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8th International Conference on Soft Computing and Pattern Recognition (SoCPaR) / 8th International Conference on Computational Aspects of Social Networks (CASoN)

作者： Goswami, Magnanil Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata 700064 India

ISBN: (纸本)9783319606187;9783319606170

In this discourse, estimation of optimum device doping concentration of symmetric-gate FinFET is carried out, which in turn, is subjected to two pellucid circuit instances for optimization using geometric programming. Initially, some of the technology specific device attributes concerning the current-voltage (I-V) characteristics are formulated as the objective and constraint functions of a geometric program to augment the optimum doping concentration. Some of the optimum parameters are thereby used in different circuit topologies to estimate their optimal behavior with least human oversight. Prompt prediction of globally optimum design solution, being the foremost trait of geometric programming, is recapitulated in this context of the relatively underexplored realm of FinFET devices.

关键词： Constraint Doping concentration geometric programming Objective Optimum Symmetric-gate FinFET

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geometric programming Formulation for Gate Sizing with Pipelining Constraints 28

Geometric Programming Formulation for Gate Sizing with Pipel...

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28th International Conference on VLSI Design (VLSID) / 14th International Conference on Embedded Systems

作者： Naidu, Srinath R. Int Inst Informat Technol Bangalore 560100 Karnataka India

ISBN: (纸本)9781479966585

We propose a novel framework to solve the combined retiming/gate sizing problem in the context of optimization of acyclic pipelines. The adjustment of sizes to gates in a combinational circuit is a continuous problem, solvable by a variety of convex optimization tools provided the delay model for each gate is placed in a convex framework. Retiming is a discrete problem since it involves physically moving registers from one location to another. In this paper, we enhance an existing convex optimization framework proposed by Boyd et al [1] to handle registers as 0-1 variables. We solve the relaxed formulation as a geometric program and glean valuable information about the circuit's performance. Another significant contribution of our paper is that we show that our problem is NP-hard.

关键词： combinational circuits computational complexity convex programming flip-flops geometric programming sizing (materials processing) NP-hard problems acyclic pipelining constraint optimization combinational circuit performance combined retiming-gate sizing problem convex optimization tool framework delay model geometric programming formulation glean valuable information physically moving registers Capacitance Delays Linear programming Logic gates Pipeline processing Registers Resistance NP-hard convex optimization gate sizing retiming Nondeterministic polynomial-time hard geometric programming Pipeline processing Combinational circuits Registers flip-flops convex programming Logic gates linear programming Convex function capacitance gates complexity classes Gate dimension Circuit optimization

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geometric programming-Based Automation of Floorplanning in ASIC Physical Design

Geometric Programming-Based Automation of Floorplanning in A...

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International Conference on Communications and Cyber Physical Engineering (ICCCE)

作者： Dastagiri, N. Bala Kishore, K. Hari Gunjan, Vinit Kumar Reddy, M. Janga Fahimuddin, S. Koneru Lakshamaiah Educ Fdn Guntur Andra Pradesh India CMR Inst Technol Hyderabad Telangana India AITS Rajampet Andra Pradesh India

ISBN: (纸本)9789811302121;9789811302114

The ASIC physical design process is a complex optimization problem with various objectives such as minimum chip minimum wire length, area, minimum of vias. The main aims of optimization are to improve the performance and reliability etc., of the ASIC design process. The objectives mentioned can be achieved through the effective implementation of floorplanning before other steps are implemented. In this study, a pseudo code is developed for floorplanning using geometric programming to achieve global optima. This study uses simulations performed using MATLAB GGP toolbox.

关键词： ASIC physical design Floorplanning geometric programming

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Multi-item inventory model with quantity-dependent inventory costs and demand-dependent unit cost under imprecise objective and restrictions: a geometric programming approach

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PRODUCTION PLANNING & CONTROL 2000年第8期11卷 781-788页

作者： Das, K Roy, TK Maiti, M Vidyasagar Univ Dept Appl Math & Oceanol & Comp Engn Midnapur 721102 India Deemed Univ BE Coll Dept Math Howrah India

A multi-item inventory model with constant demand and infinite replenishment is developed under the restrictions on storage area, total average shortage cost and total average inventory investment cost. These restrictions may be precise or imprecise. Here, it is assumed that inventory costs are directly proportional to the respective quantities, and unit purchase/production cost is inversely related to the demand. Restricted shortages are allowed but fully backlogged. First, the problem is formulated in crisp environment taking the deterministic and precise inventory parameters. It is solved by both geometric programming (GP) and gradient-based non-linear programming (NLP) methods. Later, the problem is formulated with fuzzy goals on constraints and objectives where impreciseness is introduced through linear membership functions. It is solved using the fuzzy geometric programming (FGP) method. The inventory models are illustrated with numerical values and compared with the crisp results. A sensitivity analysis on the optimum order quantity and average cost is also presented due to the variation in the tolerance of total average inventory investment cost and total average shortage cost following Dutta et al., 1993, Fuzzy Sets and Systems, 55, 133-142.

