Facilitating the evolution of products in product line engineering by capturing and replaying configuration decisions

Software product lines rely on developing reusable artifacts and defining their variability in models to support and accelerate the derivation of individual products. A major challenge in product lines is the continuous evolution of both the reusable artifacts and derived products. Products that have been derived from a product line have to be updated regularly, e.g., after bugfixes or the development of new features. Changes to reusable artifacts and variability models have to be propagated to derived products. The aim of our research is to provide automated support for the evolution of products derived from product lines by capturing and replaying configuration decisions. Our PUPLE (Product Updates in Product Line Engineering) approach supports updating derived products after changes to the product line they have been derived from. It exploits the structure of variability models and uses change-tracking data to minimize user intervention. The paper first explores how different types of product line changes influence the derived products. It then presents extensions to our decision-oriented product line approach DOPLER to support product line evolution. We evaluate the feasibility of the PUPLE approach with evolution tasks that were performed by engineers of an industry partner on a product line of an Eclipse-based tool suite with six derived products. We conclude with lessons learned and limitations of our approach.     

Joint optimization of product tolerance design,process plan,and production plan in high-precision multi-product assembly

With the ever-increasing product variety faced by the manufacturing industry, investment efficiency can only be maintained by the application of multi-product assembly systems. In such systems, the product design, process planning, and production planning problems related to different products are strongly interconnected. Despite this, those interdependent decisions are typically made by different divisions of the company, by adopting a decomposed planning approach, which can easily result in excess production costs. In order to overcome this challenge, this paper proposes an integrated approach to solving the above problems, focusing on the decisions crucial for achieving the required tolerances in high-precision assembled products. The joint optimization problems related to product tolerance design and assembly resource configuration are first formulated as a mixed-integer linear program (MILP). Then, a large neighborhood search (LNS) algorithm, which combines classical mathematical programming and meta-heuristic techniques, is introduced to solve large instances of the problem. The efficiency of the method is demonstrated through an industrial case study, both in terms of computational efficiency and industrial effectiveness.     

A knowledge support approach for the preliminary design of platform-based products in Engineering-To-Order manufacturing

A product platform is a design approach for meeting the demand for customizable products. Traditional knowledge-based technologies or systems lack flexibility in supporting both configuration and parameter design of platform-based products. In many cases, customers’ requirements and knowledge models both contain incomplete information, and there are complex relations among various solutions, functions and solution parameters in Engineering-To-Order (ETO) products. A knowledge model for the preliminary design of ETO products is presented in this paper, and linkages are established between configuration design knowledge and parameter design procedures. The basis of the knowledge model is the Extended Function-Solutions (EFS) tree, from which design case trees, design modules, constraint checking rules, and module interface templates derive. A corresponding knowledge retrieval and reuse strategy is also presented. It uses an improved fuzzy information axiom to search for the optimal configuration with incomplete information. The parameter design process model of new products then can be generated based on the optimal configuration. The case study demonstrates the knowledge modeling, retrieval and reuse for the preliminary design of open-type crank presses. Moreover, the effectiveness of the methodology is discussed by analyzing the verification approach and the satisfaction of customers’ requirements.     

A minimal-invasive approach to configuration management supporting collaborative factory configuration scenarios

Collaboration is not only an issue for geographically or temporally dispersed engineering teams jointly working on the development of products or manufacturing facilities. It is also an important issue for the operation of production lines. One task that requires close collaboration is the setup of the configuration data for the production line—the manufacturing specification data. They lay the foundations for automation scenarios on all levels of the automation hierarchy, starting from the single production equipment up to the complete production line. Even complex business rules and logistics strategies are supported by corresponding configuration data. Typically, these data are distributed over a large number of IT systems. Groups of people from different departments account for the correctness and completeness of these data. A complete, correct and consistent set of manufacturing specification data is an important precondition for production lines to run in the optimal working point. This is especially true in highly automated environments that provide a high level of flexibility. Within this paper a new, minimal-invasive approach to configuration management is presented that helps to overcome the collaboration issues.     

A dynamic constraint satisfaction approach for configuring structural products under mass customization

Configuring structured products poses new challenges to the solving technologies for product configuration. This paper presents a novel and direct approach to encoding configuration models into the Dynamic Constraint Satisfaction Problems (DCSP). In the presented approach, components are encoded as DCSP variables while structural relationships are represented as DCSP activity constraints. Furthermore, the configuration constraints such as the requisition and exclusion constraints are treated as DCSP compatibility constraints, which allow a low-level component to join in the solving process only after its high-level component is selected in the configuration. The presented method allows a more compact encoding representation, compared to CSP and generative CSP. Experimental study shows that the presented DCSP encoding approach makes a significant improvement in the performance of product configuration.     

