Constraint-handling techniques for generative product design systems in the mass customization context

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a timeconsuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms

Evaluation of Frame- and Feature-based Software Product Line Tools from the Viewpoint of Mass Customization by End Users

Customers expect Information and Communications Technology (ICT) platforms and applications to deliver services customized to their needs. Software product line (SPL) paradigm uses platforms and variability management to develop mass-customizable software applications. The paradigm necessitates effective software tools to manage platform and application artifacts and traceability and variability information. This paper constructs a comprehensive but lightweight tool evaluation framework and uses it to evaluate two tools, XML-based variant configuration language (XVCL) and FeaturePlugin – a feature modeling plug-in for Eclipse Integrated Development Environment. The paper analyzes the capabilities of the tools for enabling the mass customization of software applications by the end users performing complex workflows. Both the XVCL and FeaturePlugin tool envisage more efficient software system development by means of reusability, support for abstraction, and configuration mechanisms. Future research is needed to refine and validate the evaluation framework in the context of other types of SPL tools

Restart Strategies for Constraint-Handling in Generative Design Systems

Product alternatives suggested by a generative design system often need to be evaluated on qualitative criteria. This evaluation necessitates that several feasible solutions which fulfill all technical constraints can be proposed to the user of the system. Also, as concept development is an iterative process, it is important that these solutions are generated quickly; i.e., the system must have a low convergence time. A problem, however, is that stochastic constraint-handling techniques can have highly unpredictable convergence times, spanning several orders of magnitude, and might sometimes not converge at all. A possible solution to avoid the lengthy runs is to restart the search after a certain time, with the hope that a new starting point will lead to a lower overall convergence time, but selecting an optimal restart-time is not trivial. In this paper, two strategies are investigated for such selection, and their performance is evaluated on two constraint-handling techniques for a product design problem. The results show that both restart strategies can greatly reduce the overall convergence time. Moreover, it is shown that one of the restart strategies can be applied to a wide range of constraint-handling techniques and problems, without requiring any fine-tuning of problem-specific parameters

Design Automation Case Study : Modular Locating Fixture

Design automation systems often mimic human designers and implement routine design activities. Beside this, the idea of such knowledge-based engineering is to support developers in the analysis and syntheses of complex engineering artifacts. An instance of this is product configuration. A central aspect of knowledge-based engineering is its ability to draw conclusions about the design context. For this inference, different reasoning techniques have been proposed. One uses constraint satisfaction problems as model-based approach. In the present contribution, the authors report about a case-study about the implementation of a constraint-based configuration system with onboard resources of a computer aided design system on the example of a locating fixture

BIM-based Generative Modular Housing Design and Implications for Post-Disaster Housing Recovery

The adverse social and financial impacts of catastrophic disasters are increasing as population centers grow. After disastrous events, the government agencies must respond to post-disaster housing issues quickly and efficiently and provide sufficient resources for the reconstruction of destroyed and damaged houses for full rehabilitation. However, post-disaster housing reconstruction is a highly complex process because of the large number of projects, shortage of resources, and heavy pressure for delivery of the projects after a disastrous event. This complexity and lack of an inconsistent, systematic approach for planning lead to an ad-hoc decision-making process and inefficient recovery. This research explored modular construction as a highly time-efficient approach to tackle the abovementioned challenges and facilitate the housing reconstruction process. Firstly, this research investigated the feasibility of using the modular construction method for rapid post-disaster housing reconstruction through a targeted literature review and survey of subject matter experts to broaden the understanding of modular construction-based post-disaster housing reconstruction, benefits, and barriers. Second, this research focused on improving the design and pre-planning phase of modular construction that can facilitate the successful implementation of modular construction in a post-disaster situation. To this end, a BIM-based generative modular housing design system was developed by using Generative Adversarial Networks (GANs) to automate the entire design process by incorporating manufacturing and construction constraints to fit the needs of the modular construction method. The framework was further extended by developing an optimization model to optimize the modularization strategy in the early design phase which was capable of reflecting the entire multi-stage process of modular construction (production, transportation, and assembly), and considering both individual project’s requirements and post-disaster housing reconstruction portfolio’s requirements. The outcomes of this study fit the MC industry that may be used by designers and modular housing companies looking to automate their design process. It is also expected to provide critical benchmarks for planners, decision-makers, and community developers to facilitate their decision-making process on considering modular construction as an efficient way for mass post-disaster housing reconstruction and addressing communities’ housing needs following a disastrous event