关键词： geometric programming inventory control imprecise constraint fuzzy mathematics

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A new fuzzy multi-objective programming: Entropy based geometric programming and its application of transportation problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第2期173卷 387-404页

作者： Islam, Sahidul Roy, Tapan Kumar Bengal Engn Coll DU Dept Math Howrah 711103 W Bengal India

In this paper, we introduce a fuzzy mathematical programming with generalized fuzzy number as objective coefficients. We also examine a transportation problem with additional restriction. There is an additional entropy objective function in the transportation problem besides transportation cost objective function. Using new fuzzy mathematical programming, this multi-objective entropy transportation problem with generalized trapezoidal fuzzy number costs has been reduced to a primal geometric programming problem. Pareto optimal solution of the transportation model is found. Numerical examples have been provided to illustrate the problem. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multi-objective programming transportation entropy generalized fuzzy number geometric programming

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Profit maximization with quantity discount: An application of geometric programming

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APPLIED MATHEMATICS AND COMPUTATION 2007年第2期190卷 1723-1729页

作者： Liu, Shiang-Tai Vanung Univ Grad Sch Business & Management Tao Yuan 320 Taiwan

Profit maximization is an important issue to the firms that pursue the largest economic profit possible. Traditionally, profit maximization problem is solved by differentiating with respect to input prices. The total differentiation of the first-order conditions might give complicated equations difficult to handle. Different from traditional studies, this paper considers input quantity discount and employs geometric programming technique to derive the objective value for the profit-maximization problem. The geometric programming approach not only gives the global optimum solution but also provides the information that is able to discover the relationship between profit maximization and returns to scale in the solution process. No differentiation is required. Moreover, geometric programming can provide a computationally attractive view of sensitivity analysis for the changes in parameters. Examples are given to illustrate the idea proposed in this paper. (c) 2007 Elsevier Inc. All rights reserved.

关键词： geometric programming profit quantity discount

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Dynamic optimization of multipass milling operations via geometric programming

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INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE 1999年第2期39卷 297-320页

作者： Sonmez, AI Baykasoglu, A Dereli, T Filiz, IH Gaziantep Univ Dept Mech Engn TR-27310 Gaziantep Turkey

This paper outlines the development of an optimization strategy to determine the optimum cutting parameters for multipass milling operations like plain milling and face milling. The developed strategy is based on the "maximum production rate" criterion and incorporates eight technological constraints. The optimum number of passes is determined via dynamic programming, and the optimal values of the cutting conditions are found based on the objective function developed for the typified criterion by using a non-linear programming technique called "geometric programming". This paper also underlies the importance of using optimization strategies rather than handbook recommendations as well as pointing out the superiority of the multipass over the single-pass optimization approach. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： multipass operations milling dynamic programming geometric programming constrained optimization

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PROFIT MAXIMIZATION INVENTORY MODEL WITH UNCERTAIN DEMAND AND COSTS: A geometric programming APPROACH

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第5期20卷 1772-1801页

作者： Mondal, Tapas Ojha, Akshay Kumar Pani, Sabyasachi IIT Bhubaneswar Bhubaneswar 752050 Odisha India

In this paper, we formulate a new profit maximization inventory model in which demand is a power function of selling price, marketing expen-diture, and service expenditure, and unit cost is a power function of order quantity. In reality, the exponents of selling price, marketing expenditure, and service expenditure in a demand function and the exponent of order quantity in a unit cost function may not be exact. So, we consider the model in an uncertain-based framework with the assumption that the exponents of selling price, marketing expenditure, and service expenditure in a demand function and the exponent of order quantity in a unit cost function are uncertain. Based on uncertainty theory, we develop a method to find the equivalent crisp model. In addition, we use the geometric programming method to solve the crisp model. Furthermore, we give a suitable numerical example to show how the proposed methodology can be used to solve the model for different uncertainty distributions at different uncertainty levels. In the numerical example, we use five different uncertainty distributions and figure out the selling price, market-ing cost, service cost, order quantity, and profit with respect to the uncertainty level for each uncertainty distribution. Finally, we perform a sensitivity anal-ysis for a better understanding of the proposed model.

关键词： Inventory uncertainty distributions planning under uncertainty profit maximization geometric programming

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MULTI-OBJECTIVE geometric programming PROBLEM WITH KARUSH-KUHN-TUCKER CONDITION USING ε-CONSTRAINT METHOD

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RAIRO-OPERATIONS RESEARCH 2014年第4期48卷 429-453页

作者： Ojha, A. K. Ota, Rashmi Ranjan Indian Inst Technol Sch Basic Sci Bhubaneswar 751013 Odisha India SOA Univ ITER Dept Math Bhubaneswar 751030 Odisha India

Optimization is an important tool widely used in formulation of the mathematical model and design of various decision making problems related to the science and engineering. Generally, the real world problems are occurring in the form of multi-criteria and multi-choice with certain constraints. There is no such single optimal solution exist which could optimize all the objective functions simultaneously. In this paper, epsilon-constraint method along with Karush-Kuhn-Tucker (KKT) condition has been used to solve multiobjective geometric programming problems(MOGPP) for searching a compromise solution. To find the suitable compromise solution for multi-objective geometric programming problems, a brief solution procedure using epsilon-constraint method has been presented. The basic concept and classical principle of multi-objective optimization problems with KKT condition has been discussed. The result obtained by epsilon-constraint method with help of KKT condition has been compared with the result so obtained by Fuzzy programming method. Illustrative examples are presented to demonstrate the correctness of proposed model.

关键词： geometric programming Karush-Kuhn-Tucker (KKT) condition epsilon-constraint method fuzzy programming duality theorem Pareto optimal solution

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SENSITIVITY ANALYSIS IN POSYNOMIAL geometric-programming

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1988年第1期57卷 85-121页

作者： KYPARISIS, J 1.Department of Decision Sciences College of Business Administration Florida International University Miami Florida

Sensitivity analysis results for general parametric posynomial geometric programs are obtained by utilizing recent results from nonlinear programming. Duality theory of geometric programming is exploited to relate the sensitivity results derived for primal and dual geometric programs. The computational aspects of sensitivity calculations are also considered.

关键词： Sensitivity analysis geometric programming duality nonlinear programming

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