Applying fuzzy multiple attributes decision making for product configuration

Mass customization (MC) is an emergent concept in industry intended to provide customized products through flexible processes in high volumes and at reasonably low costs. The method of configuration is one of important ways to realize quickly product customization. But, in business, particularly through the Internet, a customer normally develops in his mind some sort of ambiguity, given the choice of similar alternative products. This paper proposes a new approach to product configuration by applying the theory of fuzzy multiple attribute decision making (FMADM), which focus on uncertain and fuzzy requirements the customer submits to the product supplier. The proposed method can be used either in the product data management system or e-commerce websites, with which it is easy for customers to get his preferred product according to the utility value with respect to all attributes. Finally, the digital camera is taken as an example to further verify the validity and the feasibility of the proposed method.     

Model-based incremental conformance checking to enable interactive product configuration

ContextModel-based product line engineering (PLE) is a paradigm that can enable automated product configuration of large-scale software systems, in which models are used as an abstract specification of commonalities and variabilities of products of a product line.ObjectiveIn the context of PLE, providing immediate feedback on the correctness of a manual configuration step to users has a practical impact on whether a configuration process with tool support can be successfully adopted in practice.MethodIn an existing work, a UML-based variability modeling methodology named as SimPL and an interactive configuration process was proposed. Based on the existing work, we propose an automated, incremental and efficient conformance checking approach to ensure that the manual configuration of a variation point conforms to a set of pre-defined conformance rules specified in the Object Constraint Language (OCL). The proposed approach, named as Zen-CC, has been implemented as an integrated part of our product configuration and derivation tool: Zen-Configurator.ResultsThe performance and scalability of Zen-CC have been evaluated with a real-world case study. Results show that Zen-CC significantly outperformed two baseline engines in terms of performance. Besides, the performance of Zen-CC remains stable during the configuration of all the 10 products of the product line and its efficiency also remains un-impacted even with the growing product complexity, which is not the case for both of the baseline engines.ConclusionThe results suggest that Zen-CC performs practically well and is much more scalable than the two baseline engines and is scalable for configuring products with a larger number of variation points.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

Information technology and structural change in the paper and pulp industry : Some technological, organizational and managerial implications

The paper and pulp industry has experienced a structural change during the last 15 years. The products have differentiated and completely new paper types have been introduced. The standard and classical products, like news print paper, are often specialized and customized versions from the standard product in order to fulfill the specific needs of different printing processes better. These changes have impacted on the production organization and structures of enterprises. The key companies have acquired more production capacity in different countries and have become really international companies. These trends are likely to continue. One of the technological driving forces has been the applying of information technology. The digital process automation systems as well as the mill wide information systems have been strategic tools and technological corner stones in the structural change. However, it is only technology which matters. Also new organisational forms as well as new implementation methods of systems are needed.     

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

ContextIn industrial settings products are developed by more than one organization. Software vendors and suppliers commonly typically maintain their own product lines, which contribute to a larger (multi) product line or software ecosystem. It is unrealistic to assume that the participating organizations will agree on using a specific variability modeling technique—they will rather use different approaches and tools to manage the variability of their systems.ObjectiveWe aim to support product configuration in software ecosystems based on several variability models with different semantics that have been created using different notations.MethodWe present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototype implementation based on web services.ResultsWe show the feasibility of the approach and its implementation by using it with the three most widespread types of variability modeling approaches in the product line community, i.e., feature-based, OVM-style, and decision-oriented modeling. To demonstrate the feasibility and flexibility of our approach, we present an example derived from industrial experience in enterprise resource planning. We further applied the approach to support the configuration of privacy settings in the Android ecosystem based on multiple variability models. We also evaluated the performance of different model enactment strategies used in our approach.ConclusionsTools and techniques allowing stakeholders to handle variability in a uniform manner can considerably foster the initiation and growth of software ecosystems from the perspective of software reuse and configuration.     

Architectural evolution of FamiWare using cardinality-based feature models

ContextAmbient Intelligence systems domain is an outstanding example of modern systems that are in permanent evolution, as new devices, technologies or facilities are continuously appearing. This means it would be desirable to have a mechanism that helps with the propagation of evolution changes in deployed systems.ObjectiveWe present a software product line engineering process to manage the evolution of FamiWare, a family of middleware for ambient intelligence environments. This process drives the evolution of FamiWare middleware configurations using cardinality-based feature models, which are especially well suited to express the structural variability of ambient intelligence systems.MethodFamiWare uses cardinality-based feature models and clonable features to model the structural variability present in ambient intelligence systems, composed of a large variety of heterogeneous devices. Since the management evolution of configurations with clonable features is manually untreatable due to the high number of features, our process automates it and propagates changes made at feature level to the architectural components of the FamiWare middleware. This is a model driven development process as the evolution management, the propagation of evolution changes and the code generation are performed using some kind of model mappings and transformations. Concretely we present a variability modelling language to map the selection of features to the corresponding FamiWare middleware architectural components.ResultsOur process is able to manage the evolution of cardinality-based feature models with thousands of features, something which is not possible to tackle manually. Thanks to the use of the variability language and the automatic code generation it is possible to propagate and maintain a correspondence between the FamiWare architectural model and the code. The process is then able to calculate the architectural differences between the evolved configuration and the previous one. Checking these differences, our process helps to calculate the effort needed to perform the evolution changes in the customized products. To perform those tasks we have defined two operators, one to calculate the differences between two feature model configurations and another to create a new configuration from a previous one.ConclusionOur process automatically propagates the evolution changes of the middleware family into the existing configurations where the middleware is already deployed and also helps us to calculate the effort in performing the changes in every configuration. Finally, we validated our approach, demonstrating the functioning of the defined operators and showing that by using our tool we can generate evolved configurations for FamiWare with thousands of cloned features, for several case studies.     