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage

On the selection and analysis of software product line implementation components using intelligent techniques

En los últimos años y con el creciente avance tecnológico, las empresas ya no se centran exclusivamente en diseñar un producto para un cliente (por ejemplo, el diseño de un sitio web para el Hotel Decameron), sino en producir para un dominio (por ejemplo, el diseño de sitios web para hoteles); es decir, el diseño de un producto que pueda adaptarse fácilmente a las diferentes variaciones que puedan existir para un mismo producto y que se adapte a los gustos individuales de los clientes. En la ingeniería de software, esto puede lograrse a través de la gestión de líneas de productos de software (SPL). Una SPL se define como un conjunto de sistemas que comparten un conjunto común de características que satisfacen la demanda de un mercado específico. Una SPL intenta reducir el esfuerzo y el costo de implementar y mantener en el tiempo un conjunto de productos de software similares; sin embargo, manejar la variabilidad en estos sistemas es una tarea dif´ıcil, a mayor n´umero de productos m´as complejo se hace manejarlos. Los modelos de caracter´ısticas (FMs) se emplean para representar gr´aficamente las partes comunes y variables de una SPL. Dada la gran cantidad de caracter´ısticas que se pueden derivar de un modelo de caracter´ıstica (FM), resulta dif´ıcil de gestionarlos. Para hacer frente a estos problemas se ha propuesto el An´alisis Autom´atico de Modelos de Caracter´ısticas (AAFM) que mediante el uso de herramientas asistidas por ordenador, se ocupa de la extracci ´on de información de los modelos de características. No obstante, existen ciertos escenarios en los que la configuración de un producto se convierte en una actividad compleja dado el número de componentes que existen para implementar una determinada característica. En esta tesis, exploramos técnicas inteligentes para resolver dos problemas que surgen al manejar una SPL: i. Por un lado, hemos identificado los problemas que surgen cuando un desarrollador desea mantener sus aplicaciones al d´ıa con los últimos avances tecnol´ogicos. La estrecha relaci ´on entre las caracter´ısticas de aplicaci ´on y los componentes de plataforma es dif´ıcil de rastrear. Los desarrolladores deben ser conscientes de las consecuencias que podr´ıan traer a las aplicaciones existentes cuando cambia el hardware donde se va a ejecutar; por ejemplo, cuando una aplicaci ´on se traslada de un smartphone a una computadora/tablet, o cuando una plataforma se actualiza a una nueva versi´on. Los diferentes tama˜nos y resoluciones de pantalla, la posible ausencia de un radio celular o el aumento de la cantidad de memoria pueden tener impactos positivos o negativos en una aplicaci ón. En este contexto, dado que las caracter´ısticas de aplicaci ´on y de plataforma están conceptualmente separadas, sus caracter´ısticas pueden modelarse en dos modelos distintos. Por consiguiente, manejar la trazabilidad entre estas dos capas y c´omo los posibles cambios en ciertas caracter´ısticas puedan afectar a la otra capa, es un problema que est´a por resolver. ii. Por otro lado, hemos encontrado lo complicado que es para el desarrollador de aplicaciones configurar un producto cuando hay una variedad de componentes de implementación para cada característica. Por ejemplo, un desarrollador web en WordPress busca manualmente aquellos componentes (plugins) que son factibles y más óptimos para cada sitio web. Esta tarea lleva tiempo y no siempre garantiza que los componentes seleccionados sean los m´as adecuados (en términos de calidad) para la aplicación requerida. Dos escenarios podrían surgir durante esta configuraci´on: primero, la selecci ´on emp´ırica de un componente, en la pr´actica, puede no proporcionar los resultados esperados; adem´as, no tener criterios basados en la experiencia de otros usuarios con respecto a estos componentes, podr´ıa inducir una mala selecci ´on y lograr una mala experiencia para el usuario final. En este contexto, el manejo de la relaci ´on entre los componentes de implementaci´on y sus caracter´ısticas es otro problema a resolver. Concretamente, las contribuciones de esta tesis se detallan a continuaci´on; Modelos de caracter´ısticas en m´ ultiples capas: En esta ´area introducimos un framework para el an´alisis de modelos de caracter´ısticas de m´ ultiples capas, llamado MAYA. Los objetivos que perseguimos con esta soluci´on son: i) modelar la variabilidad de los sistemas software en dos capas, incluyendo sus respectivas interdependencias; ii) definir un conjunto de operaciones que puedan imponerse a dichos modelos; iii) una implementaci ´on de referencia para el an´alisis de m´ ultiples capas basado en un caso de estudio en Android, y finalmente; iv) dos evaluaciones emp´ıricas que demuestran la viabilidad de nuestra propuesta en la pr´actica. Componentes de implementaci´on: La configuraci´on de un producto es una de las actividades m´as propensas a errores, m´as a ´un cuando para cada caracter´ıstica hay m´as de un componente que la implemente. Para gestionar estas configuraciones, introducimos un sistema de recomendaci ´on basado en componentes llamado RESDEC que facilita la selecci ´on de componentes de implementaci´on al crear productos en una SPL. Concretamente las contribuciones que se presentan con esta propuesta son: i) modelado del problema de selecci ´on de componentes de implementaci ´on como una tarea de recomendaci´on utilizando algoritmos de filtrado colaborativo y por contenido; ii) dise ˜no de un prototipo de herramienta de sistema de recomendaci´on basada en componentes lista para ser utilizada y extendida a otros entornos a partir de la selecci ´on de componentes de implementaci´on y, finalmente; iii) una evaluaci´on emp´ırica basado en sitios web de comercio electr ´onico enWordPress

