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جستجوی مقالات مرتبط با کلیدواژه

nonlinear programming

در نشریات گروه صنایع
تکرار جستجوی کلیدواژه nonlinear programming در نشریات گروه فنی و مهندسی
  • Pegah Rahimian*, Sahand Behnam

    In this paper, a novel data driven approach for improving the performance of wastewater management and pumping system is proposed, which is getting knowledge from data mining methods as the input parameters of optimization problem to be solved in nonlinear programming environment. As the first step, we used CART classifier decision tree to classify the operation mode -number of active pumps- based on the historical data of the Austin-Texas infrastructure. Then SOM is applied for clustering customers and selecting the most important features that might have effect on consumption pattern. Furthermore, the extracted features will be fed to Levenberg-Marquardt (LM) neural network which will predict the required outflow rate of the period for each operation mode, classified by CART. The result show that F-measure of the prediction is 90%, 88%, 84% for each operation mode 1,2,3, respectively. Finally, the nonlinear optimization problem is developed based on the data and features extracted from previous steps, and it is solved by artificial immune algorithm. We have compared the result of the optimization model with observed data, and it shows that our model can save up to 2%-8% of outflow rate and wastewater, which is significant improvement in the performance of pumping system.

    Keywords: Network Pressure Management, Data mining, Neural network, Nonlinear programming, Artificial Immune network
  • امیرحسین نوبیل، سید حمیدرضا پسندیده*، حجت نبوتی

    یکی از موضوعات بسیار مهم در بهینه سازی مسایل زنجیره ی تامین، مسایل تولید - توزیع است. در این مقاله یک مسئله ی تولید - توزیع برای یک شبکه ی زنجیره ی تامین دوسطحی شامل تولیدکنندگان و توزیع کنندگان ارایه شده است. مدل پیشنهادی یک برنامه ریزی غیرخطی پیوسته است که محدودیت های ظرفیت انبار و ظرفیت تولید کالاها را شامل می شود. در این مسئله ی پیشنهادی سعی می شود که مقدار محصول ارسالی و حمل توسط هر وسیله ی نقلیه با توجه به بیشینه کردن میانگین سود کالاهای ارسالی از تولیدکنندگان به توزیع کنندگان به دست آید. در این پژوهش ثابت می شود که این مسئله یک برنامه ریزی غیرخطی محدب است؛ زیرا تابع هدف مدل محدب است و محدودیت های آن نیز خطی اند. در ادامه این مسئله ی غیرخطی پیشنهادی با دو روش الگوریتم ژنتیک و روش کمینه کردن بدون محدودیت ترتیبی با رویکرد تندترین شیب حل شده است.

    کلید واژگان: مدیریت زنجیره ی تامین، مسئله ی تولید - توزیع، برنامه ریزی غیرخطی، تندترین شیب، الگوریتم ژنتیک
    A.H. Nobil, S.H.R. Pasandideh *, H. Nabovati

    Supply chain management and integration of its components are a key issue for sustainable economy. One of the most important in optimization supply chain modeling is production- distribution planning problem. Several authors have developed models for the production-distribution problem when only a percentage of solution procedure is in exact area. Most of these models were solved with the meta-heuristic method. In this paper, we are extended a production-distribution nonlinear programming problem in a two-echelon supply chain network, including manufacturers and distributors, and are solved with a mixed of exact solution and a meta-heuristic algorithm. The aim of this research is to determine the value of products delivered and the carrying amount of each vehicle such that the profit average, including sales price, production costs and transportation costs, is maximized. The model is for multiple distributors and all manufacturers in which all manufacturers are produced a type of product and are sent it to distributors. The mathematical model of the production-distribution problem is derived for which the objective function is proved to be convex, and the constraints being in linear forms are convex too. So, the proposed model is a convex nonlinear programming problem and its local maximum is the global maximum. Then, the proposed nonlinear programming problem is solved by two methods of a genetic algorithm and, Sequential Unconstrained Minimization Technique (SUMT) approach along with steepest descent method. The SUMT is the usual way in which constrained problems are converted to an unconstrained form and solved that way. It makes use of barrier methods as well to find a suitable initial point that over satisfies the inequality constraints. In this study, the genetic algorithm is used to validate the SUMT nonlinear programming approach. The numerical example is provided to illustrate the solution methods. Finally, future research and conclusion recommendations come in the last section of paper.

