A Multi-objective Collaborative Optimization Algorithm Based on Cycle Subsystem Model with Features for Product Family Design

2018 ◽  
Author(s):  
Qihao Wan ◽  
Gongzhuang Peng ◽  
Jiaxin Zhao ◽  
Heming Zhang
Keyword(s):  
Optimization Algorithm ◽  
Product Family ◽  
Collaborative Optimization ◽  
Product Family Design ◽  
Multi Objective

Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization

2013 ◽  
Vol 135 (8) ◽  
Author(s):  
Ying Liu ◽  
Soon Chong Johnson Lim ◽  
Wing Bun Lee
Keyword(s):  
Information Search ◽  
Product Family ◽  
Component Analysis ◽  
Product Family Design ◽  
Product Analysis ◽  
Laptop Computer ◽  
Multi Objective ◽  
Design Structure ◽  
Product Service ◽  
Design Requirements

Product family design (PFD) is a widely adopted strategy for product realization, especially when design requirements are diversified and multi-faceted. Due to ever-changing customer needs and the increasingly complex and integrated product design structure, PFD and its optimization have been concerned more about a rapid and contextual product analysis and variant derivation based on a multi-objective optimization scheme subject to design concerns, which are often cross disciplinary, such as product service, carbon footprint, user experience, esthetics, etc. Existing PFD modeling approaches, which are primarily structured using component attributes and assembly relationships, possess notable limitations in representing complex component and design relationships. Hence, it has restricted comprehensive PFD analysis in an agile and contextual manner. Previously, we have studied and demonstrated the feasibility of using ontology for product family modeling and have suggested a framework of faceted information search and retrieval for product family design. In this paper, several new perspectives towards PFD based on ontology modeling are presented. Firstly, new metrics of ontology-based commonality that better reveal conceptual similarity under various design perspectives are formed. Secondly, faceted concept ranking is proposed as a new ranking approach for ontology-based component search under complex and heterogeneous design requirements. Thirdly, using these ranked results, a platform selection approach that considers a maximum aggregated ranking with a minimal platform modification among various platform choices is researched. From the selected platform and the newly proposed metrics, a modified multi-objective evolutionary algorithm with an embedded feature of configuration incompatibility check is studied and deployed for the optimal selection of components. A case study of PFD using four laptop computer families is reported as our first attempt to showcase how faceted component analysis, selection, and optimization can be accomplished based on the proposed family ontology.

Platform Strategy for Product Family Design Using Particle Swarm Optimization

2011 ◽  
Author(s):  
Seung Ki Moon ◽  
KyoungJong Park ◽  
Timothy W. Simpson
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Product Family ◽  
Design Strategy ◽  
General Aviation ◽  
Multidisciplinary Optimization ◽  
Product Family Design ◽  
Swarm Optimization ◽  
Platform Design ◽  
Multi Objective

Product family design allows innovative companies to create customized product roadmaps, to manage designers and component partners, and to develop the next generation of products based on platform strategies. In product family design, problems for determining a design strategy or the degree of commonality for a platform can be considered as a multidisciplinary optimization problem with respect to design variables, production cost, company’s revenue, and customers’ satisfaction. In this paper, we investigate strategic module-based platform design to identify an optimal platform strategy in a product family. The objective of this paper is to introduce a multi-objective particle swarm optimization (MOPSO) approach to select the best platform design strategy from a set of Pareto-optimal solutions based on commonality and design variation within the product family. We describe modifications to apply the proposed MOPSO to the multi-objective problem of product family design and allow designers to evaluate varying levels of platform strategies. To demonstrate the effectiveness of the proposed approach, we use a case study involving a family of General Aviation Aircraft. The limitations of the approach and future work are also discussed.

Global Product Family Design: Multi-Objective Optimization and Design Concept Exploration

2012 ◽  
Author(s):  
Kikuo Fujita ◽  
Ken Nasu ◽  
Yuma Ito ◽  
Yutaka Nomaguchi
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Product Family ◽  
Design Concept ◽  
Mixed Integer ◽  
Product Family Design ◽  
Multi Objective ◽  
Concept Exploration ◽  
Supply Chain Configuration ◽  
Chain Configuration

Global product family design is the problem in which product variants and supply chain configuration are simultaneously designed. It has become a significant concern of manufacturing industries under globalization. Its context is not only complicated under various factors and their interactions but also vague under strategic decision making. In this paper, first, a multi-objective mixed-integer formulation of simultaneous design of module commonalization and supply chain configuration is developed under the criteria on quality, cost and delivery, and an optimization algorithm for obtaining Pareto optimal solutions is configured by using a neighborhood cultivation genetic algorithm and simplex method. Then, this paper investigates into design concept exploration on the optimality and compromise in global product family design with data-mining techniques, a principal component analysis technique and a self-organizing map technique. This paper demonstrates some numerical case studies for ascertaining the validity and promise of the proposed mathematical model and computational techniques for supporting the designer’s decision making toward the excellence in global product family design.

Multi-Objective Design Optimization for Product Platform and Product Family Design Using Genetic Algorithms

2005 ◽  
Author(s):  
Satish V. K. Akundi ◽  
Timothy W. Simpson ◽  
Patrick M. Reed
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Product Family ◽  
Optimization Techniques ◽  
Product Platform ◽  
Product Family Design ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective

Many companies are using product families and platform-based product development to reduce costs and time-to-market while increasing product variety and customization. Multi-objective optimization is increasingly becoming a powerful tool to support product platform and product family design. In this paper, a genetic algorithm-based optimization method for product family design is suggested, and its application is demonstrated using a family of universal electric motors. Using an appropriate representation for the design variables and by adopting a suitable formulation for the genetic algorithm, a one-stage approach for product family design can be realized that requires no a priori platform decision-making, eliminating the need for higher-level problem-specific domain knowledge. Optimizing product platforms using multi-objective algorithms gives the designer a Pareto solution set, which can be used to make better decisions based on the trade-offs present across different objectives. Two Non-Dominated Sorting Genetic Algorithms, namely, NSGA-II and ε-NSGA-II, are described, and their performance is compared. Implementation challenges associated with the use of these algorithms are also discussed. Comparison of the results with existing benchmark designs suggests that the proposed multi-objective genetic algorithms perform better than conventional single-objective optimization techniques, while providing designers with more information to support decision making during product family design.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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