Reconfiguring process plans: A mathematical programming approach

Increased global competition and frequent unpredictable market changes are current challenges facing manufacturing enterprises. Unpredictable changes of part design and engineering specifications trigger frequent and costly changes in process plans, which often require changes in the functionality and design of the manufacturing system. Process planning is a key logical enabler that should be further developed to cope with the changes encountered at the system level as well as to support the new manufacturing paradigms and continuously evolving products. Retrieval-based process planning predicated on rigid pre-defined boundaries of part families, does not satisfactorily support this changeable manufacturing environment. Since purely generative process planning systems are not yet a reality, a sequential hybrid approach at the macro-level has been proposed. Initially the master plan information of the part family\u27s composite part is retrieved, then modeling tools and algorithms are applied to arrive at the process plan of the new part, the definition of which does not necessarily lie entirely within the boundary of its original part family. Two distinct generative methods, namely Reconfigurable Process Planning (RPP) and Process Re-Planning were developed and compared. For RPP, a genuine reconfiguration of process plans to optimize the scope, extent and cost of reconfiguration is achieved using a novel 0-1 integer-programming model. Mathematical programming and formulation is proposed, for the first time, to reconfigure process plans to account for changes in parts\u27 features beyond the scope of the original product family. The computational time complexity of RPP is advantageously polynomial compared with the exponentially growing time complexity of its classical counterparts. As for Process Re-Planning, a novel adaptation of the Quadratic Assignment Problem (QAP) formulation has been developed, where machining features are assigned positions in one-dimensional space. A linearization of the quadratic model was performed. The proposed model cures the conceptual flaws in the classical Traveling Salesperson Problem; it also overcomes the complexity of the sub-tour elimination constraints and, for the first time, mathematically formulates the precedence constraints, which are a comer stone of the process planning problem. The developed methods, their limitations and merits are conceptually and computationally, analyzed, compared and validated using detailed industrial case studies. A reconfiguration metric on the part design level is suggested to capture the logical extent and implications of design changes on the product level; equally, on the process planning level a new criterion is introduced to evaluate and quantify impact of process plans reconfiguration on downstream shop floor activities. GAMS algebraic modeling language, its SBB mixed integer nonlinear programming solver, CPLEX solvers and Matlab are used. The presented innovative new concepts and novel formulations represent significant contributions to knowledge in the field of process planning. Their effectiveness and applicability were validated in different domains