    Keywords: Supply chain, production-distribution problem, nonlinear programming, steepest descent method, genetic algorithm
  • Reliability analysis of a robotic system using hybridized technique
    Naveen Kumar *, Komal Komal, J. S. Lather

    In this manuscript, the reliability of a robotic system has been analyzed using the available data (containing vagueness, uncertainty, etc). Quantification of involved uncertainties is done through data fuzzification using triangular fuzzy numbers with known spreads as suggested by system experts. With fuzzified data, if the existing fuzzy lambda–tau (FLT) technique is employed, then the computed reliability parameters have wide range of predictions. Therefore, decision-maker cannot suggest any specific and influential managerial strategy to prevent unexpected failures and consequently to improve complex system performance. To overcome this problem, the present study utilizes a hybridized technique. With this technique, fuzzy set theory is utilized to quantify uncertainties, fault tree is utilized for the system modeling, lambda–tau method is utilized to formulate mathematical expressions for failure/repair rates of the system, and genetic algorithm is utilized to solve established nonlinear programming problem. Different reliability parameters of a robotic system are computed and the results are compared with the existing technique. The components of the robotic system follow exponential distribution, i.e., constant. Sensitivity analysis is also performed and impact on system mean time between failures (MTBF) is addressed by varying other reliability parameters. Based on analysis some influential suggestions are given to improve the system performance.

    Keywords: Reliability analysis, Robotic system, Nonlinear programming, Fuzzy lambda- tau technique
  • اژدر سلیمانپور باکفایت *
    در این مقاله، یک روش ابتکاری برای حل مسائل بهینه سازی غیرخطی که دارای قیود و تابع هدف محدب هستند طراحی شده است. در این روش، یک تابع هزینه تعریف می گردد، سپس مقادیر متغیرها طوری تعیین می شوند که آن تابع هدف مینیمم شود. جهت ایجاد تابع هزینه مناسب، از شرایط بهینگی K.K.T استفاده شده است. مینیمم سازی تابع هزینه با استفاده از روش بهینه سازی بدون مشتق نلدرمید انجام شده است. کاربردها نشان می دهند کارایی این روش برای مسائل با ابعاد بزرگ مانند R^10 نسبت به روش های مشابه بیشتر است و به کارگیری این روش، آسان تر از روش های مشابه است. توسط مثال هایی کارایی روش توضیح داده شده است.
    کلید واژگان: روش نلدرمید، شرایط بهینگی KKT، بهینه سازی نامقید، برنامه ریزی غیرخطی
    Azhdar Soleymanpour Bakefayat *
    In this paper, A innovative method designed to solving nonlinear optimization problems with convex object function and constrained. In this method, we define an cost function and we find variables to minimization of cost function. For create properly cost function we use K. K. T. optimal conditions. We used Nelder-Mead without derivative optimization method to minimization of cost function. When, dimensions of problem is about 10, application shows that efficiency of Nelder-Mead method is more than the other methods. Using new mathod is easier than the similar methods. By several examples efficiency of new method are verified.
    Keywords: Nelder-Mead method, KKT optimal conditions, Unconstrained optimization, Nonlinear Programming
  • Mohammad Ebrahim Karbaschi, Mohammad Reza Banan
    Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochastic optimization problem is transformed intoan equivalent deterministic problem,which can be solved byany known classical methods (interior penalty method is applied here).The paper mainly focuseson investigatingthe effect of applying various probability functions distributions(normal, gamma, and exponential) for design variables. The following basic required equations to solve nonlinear stochastic problems with various probability functionsfor random variables are derived and sensitivity analyses to studythe effects of distribution function typesand input parameterson the optimum solution are presented as graphs and in tables by studyingtwoconsidered test problems. It is concluded that thedifference between probabilistic and deterministic solutions toa problem, when the normal distribution ofrandom variables isused, is very different fromthe results when gamma and exponential distribution functions are used. Finally, it is shownthat the rate of solution convergence tothe normal distribution is faster than the other distributions.
    Keywords: Stochastic programming, Sensitivity Analysis, Linear programming, Nonlinear programming, Exponential, Gamma, normal probability functions
  • Mohammad Mehdi Movahedi *, Mohsen Khounsiavash, Mahmood Otadi, Maryam Mosleh

