An active styling platform for designing and developing product families

In recent years, fierce market competition has increased the need for incorporating manufacturing flexibility into product styling. The product styling platform approach proposed in this paper offers an insight into how this challenge can be dealt with by transforming an existing product fashion into an intangible styling module with the view to refreshing its appeal. This paper addresses the important issues of how the approach can be turned into a robust applicational tool to facilitate the design of product families to satisfy diverse customer needs. The paper investigates the nature and quantification of modular styling. By capturing and analysing design experts’ opinions on product styling, the evolution of a fashion can be quantified in the form of a schema of diffusion styles. The study has also led to the development of the ideas of “Prompt Time Market Segmentation Grid” and the “Active Style Diagnosis”. The concepts are applied in an “Active Product Styling Platform” with scalable and expandable capabilities to enhance the effectiveness of conventional product styling practices. This approach can also help the formation of product style families and facilitate the development of products particularly in a mass customisation environment.     

An improved critical constraint method for structural optimization of product families

This paper discusses important improvements in the efficient Critical Constraint Method (CCM) for the optimization of structural product families subjected to multiple crash load cases. The method was first presented by Öman and Nilsson (Struct Multidisc Optim 41(5):797–815, 2010). However, the algorithm often converged towards an infeasible solution, which considerably limited the applicability of the method. Therefore, improvements are presented here to make the method more robust regarding feasible solutions, resulting in only a minor decrease in efficiency compared to the original method. The improvements include; a penalty approach to control the feasibility of the method by continuously pushing the solution out of the infeasible region, a dynamic contraction algorithm to increase the accuracy and robustness of the method by considering the optimization progress and variable history in the reduction of the step size, and the implementation of a parallel approach to further increase the efficiency of the method by enabling the full potential of large-scale computer clusters. Finally, the potential of the improved CCM algorithm is demonstrated on a large-scale industrial family optimization problem and it is concluded that the high efficiency of the method enables the usage of large product family optimization in the design process.     

Variability assessment in software product families

Software variability management is a key factor in the success of software systems and software product families. An important aspect of software variability management is the evolution of variability in response to changing markets, business needs, and advances in technology. To be able to determine whether, when, and how variability should evolve, we have developed the COVAMOF software variability assessment method (COSVAM). The contribution of COSVAM is that it is a novel, and industry-strength assessment process that addresses the issues that are associated to the current variability assessment practice. In this paper, we present the successful validation of COSVAM in an industrial software product family.     

An algebra of hybrid systems

Hybrid systems are heterogeneous systems characterised by the interaction of discrete and continuous dynamics. We present a trajectory-based algebraic model for describing hybrid systems; the trajectories used are closely related to streams. The algebra is based on left quantales and left semirings and provides a new application for these algebraic structures. We show that hybrid automata, which are probably the standard tool for describing hybrid systems, can conveniently be embedded into our algebra. Moreover we point out some important advantages of the algebraic approach. In particular, we show how to handle Zeno effects, which are excluded by most other authors. The development of the theory is illustrated by a running example and a larger case study.     

An algebra of mixed computation

Algebraic tools for mixed computation are presented. Some axioms for informational objects, program functions, inputs and outputs are introduced. These axioms are sufficient for the correct mixed computation of some basic program composition forms.     

An algebra of temporal faults

Faults modelling is essential to anticipate failures in critical systems. Traditionally, Static Fault Trees are employed to this end, but Temporal and Dynamic Fault Trees are gaining evidence due to their enriched power to model and detect intricate propagation of faults that lead to a failure. In previous work, we showed a strategy based on the process algebra CSP and Simulink models to obtain fault traces that lead to a failure. Although that work used Static Fault Trees, it could be used with Temporal or Dynamic Fault Trees. In the present work we define an algebra of temporal faults (with a notion of fault propagation) and prove that it is indeed a Boolean algebra. This allows us to inherit Boolean algebra’s properties, laws and existing reduction techniques, which are very beneficial for faults modelling and analysis. We illustrate our work on a simple but real case study supplied by our industrial partner EMBRAER.     

A methodology to redesign heterogeneous product portfolios as homogeneous product families

This paper presents a methodology to redesign heterogeneous product portfolios as homogeneous product families. A heterogeneous portfolio is one that has no common components, or shares a minimum number of components. The methodology proposed to homogenize the portfolio begins by analyzing the current product offerings to determine customer needs and functions. Then, a granulation process is initiated to remove redundancies, and identify physical function carriers to deliver the required functions. Next, function carriers are grouped on the basis of their functional commonalities into groups for parallel development. The applicability and the use of the methodology are illustrated through the redesign of an existing heterogeneous ‘office furniture’ product portfolio.     

Configuring use case models in product families

In many domains such as automotive and avionics, the size and complexity of software systems is quickly increasing. At the same time, many stakeholders tend to be involved in the development of such systems, which typically must also be configured for multiple customers with varying needs. Product Line Engineering (PLE) is therefore an inevitable practice for such systems. Furthermore, because in many areas requirements must be explicit and traceability to them is required by standards, use cases and domain models are common practice for requirements elicitation and analysis. In this paper, based on the above observations, we aim at supporting PLE in the context of use case-centric development. Therefore, we propose, apply, and assess a use case-driven configuration approach which interactively receives configuration decisions from the analysts to generate product-specific (PS) use case and domain models. Our approach provides the following: (1) a use case-centric product line modeling method (PUM), (2) automated, interactive configuration support based on PUM, and (3) an automatic generation of PS use case and domain models from Product Line (PL) models and configuration decisions. The approach is supported by a tool relying on Natural Language Processing (NLP) and integrated with an industrial requirements management tool, i.e., IBM DOORS. We successfully applied and evaluated our approach to an industrial case study in the automotive domain, thus showing evidence that the approach is practical and beneficial to capture variability at the appropriate level of granularity and to configure PS use case and domain models in industrial settings.     

