scholarly journals Optimization Methodologies for Product Variety Design(3rd Report, Simultaneous Optimization Method for Module Attributes and Module Combination)

2002 ◽  
Vol 68 (668) ◽  
pp. 1329-1337 ◽  
Author(s):  
Kikuo FUJITA ◽  
Hiroko YOSHIDA
Keyword(s):  
Product Variety ◽  
Optimization Method ◽  
Simultaneous Optimization

Product Variety Optimization: Simultaneous Optimization of Module Combination and Module Attributes

2001 ◽  
Author(s):  
Kikuo Fujita ◽  
Hiroko Yoshida
Keyword(s):  
Product Variety ◽  
Optimization Method ◽  
Programming Method ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Strategy System ◽  
Multiple Products ◽  
Simultaneous Design ◽  
Design Paradigm ◽  
Quadratic Programming Method

Abstract This paper proposes a simultaneous optimization method for both module combination and module attributes of multiple products. As manufacturing competition has become restricted with high profitability and external constraints, simultaneous design of multiple products, which is called product variety design etc., becomes an important strategy. System-based optimal design paradigm is expected to be essential to rationalize such practices, since design for product variety is more complicated than one for a single product. Toward such a direction, we configure an optimization method for both module combination and module attributes across multiple products. The optimization method hybridizes a genetic algorithm, a mixed-integer programming method with a branch-and-bound technique, and a constrained nonlinear programming method, i.e., a successive quadratic programming method. In its optimization process, the first optimizes the combinatorial pattern of module commonality and similarity among different products, the second optimizes the directions of similarity on scale-based variety, and the third optimizes the continuous module attributes under the others. Finally it is applied to the simultaneous design problem of multiple airplanes to demonstrate its validity and effectiveness.

Global Product Family Design: A Mathematical Model for Simultaneous Decision of Module Commonalization and Supply Chain Configuration

2011 ◽  
Author(s):  
Kikuo Fujita ◽  
Hirofumi Amaya ◽  
Ryota Akai
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Product Family ◽  
Product Variety ◽  
Optimization Method ◽  
Product Family Design ◽  
Design Production ◽  
Supply Chain Configuration ◽  
The Given ◽  
Chain Configuration

Today’s manufacturing has become global at all aspects of marketing, design, production, distribution, etc. While product family design has been an essential viewpoint for meeting with the demand for product variety, its meaning is becoming more broad and complicated with linking product design with issues on market systems, supply chain, etc. This paper calls such a design situation ‘global product family design,’ and firstly characterizes its components and complexity. Following them, this paper develops a mathematical model for the simultaneous decision problem of module commonalization strategies under the given product architecture and supply chain configuration through selection of manufacturing sites for module production, assembly and final distribution as an instance of the problems. This paper demonstrates some numerical case studies for ascertaining the validity and promise of the developed mathematical model with an optimization method configured with a genetic algorithm and a simplex method. Finally, it concludes with some discussion on future works.

scholarly journals Gravitational Search Algorithm for NURBS Curve Fitting

2018 ◽  
Vol 3 (1) ◽  
pp. 33
Author(s):  
Erkan Ülker ◽  
İsmail Babaoğlu
Keyword(s):  
Curve Fitting ◽  
Computer Aided Design ◽  
High Performance ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Nurbs Curve ◽  
B Spline ◽  
Gravitational Search

By providing great flexibility non-uniform rational B-spline (NURBS) curves and surfaces are reason of preferability on areas like computer aided design, medical imaging and computer graphics. Knots, control points and weights provide this flexibility. Computation of these parameters makes the problem as a non-linear combinational optimization problem on a process of reverse engineering. The ability of solving these problems using meta-heuristics instead of conventional methods attracts researchers. In this paper, NURBS curve estimation is carried out by a novel optimization method namely gravitational search algorithm. Both knots and knots together weights simultaneous optimization process is implemented by using research agents. The high performance of the proposed method on NURBS curve fitting is showed by obtained results.Keywords: Non-uniform rational B-spline, gravitational search algorithm, meta-heuristic

scholarly journals Anisotropic Multicomponent Topology Optimization for Additive Manufacturing With Build Orientation Design and Stress-Constrained Interfaces

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuqing Zhou ◽  
Tsuyoshi Nomura ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Maximum Stress ◽  
Optimization Method ◽  
Stress Constraints ◽  
Material Behavior ◽  
Simultaneous Optimization ◽  
Structural Topology ◽  
Build Orientation ◽  
Interface Location ◽  
Component Interface

Abstract This paper presents a multicomponent topology optimization method for designing structures assembled from additively manufactured components, considering anisotropic material behavior for each component due to its build orientation, distinct material behavior, and stress constraints at component interfaces (i.e., joints). Based upon the multicomponent topology optimization (MTO) framework, the simultaneous optimization of structural topology, its partitioning, and the build orientations of each component is achieved, which maximizes an assembly-level structural stiffness performance subject to maximum stress constraints at component interfaces. The build orientations of each component are modeled by its orientation tensor that avoids numerical instability experienced by the conventional angular representation. A new joint model is introduced at component interfaces, which enables the identification of the interface location, the specification of a distinct material tensor, and imposing maximum stress constraints during optimization. Both 2D and 3D numerical examples are presented to illustrate the effect of the build orientation anisotropy and the component interface behavior on the resulting multicomponent assemblies.

Ant Colony Optimization Method for Product Platform Formation

2004 ◽  
Author(s):  
Rupesh Kumar ◽  
Venkat Allada ◽  
Sreeram Ramakrishnan
Keyword(s):  
Ant Colony Optimization ◽  
Product Variety ◽  
Optimization Method ◽  
Ant Colony ◽  
Product Platform ◽  
Probabilistic Search ◽  
Design Variables ◽  
Product Customization ◽  
Scaling Variables ◽  
Effective Product

Product platform concepts are often deployed to achieve product variety and hence effective product customization. One of the popular methods to achieve product variety is to scale one or more design variables called the scaling variable(s). This necessitates efficient methods for identifying the values for scaling variables. This paper presents a graph-based optimization method called Platform Ant Colony Optimization (PACO) for identifying the values of the scaling variable(s) for platform formation. In PACO, the overall decision is a function of the cumulative decisions of simple computing agents called the ‘ants.’ The method employs an autocatalytic mechanism using a probabilistic search to improve the solution iteratively. We use a universal electric motor example cited in the literature to test the efficiency of the proposed method. Simulation results on the example problem indicate that the PACO method produces promising results.

Simultaneous Optimization Method for Directly Integrating ORC with HEN to Achieve Exergy-Economy Multiobjective

2020 ◽  
Vol 59 (49) ◽  
pp. 21488-21501
Author(s):  
Yanyan Xu ◽  
Lei Wang ◽  
Yuting Chen ◽  
Shuang Ye ◽  
Weiguang Huang
Keyword(s):  
Optimization Method ◽  
Simultaneous Optimization
Sign in / Sign up

Export Citation Format

Share Document