    Tolerancing conducted by design engineers to meet customers’ needs is a prerequisite for producing high-quality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not something new, engineers often use known distributions, including the normal distribution. Yet, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. In this paper, we use generalized lambda distribution for design and analyses component tolerance. We use percentile method (PM) to estimate the distribution parameters. The findings indicated that, when the distribution of the component data is unknown, the proposed method can be used to expedite the design of component tolerance. Moreover, in the case of assembled sets, more extensive tolerance for each component with the same target performance can be utilized.

    Keywords: Nonlinear programming, Generalized lambda, distribution (GLD), Tolerancing, Percentile matching, Estimates
  • Yahia Zare Mehrjerdi *
    It is the purpose of this article to introduce a linear approximation technique for solving a fractional chance constrained programming (CC) problem. For this purpose, a fuzzy goal programming model of the equivalent deterministic form of the fractional chance constrained programming is provided and then the process of defuzzification and linearization of the problem is started. A sample problem is presented for clarification purposes.
    Keywords: Nonlinear Programming, Chance Constrained Programming, Linear Approximation, Fuzzy Goal Programming, Optimization
  • Payel Ghosh *, Tapan Kumar Roy

    A very useful multi-objective technique is goal programming. There are many methodologies of goal programming such as weighted goal programming, min-max goal programming, and lexicographic goal programming. In this paper, weighted goal programming is reformulated as goal programming with logarithmic deviation variables. Here, a comparison of the proposed method and goal programming with weighted sum method is presented. A numerical example and applications on two industrial problems have also enriched this paper.

    Keywords: goal programming, Geometric programming, Pareto optimality, Nonlinear Programming
  • آسیه وریانی، پرویز فتاحی
    در این تحقیق یک مدل اندازه نمونه دو سطحی شامل یک تولیدکننده و یک انبارمرکزی یکپارچه با اضافه کردن محدودیت تاثیرپذیری تقاضا از متوسط درصد کمبود مورد بررسی قرار گرفته است. در این مدل، انبارمرکزی با تقاضای تصادفی مشتری روبرو می باشد و هزینه سفارش دهی انبار با سرمایه گذاری قابل کاهش می باشد. کارخانه دارای دو بخش مونتاژ و پردازش می باشد. مواد به دو صورت وارد بخش مونتاژ می گردند؛ گونه ای از مواد تحت عنوان مواد پردازش شده از واحد پردازش و برخی دیگر تحت عنوان مواد اولیه آماده، از بیرون کارخانه وارد مرحله مونتاژ می گردند. در مرحله مونتاژ تحت فرایندهای لازم، کالای نهایی تولید می شود. پس از ارایه یک مدل برنامه ریزی غیرخطی، از دو روش شاخه وکران و روش گرادیان کاهشی تعمیم یافته برای حل مدل استفاده شده است. سپس به کمک آزمایش های عددی کارایی روش های پیشنهادی مورد ارزیابی قرار می گیرد.
    کلید واژگان: زنجیره تامین یکپارچه، مدل های موجودی، برنامه ریزی غیرخطی، تقاضای احتمالی
    A. Varyani, P. Fattahi
    In this article, we integrate production and maintenance to two stage lot sizing models with a central warehouse and a manufacturer by adding a new constraint in which the demand is depend on the average percent of product shortage. The central warehouse faces stochastic demand and is controlled by continuous review (R,Q) policy. Additionally, Warehouse ordering cost can be reduced through further investment. In manufacturer system, assembly line needs two types of raw materials before converting them in to the finished product. One of them requires preprocessing inside the facility before the assembly operation and the other comes directly from outside supplier in assembly line. To analyze, we formulate a nonlinear cost function to aggregate all the costs. For doing this, we use Branch and Bound and nonlinear optimization technique– Generalized Reduced Gradient methods and compare the optimal value of these methods. The model is illustrated through numerical examples and sensitivity analyses on cost functions are presented.
    Keywords: Integerated supply chain, Inventory models, Nonlinear programming, Stochastic demand
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