An algebra for commitment protocols

Protocols enable unambiguous, smooth interactions among agents. Commitments among agents are a powerful means of developing protocols. Commitments enable flexible execution of protocols and help agents reason about protocols and plan their actions accordingly, while at the same time providing a basis for compliance checking. Multiagent systems based on commitments can conveniently and effectively model business interactions because the autonomy and heterogeneity of agents mirrors real-world businesses. Such modeling, however, requires multiagent systems to host a rich variety of protocols that can capture the needs of different applications. We show how a commitment-based semantics provides a basis for refining and aggregating protocols. We propose an approach for designing commitment protocols wherein traditional software engineering notions such as refinement and aggregation are extended to apply to protocols. We present an algebra of protocols that can be used to compose protocols by refining and merging existing ones, and does this at a level of abstraction high enough to be useful for real-world applications. We thank Amit Chopra, Nirmit Desai, and anonymous referees for valuable comments. This research was supported partially by the NSF under grant DST-0139037, and partially by DARPA under contract F30603-00-C-0178.     

An algebra for probabilistic databases

An algebra is presented for a simple probabilistic data model that may be regarded as an extension of the standard relational model. The probabilistic algebra is developed in such a way that (restricted to α-acyclic database schemes) the relational algebra is a homomorphic image of it. Strictly probabilistic results are emphasized. Variations on the basic probabilistic data model are discussed. The algebra is used to explicate a commonly used statistical smoothing procedure and is shown to be potentially very useful for decision support with uncertain information     

An algebra for process creation

In this paper, we study the issue of process creation from an algebraic perspective. The key to our approach, which is inspired by work of America and De Bakker, consists of giving a new interpretation to the operator symbol · (sequential composition) in the axiom system BPA of Bergstra and Klop. We present a number of other models for BPA and show how the new interpretation of · naturally generalises the usual interpretation in ACP. We give an operational semantics based on Plotkin style inductive rules for a simple language with process creation and communication, and give a complete finite axiomatisation of the associated bisimulation model. Note: Partial support received from the European Communities under ESPRIT contract 432, An Integrated Formal Approach to Industrial Software Development (METEOR), and RACE contract 1046, Specification and Programming Environment for Communication Software (SPECS).     

Image algebra: An overview

This paper is the first in a sequence of papers describing an algebraic structure for image processing that has become known as the AFATL Standard Image Algebra. This algebra provides a common mathematical environment for image processing algorithm development and methodologies for algorithm optimization, comparison, and performance evaluation. In addition, the image algebra provides a powerful algebraic language for image processing which, if properly embedded into a high level programming language, will greatly increase a programmer's productivity as programming tasks are greatly simplified due to replacement of large blocks of code by short algebraic statements. The purpose of this paper is to familiarize the reader with the basic concepts of the algebra and to provide a general overview of its methodology.     

Using patterns to model variability in product families

A product family can generate significant savings in cost and time by permitting software component reuse. The authors have developed a simple method of product family engineering using patterns to model variability and tested it on the European Space Operations Centre new mission planning software system with positive results     

An extended algebra for constraint databases

Constraint relational databases use constraints to both model and query data. A constraint relation contains a finite set of generalized tuples. Each generalized tuple is represented by a conjunction of constraints on a given logical theory and, depending on the logical theory and the specific conjunction of constraints, it may possibly represent an infinite set of relational tuples. For their characteristics, constraint databases are well suited to model multidimensional and structured data, like spatial and temporal data. The definition of an algebra for constraint relational databases is important in order to make constraint databases a practical technology. We extend the previously defined constraint algebra (called generalized relational algebra). First, we show that the relational model is not the only possible semantic reference model for constraint relational databases and we show how constraint relations can be interpreted under the nested relational model. Then, we introduce two distinct classes of constraint algebras, one based on the relational algebra, and one based on the nested relational algebra, and we present an algebra of the latter type. The algebra is proved equivalent to the generalized relational algebra when input relations are modified by introducing generalized tuple identifiers. However, from a user point of view, it is more suitable. Thus, the difference existing between such algebras is similar to the difference existing between the relational algebra and the nested relational algebra, dealing with only one level of nesting. We also show how external functions can be added to the proposed algebra     

Classifying product families using platform coverage and variation mechanisms

The notion of product families is becoming more and more popular, both in research and in industry. Every product family initiative that is started within a company has its own context, such as a particular business strategy and a particular application domain. Each product family has its own specific characteristics that have to fit in with its context. In this paper we will describe two dimensions for classifying product families. The first dimension deals with the coverage of the product family platform. Platform coverage deals with the proportion of the functionality provided by the platform, and the additional functionality needed to derive a specific product within the product family. The second dimension deals with the variation mechanisms that are used to derive a specific product from the generic platform. The coverage of the platform and the variation mechanisms used are not totally unrelated. We will discuss various types of platform coverage and variation mechanisms, including their characteristics. These two dimensions are based on experience gained with a number of product families. We will look at four of these in greater detail to illustrate our ideas. We believe that these dimensions will aid the classification of product families. This will both facilitate the selection of a new product family approach for a particular context, and support the evaluation of existing product families. Copyright © 2005 John Wiley & Sons, Ltd.