Web-based environment for mechanism simulation integrated with CAD system

The present work describes the architecture of a new approach addressing the sharing of virtual prototypes over the Web with standard programming tools. The usage of standard Web-based technologies leads to easier, effective and more general applications suitable for small and medium size companies with limited resources in that field of research. Designers not only exchange just geometry data, but also more information about design knowledge and design solutions, and collaboration is performed on remote locations. In this work the framework is proposed to model a mechanical product focusing on behavioural criteria. In order to avoid time-consuming virtual model preparation, product variety is defined in single configuration file. The configuration file is covering all the variations, with formalisation of the assembly structure and linking the parts and attributes in uniform way. It enables visualisation of the models from different modelling systems and allows interactive changes and evaluation of the virtual prototype.     

An integrated model for supplier selection decisions in configuration changes

Configuration change management provides a way for a manufacturer to become more competitive. Because of the short life and the large variety involved in commercial products, they must be configured accordingly. It is a task for the configuration change management. This paper presents an integrated model for modeling the change behavior of product parts, and for evaluating alternative suppliers for each part by applying fuzzy theory, T transformation technology, and genetic algorithms. The proposed model is based on the concepts of part change requirements, fuzzy performance indicators, and the integration of different attributes, to allow the part supplier selection of a specific commercial product to be explored and modeled. The application of this approach is illustrated through a case study of a TFT-LCD product for part change optimization. In terms of change performance, experimental analyses with different genetic parameters allowed the potential alternative suppliers for the product parts to be evaluated. The results of the experimental analyses show that this proposed methodology is a suitable approach and provides a quality solution for products with a complex configuration. In addition, the numerical results obtained from the new approach were compared with the results obtained by linear programming. The result shows that the proposed algorithm is reliable and robust.     

A systematic approach for product families formation in Reconfigurable Manufacturing Systems

The aim of this work is to establish a methodology for an effective working of Reconfigurable Manufacturing Systems (RMSs). These systems are the next step in manufacturing, allowing the production of any quantity of highly customised and complex products together with the benefits of mass production. In RMSs, products are grouped into families, each of which requires a system configuration. The system is configured to produce the first family of products. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. Therefore, the effectiveness of a RMS depends on the formation of the best set of product families. Therefore, a methodology for grouping products into families, which takes into account the requirements of products in RMSs, is an issue of core importance. These requirements are modularity, commonality, compatibility, reusability, and product demand. The methodology starts by calculating, for each product requirement, a matrix that summarises the similarity between pairs of products. Then, through the use of the AHP methodology, a unique matrix that comprises the similarity values between products is obtained. The Average Linkage Clustering algorithm is applied to this matrix in order to obtain a dendogram that shows the diverse sets of product families that may be formed.     

支持大批量定制的产品配置设计系统的研究

针对大批量定制生产的特点，提出基于产品功能结构单元的产品配置模板来完成对客户配置需求的快速响应，给出了产品配置模板的信息模型和配置求解策略．由于机械产品结构的复杂性，引入配置设计知识的混合表达方法以适应不同产品设计类型的要求．最后给出基于产品配置模板信息模型的配置设计系统的实例．     

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.     

Customized purchase supporting expert system: UNIK-SES

An expert system that assists the customized men's wear purchasing process is developed. In the past, configuration systems have adopted either a rule- or a constraint-based representation. To overcome the limitation of the representation, an integrated representation of the Constraint and Rule Satisfaction Problems (CRSP) is adopted. Since the general purpose tool UNIK-CRSP provides concurrent, integrated and interactive reasoning, UNIK-CRSP is very suitable for assisting the personalized customer product configuration process in a natural manner. Using UNIK-CRSP, the domain-specific tool UNIK-SES is developed with the additional features of candidate products selection and user interfaces. This approach should be useful for customer support in electronic marketing on the information superhighway.     

产品质量和WTP差异下的制造/再制造生产决策

在需求与价格、质量相关的假设下, 提出再制造产品的3 种生产模式, 给出最优生产决策. 研究结果表明, 随着再制造产品支付意愿(WTP) 的提高, 第2 阶段新产品产量都将下降, 再制造产品和第1 阶段新产品产量在Model O和Model OI 模式下上升, 在Model I 模式下不变; 产品质量的上升会使所有产品的产量都下降, 导致原设备生产商(OEM) 的收益下降, 而独立的再制造商(IR) 的收益在一定范围内上升, 但其获利所需最低WTP值也将上升. 数值仿真验证了各参数对生产决策及利润的影响.

     

Integrated Vehicle Configuration System—Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.