Sharif Digital Repository

In this paper, a tolerance allocation approach is proposed which attempts to select tolerances for individual dimensional variables of a product in a way that minimizes the cost while maintaining critical design requirements. The algorithm uses a reciprocal power cost-tolerance function which incorporates variable sensitivities computed in a DLM-based tolerance analysis technique. The process capabilities are applied as constraints and the genetic algorithm is used to find the optimum solution. The method is applied to a heavy duty brake problem and its results are presented. Copyright © 2005 SAE International  

In this paper, a numerical optimization method has been carried out to optimize the shape and efficiency of a propeller. For analysis of the hydrodynamic performance parameters, an extended vortex lattice method was used by implementing an open-source code which is called OpenProp. The method of optimization is a non-gradient based algorithm. After a trade-off between a few gradient-based and non-gradient based algorithms, it is found that the problem of being trapped in local optimum solutions can be easily solved by choosing nongradient based ones. Hence, modified Genetic algorithm is used to implement the so-called hydrodynamic performance analyzer code. The objective function is to... 

A comprehensive approach is proposed to manage landing sequences and their associated trajectories for an arbitrary number of aircraft in the vicinity of a controlled aerodrome. The current approach, similar to that of "first come, first served," could consider different types of priorities as well as emergencies. The approach is especially useful to combine unstructured free-flight trajectories with structured ones during the approach phase of the flight A comprehensive cost function considers the relative size of all aircraft together with their relative speeds and flight directions. This helps optimize the amount of fuel consumption while respecting separation minima. Resulting... 

Fault indicator (FI) plays a crucial rule in enhancing service reliability in distribution systems. This device brings substantial benefits for fault management procedure by speeding up fault location procedure. This paper intends to develop a new optimization model to optimally deploy FI in distribution systems. The proposed model aims to consider a tradeoff between the total customer interruption cost and FI relevant costs, including capital investment, installation, and maintenance costs. As the main contribution of this paper, the problem is formulated in mixed integer programing, which guarantees global optimum solution achieved in an effective runtime. Moreover, the model takes... 

Hardware/Software (HW/SW) partitioning plays one of the most important roles in Co-design of embedded systems that is due to made at the beginning of the cycle of the design. The ultimate designed system's performance strongly depends on partitioning. Therefore, achieving the optimum solutions can reduced the systems cost and delay. On the other hand, Genetic algorithms (GAs) are powerful function optimizers that are used successfully to solve problems in many different disciplines. Parallel GAs (PGAs) are particularly easy to implement and promise substantial gains in performance and results. In this paper, we present a PGA-based approach to achieve near optimal solutions for HW/SW... 

In this paper the setup assembly line balancing and scheduling problem (SUALBSP) is considered. Since this problem is NP-hard, a hybrid genetic algorithm (GA) is proposed to solve the problem. This problem involves assigning the tasks to the stations and scheduling them inside each station. A simple permutation is used to determine the sequence of tasks. To determine the assignment of tasks to stations, the algorithm is hybridized using a dynamic programming procedure. Using dynamic programming, at any time a chromosome can be converted to an optimal solution (subject to the chromosome sequence). Since population diversity is very important to prevent from being trapped in local optimum... 

In the present research the aging behavior of Al6061 alloy and Al6061/SiCp composite fabricated by powder metallurgy method was investigated. The solution treatment of the samples were carried out at 527°C for 2, 3and 4 h followed by aging at 180°C for different aging times between 1 and 10 h. The existence of SiC particles led to increasing the peak hardness of the composite. The peak hardness of the composite took place at shorter times than that of the 6061 alloy for the samples solution treated for 3and 4 h, but took place at longer times for the samples solution treated for 2 h. The optimum solution treating time was about 3 h for both the composite and the 6061 alloy that led to the... 

In modern power systems, technical virtual power plants (TVPPs) play an important role enabling presence of distributed energy resources (DERs) in electricity markets. In this paper, strategy of using the available energy resources for a TVPP is put under investigation. A new optimization framework is presented for problem of TVPP energy scheduling by taking operational constraints of distribution network into account. In the proposed model, photovoltaic (PV) units and micro turbines along with the electric vehicles (EVs) are scheduled in such a way that the profit of TVPP owner would be maximized. The uncertainty in output generation of PV units is modeled by adopting fuzzy c-means (FCM)... 

This paper proposes a new methodology to find the most economic system configuration and energy management strategy for Li-ion battery based off-grid renewable energy systems. A system level macroscopic model and a microscopic battery lifetime prediction model are incorporated into the optimization framework to simulate hourly performance of the system. Due to the computational efficiency of the model, optimization is carried out using enumerative method (evaluating all the possible combinations of components and control strategies) to ensure finding the global optimum solution of the problem. To investigate the effectiveness of the proposed methodology, the optimization results are compared... 

New penalty functions, which have better convergence properties, as compared to the commonly used exterior and interior penalty functions, have been proposed in this paper. The convergence behavior and accuracy of ordinary penalty functions depend on the selection of appropriate penalty parameters. The optimization of ordinary penalty functions is accomplished after several rounds of optimization where, at each round a different but fixed value of penalty parameter is used. While some useful hints and rules for the selection of suitable penalty parameter values have been provided by different authors, the objective of this paper has been to improve this procedure by including the penalty... 

Pipeline Analog to Digital Converter (ADC) design processes include several redesign steps to achieve the optimum solution. Hence, designers prefer to use automated algorithms for this purpose. In this paper, an automated algorithm for CAD tool is presented considering the trade-off between yield and power consumption for pipeline ADCs. This automated algorithm benefits from multiple degrees of freedom including the system level down to transistor level parameters, which helps CAD tools to find the optimized solution. It allows designers to choose an optimum scenario considering the trade-off between yield and power consumption. To evaluate the capabilities of this algorithm, a 10-bit... 

In this paper, joint dynamic frequency selection and transmit power control problem with the goal of power minimization for a target SINR is investigated for cognitive radio networks. The optimization problem is formulated and three algorithms are proposed: A centralized algorithm based on graph coloring, and two distributed algorithms based on game theory; one for coordinated and the other for non-coordinated scenarios. Performance of the proposed algorithms in terms of power minimization and convergence speed is evaluated through simulations. Comparing the performance with the optimum solution, all three algorithms show near-optimum power minimization performance for the simulated... 

We study permutation betting markets, introduced by Chen, Fortnow, Nikolova, and Pennock [3]. For these markets, we consider subset bettings in which each trader can bet on a subset of candidates ending up in a subset of positions. We consider the revenue maximization problem for the auctioneer in two main frameworks: the risk-free revenue maximization (studied in [3]), and the probabilistic revenue maximization. We also explore the use of some certain knowledge or extra information about the possible outcomes of the market. We first show that finding the optimal revenue in the risk-free model for the subset betting problem is inapproximable. This resolves an open question posed by Chen et...