHOR]

Single row facility layout problem is the problem of arranging departments on a straight line. The main objective of the single row facility layout problem is to minimize the total material handling cost. However, it is not explanatory to use one objective. Hence, researchers pay more attention to model facility layout problems with qualitative and quantitative objectives. In this study, a weighted goal programming approach and a NSGA-II algorithm are proposed to solve multi-objective single row facility layout problem with the objectives of minimizing total flow distance and maximizing closeness rating scores. These objectives are taken into account for single row facility layout problem, for the first time, in this paper. [ABSTRACT FROM AUTHOR]

Today, many web sites from which presents newspapers and papers to e-commerce web sites which sells products presents very large contents. It is a big problem for web sites with large information that web site should be prepared dynamically and users should read it conveniently and also it should require minimum page size. It is not possible to prepare these pages being created in real-time depending on users' choices and prefers by means of manual. In this kind of web sites there may be bad page layout and spaces. In this study, it has been realized that web pages are created in real-time with two columns and least spaces. So that, papers in the web page are arranged depending on expire time and total lines are significantly decreased. The experimental results have showed that genetic algorithm is successful in optimization of real-time web page layout. [ABSTRACT FROM AUTHOR]

Node deployment is one of the important issues to be addressed in Wireless Sensor Networks (WSNs). A proper node deployment scheme can reduce the complexity of problems in WSNs such as routing, data fusion, communication, etc. Sensors in Mobile WSNs (MWSNs) have ability to travel in the network with motion capability. This ability can extend the lifetime of WSNs by minimizing energy consumption due to travelling with optimization of deployment. This paper employs two recently proposed multi-objective optimization algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and fast and elitist genetic algorithm (NSGA-II) to find proper deployment scenarios. These multi-objective evolutionary algorithms aim at relocating mobile nodes to provide maximum sensing coverage area while minimizing the energy required for the relocation. The relocation is defined as the total travel distance of the sensors from their initial locations to their final locations. Simulation results clearly show that the non-dominated solutions have tradeoff between the travelled distance and coverage area. [ABSTRACT FROM AUTHOR]

In mass customization using resources efficiently with changing customer demands and manufacturing conditions is vital, since in today's competitive market conditions, this efficiency provides cost, time and labor savings to companies. In production environment, Process Planning (PP) and Scheduling are two functions that provide efficient usage of resources. Isolation and long time gap between these functions are main problems affecting the effiency of production. In this study, to solve the problems of productivity, it is aimed to build an integrated system which is able do PP and Scheduling in parallel and respond quickly to fluctuations in job floor. In existing integration models aiming to eliminate this isolation and time gap, it has been observed that if the search and solution space expands, the computational time increases rapidly. Therefore, in this research, a hybrid optimization approach, which can find the optimal solution rapidly is considered and a hybrid model combining both Genetic Algorithm (GA) and Artificial Neural Network (ANN) is proposed. To improve GA performance and increase the effiency of searching, clustering activities are carried out for building new cromosome structures. To increase population diversity, effective genetic operator schemes and efficient genetic represantations are used. In the integration module, 3 different GA structures created within the scope of our research are compared and the algorithm formed by clustering method shows better performance than the others. In this paper by using ANN method, a new system trained by data obtained from Scheduling is generated and this system is able to quickly respond the changes in shop floor and provide new schedules instantly. In rescheduling module, ANN's performance measures provide evidence to accuracy of Heuristic solution generated by Integration module. [ABSTRACT FROM AUTHOR]

In this paper, a scheduling problem on two identical parallel machines with sequence-dependent setup times and setup operations that performed by a single server is considered. The main objective is to minimize the makespan of the schedule. For solution procedure, an algorithm combining genetic algorithm and tabu search methodology is proposed. Firstly, the algorithm finds an initial solution using genetic algorithm module. Then, tabu search module is applied to the solution of genetic algorithm in order to find better solution. The performance of the algorithm is analyzed by comparing the results with the random search results. It has been seen that the proposed algorithm is effective to solve P2,S|STsd|Cmax scheduling problem in reasonable time, and the results are close to optimum solution values. [ABSTRACT FROM AUTHOR]

This paper presents multiobjective fuzzy genetic algorithm optimization approach to a submersible motor design. Utilizing the concept of fuzzy sets and convex fuzzy decision making, the motor design task is formulated as a multiobjective fuzzy optimization problem and solved using a genetic algorithm. The two-dimensional Finite Element Method (FEM) is then used to confirm the validity of the optimal design. The optimization results show the effectiveness and achievement of the proposed method. [ABSTRACT FROM AUTHOR]

The majority of research on scheduling problems addresses only a single criterion while the majority prodution planning problems require the decision maker to consider more than a single criterion for making a decision. In this paper, a problem with uncertain time parameters such as processing times and due dates in the m-machine flow shop scheduling problem, which has a big importance in nowadays manufacturing systems, is considered. Further a multiobjective approach based on genetic algorithm, optimizing fuzzy makespan, fuzzy maximum tardiness and fuzzy total flow time objectives simultaneously, is developed. The algorithm is developed to obtain efficient solutions. In this algorithm the values of the multiobjective function are expressed by using membership functions. The effectiveness of the algorithm is illustrated by using small size problems. The best parameter sets of the genetic algorithm are determined by using factorial design of experiment. It is shown that the algorithm produces efficient solutions for medium and large size problems in a reasonable amount of time. [ABSTRACT FROM AUTHOR]