Towards product platform introduction: optimising commonality of components

Companies that design and manufacture products for a wide range of related applications need to offer the right product for each use. A platform design strategy allows designing the product range based on product platforms, where some of the components and systems are common across the range whereas other components are individual for each product variant. This paper presents the problems that a company faces when trying to introduce a platform strategy and outlines a method to find suitable components to be made common. The method is shown with a simple case. The approach uses fuzzy logic to obtain a suitable criterion to assess the overall value of the product line and a genetic algorithm for finding the set of components to be made commo

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.Comment: 16 page

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

A Genetic Programming Approach to Geometrical Digital Content Modeling in Web Oriented Applications

The paper presents the advantages of using genetic techniques in web oriented problems. The specific area of genetic programming applications that paper approaches is content modeling. The analyzed digital content is formed through the accumulation of targeted geometrical structured entities that have specific characteristics and behavior. The accumulated digital content is analyzed and specific features are extracted in order to develop an analysis system through the use of genetic programming. An experiment is presented which evolves a model based on specific features of each geometrical structured entity in the digital content base. The results show promising expectations with a low error rate which provides fair approximations related to analyzed geometrical structured entities.Genetic Algorithm, Genetic Programming, Fitness, Geometrical Structured Entities, Analysis