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.

Product Variety Deployment and its Optimization Under Modular Architecture and Module Commonalization

1999 ◽  
Author(s):  
Kikuo Fujita ◽  
Hisato Sakaguchi ◽  
Shinsuke Akagi
Keyword(s):  
Product Variety ◽  
Optimization Method ◽  
Manufacturing Firms ◽  
Integer Programming Problem ◽  
Modular Architecture ◽  
Virtual Design ◽  
Abstract Form ◽  
Multiple Products ◽  
Simultaneous Design ◽  
Television Receiver

Abstract Simultaneous design of multiple products has become essential for manufacturing firms to expand design optimization basis. Various viewpoints on this issue have been provided from investigation of practices, but their outcomes still stay on descriptive stages. This paper discusses product variety design under modular architecture and module commonalization toward a computational methodology. It reviews the module based product variety with a mixture of views from customer’s needs, functions, manufacturing modules and hierarchical representation of systems. Based on the consequent framework, this paper also mathematically formulates the problem of product variety design as a 0-1 integer-programming problem in a systematically abstract form, and develops an optimization algorithm based on a simulated annealing technique. These discussion and optimization method are applied to a virtual design problem of television receiver circuits to show their validity and promise.

Simultaneous Optimization of Product Family Sharing System Structure and Configuration

1998 ◽  
Author(s):  
Kikuo Fujita ◽  
Shinsuke Akagi ◽  
Tetsu Yoneda ◽  
Makibi Ishikawa
Keyword(s):  
Cost Model ◽  
Product Family ◽  
Aircraft Design ◽  
Product Variety ◽  
System Structure ◽  
Simultaneous Optimization ◽  
Design Development ◽  
Product Representation ◽  
Multiple Products ◽  
Independent Design

Abstract This paper discusses the optimization problem of a family of products, where multiple products sharing system structure and configuration are simultaneously designed to be optimal. This kind of problems is a typical subproblem of product variety design. It requires the considerations on how to control the commonality of modules and attributes across products and on how design targets, such as requirements, production unit numbers, etc., affect the optimality. First, we classify design tactics into independent design, similar design and same design, and describe how to use these three methods over product representation that consists of systems, modules and attributes. After discussing typical optimization situation on design methods and influence of design target deviation to product family optimality, we develop the framework for assessing the optimality and sensitivity that includes cost model for design, development, production, use, etc. Then, we demonstrate the simultaneous optimization of multiple products with simple cases in aircraft design and planning.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Allocation and Scheduling of Handling Resources in the Railway Container Terminal Based on Crossing Crane Area

2021 ◽  
Vol 13 (3) ◽  
pp. 1190
Author(s):  
Gang Ren ◽  
Xiaohan Wang ◽  
Jiaxin Cai ◽  
Shujuan Guo
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Gantry Crane ◽  
Optimization Scheme ◽  
Long Distance ◽  
Proposed Model ◽  
Hybrid Heuristic Algorithm ◽  
Searching Ability

The integrated allocation and scheduling of handling resources are crucial problems in the railway container terminal (RCT). We investigate the integrated optimization problem for handling resources of the crane area, dual-gantry crane (GC), and internal trucks (ITs). A creative handling scheme is proposed to reduce the long-distance, full-loaded movement of GCs by making use of the advantages of ITs. Based on this scheme, we propose a flexible crossing crane area to balance the workload of dual-GC. Decomposing the integrated problem into four sub-problems, a multi-objective mixed-integer programming model (MIP) is developed. By analyzing the characteristic of the integrated problem, a three-layer hybrid heuristic algorithm (TLHHA) incorporating heuristic rule (HR), elite co-evolution genetic algorithm (ECEGA), greedy rule (GR), and simulated annealing (SA) is designed for solving the problem. Numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm has excellent searching ability, and the simultaneous optimization scheme could ensure the requirements for efficiency, effectiveness, and energy-saving, as well as the balance rate of dual-GC.

scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

scholarly journals Optimal use of quadratic programming method for alignment of geodesic networks

2019 ◽  
Vol 15 (2) ◽  
pp. 38-42
Author(s):  
O.S. Goncharenko ◽  
V.N. Gladilin ◽  
L. Šiaudinytė
Keyword(s):  
Quadratic Programming ◽  
Programming Method ◽  
Quadratic Programming Method
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