Exploring alternative product modularisations with multi-objective optimisation

This paper presents a multi-objective optimisation framework for product modularisation. At the heart of the software is a custom developed genetic algorithm that is able to generate a whole range of alternative product modularisations. Once generated, the solution set is then explored to examine the inherent trade-offs needed. In this way the decision maker is able to choose the most suitable modular architecture ac-cording to the company’s strategic objectives. The focus of this paper is to illustrate the developed computerised framework using an example product: a car climate control system

A Stackelberg Solution to Joint Optimization Problems: A Case Study of Green Design

AbstractDesign of complex engineered systems often involves optimization of multiple competing problems that are supposed to compromise to arrive at equilibrium optima, entailing a joint optimization problem. This paper reveals the leader-follower decision structure inherent in joint optimization problems. A Stackelberg game solution is formulated to model a leader-follower joint optimization problem as a two-level optimization problem between two decision makers, implicating a mathematical program that contains sub-optimization problems as its constraints. A case study of coffee grinder green design demonstrates the potential of Stackelberg solution to joint optimization of modularity subject with conflicting goals

Multi-objective grouping genetic algorithm for product life-cycle optimisation

A product’s lifecycle performance (e.g. assembly, outsourcing, maintenance and recycling) can often be improved through modularity. However, modularisation under different and often conflicting lifecycle objectives is a complex problem that will ultimately require trade-offs. This paper presents a novel multi-objective modularity optimisation framework; the application of which is illustrated through the modularisation of a car climate control system. Central to the framework is a specially designed multi-objective grouping genetic algorithm (MOGGA) that is able to generate a whole range of alternative product modularisations. Scenario analysis, using the principles of the analytical hierarchical process (AHP), is then carried out to explore the solution set and choose a suitable modular architecture that optimises the product lifecycle according to the company’s strategic vision

Product lifecycle optimisation of car climate controls using analytical hierarchical process (Ahp) analysis and a multi-objective grouping genetic algorithm (mogga)

© School of Engineering, Taylor’s University. A product’s lifecycle performance (e.g. assembly, outsourcing, maintenance and recycling) can often be improved through modularity. However, modularisation under different and often conflicting lifecycle objectives is a complex problem that will ultimately require trade-offs. This paper presents a novel multi-objective modularity optimisation framework; the application of which is illustrated through the modularisation of a car climate control system. Central to the framework is a specially designed multi-objective grouping genetic algorithm (MOGGA) that is able to generate a whole range of alternative product modularisations. Scenario analysis, using the principles of the analytical hierarchical process (AHP), is then carried out to explore the solution set and choose a suitable modular architecture that optimises the product lifecycle according to the company’s strategic vision

Integrating product knowledge with modular product structures in PLM

The changes in world economy are changing very fast and the company knowledge assets and processes are becoming primary source of organization which is intellectual property that need securely stored and maintained. Challenges that companies are facing today such as need to reduce time-to- market, the development and manufacture costs, or to manage complex products with advancing technology. Due to recent global financial crisis price competition in the market has led companies to fight with competitors for limited orders. The external pressure on delivery time has increased, which again has put internal pressure on bringing down development time, which leads for collaborative work environments. Modularisation of product structures will facilitate in collaborating design activities between a diversity of disciplines in global companies, which again involves supporting computer based tools for enhancing interaction, communication and design management. Product Lifecycle Management (PLM) serves as particularly useful tool for product data and knowledge management. The deployment of a PLM tool has been seen as an important facilitator for achieving success with the modular design strategy. One of the biggest challenges in implementing new techniques is how to handle existing knowledge and / or information. This paper describes how modular product structure can be implemented in PLM and connects relevant product knowledge at different levels when the product is generated in the process of new product development. This will enable to trace the information across products to compare existing information and reuse for future products