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This paper aims to propose a conceptualization of the supply chain resilience domain using conceptual modelling techniques formalized through a metamodel and illustrated through an example.

This research uses conceptual modelling techniques to build and modularize the metamodel, the latter to manage complexity. The metamodel was built iteratively and subsequently instantiated with an example of a yogurt factory to analyse its usefulness and theoretical relevance, and thus its contributions to the domain.

Conceptual modelling techniques can represent a complex domain such as supply chain resilience simply, and the proposed metamodel makes it possible to create models that become valuable decision support tools.

Consolidation and structuring of concepts in the supply chain resilience domain through conceptual modelling techniques.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

This paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization.

The method has fully utilized the information provided by different metamodels in the optimization process. It not only imparts the expected improvement criterion of kriging into other metamodels but also intelligently selects appropriate metamodeling techniques to guide the search direction, thus making the search process very efficient. Besides, the corresponding local search strategies are also put forward to further improve the optimizing efficiency.

To validate the method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the global optimization efficiency of the proposed method is higher than that of the other methods for most situations.

The proposed method sufficiently utilizes multiple metamodels in the optimizing process. Thus, good optimizing results are obtained, showing great applicability in engineering design optimization problems which involve costly simulations.

The paper offers an intelligent approach to analyze and determine the design parameters minimizing the total cost and achieving the desired performance measures in the maintenance float systems. The expected total cost in a maintenance float system includes the cost of lost production, the cost of repair persons and the cost of standby machines. The developed design procedure integrates simulation, metamodel and genetic algorithms. Neural networks are able to approximate functions based on a set of sample data, i.e. construct metamodels from simulation results in this study. The objective of metamodels is to predict simulation responses in order to significantly reduce the amount of simulation runs. The predictive performance of neural metamodels comparably outperforms the traditional regression metamodels. The neural metamodels are further extended to formulate a decision model for optimizing the maintenance float systems by using genetic algorithms.

The definition of modeling languages is a key‐prerequisite for model‐driven engineering. In this respect, Domain‐Specific Modeling Languages (DSMLs) defined from scratch in terms of metamodels and the extension of Unified Modeling Language (UML) by profiles are the proposed options. For interoperability reasons, however, the need arises to bridge modeling languages originally defined as DSMLs to UML. Therefore, the paper aims to propose a semi‐automatic approach for bridging DSMLs and UML by employing model‐driven techniques.

The paper discusses problems of the ad hoc integration of DSMLs and UML and from this discussion a systematic and semi‐automatic integration approach consisting of two phases is derived. In the first phase, the correspondences between the modeling concepts of the DSML and UML are defined manually. In the second phase, these correspondences are used for automatically producing UML profiles to represent the domain‐specific modeling concepts in UML and model transformations for transforming DSML models to UML models and vice versa. The paper presents the ideas within a case study for bridging ComputerAssociate's DSML of the AllFusion Gen CASE tool with IBM's Rational Software Modeler for UML.

The ad hoc definition of UML profiles and model transformations for achieving interoperability is typically a tedious and error‐prone task. By employing a semi‐automatic approach one gains several advantages. First, the integrator only has to deal with the correspondences between the DSML and UML on a conceptual level. Second, all repetitive integration tasks are automated by using model transformations. Third, well‐defined guidelines support the systematic and comprehensible integration.

The paper focuses on the integrating direction DSMLs to UML, but not on how to derive a DSML defined in terms of a metamodel from a UML profile.

Although, DSMLs defined as metamodels and UML profiles are frequently applied in practice, only few attempts have been made to provide interoperability between these two worlds. The contribution of this paper is to integrate the so far competing worlds of DSMLs and UML by proposing a semi‐automatic approach, which allows exchanging models between these two worlds without loss of information.

This paper aims to present a new approach for developing a framework based on a model driven architecture (MDA) for the modelling of technology‐independent collaborative processes.

The paper suggests a new collaborative process modelling approach based on an MDA and a metamodelling technique. The research method, based on the design science approach, was started by identifying the characteristics of the collaborative processes, which distinguish them from the classical intraorganizational ones. Then, the generic collaborative business process (CBP) modelling framework is developed based on MDA approach and definition of a set of transformation rules through three layers: business, process, and technical. After that, the core component of the framework was the proposition of a generic CBP metamodel at PIM/MDA level. The specific collaboration participant's business processes (expressed as BPMN model) are generated from the generic CBP model represented as an UML2 Profile activity diagram, which is compliant to CBP metamodel. Finally, as proof‐of‐concept, the architecture of an Eclipse‐based open development platform is developed implementing an e‐Procurement collaborative process.

The proposed framework for CBP modelling and the generic CBP metamodel contribute towards a more efficient methodology and have consequences for BPM‐related collaboration, facilitating the B2B processes modelling and implementation. In order to demonstrate and evaluate the practical applicability of the framework, the architecture of an Eclipse‐based open development platform is developed implementing a collaborative business application on the basis of an e‐Procurement use case.

There is a need to focus future research efforts on the improvement of the semi‐automatic transformation phase from public to private processes which needs human intervention by adding a suitable interfaces at both sides of the B2B interaction. In addition, the problem of semantic heterogeneities regarding the partner's business process elements (business documents, activity/task names) should be tackled by developing an approach that uses ontology.

Business processes developers find a B2B technology‐independent solution for implementing and using interorganizational information systems.

The paper provides a framework that enables the CBP modelling and integrates a generic CBP metamodel. Currently, to the best of the authors' knowledge, such a generic metamodel and his instantiation have not so far been developed.

The modelling landscape experiences a rich proliferation of modelling language, or metamodel. The emergence of cross-disciplinary disciplines, such as enterprise engineering and service engineering, necessitates a multi-perspective approach to traverse the component from strategic level to technological aspect. This paper aims to find a unifying structure of metamodels introduced by academics and industries.

A grounded approach is taken to define the structure by collating the metamodels to form an emerging structure. Metamodels were collected from a literature survey from several interrelated disciplines: software engineering, system engineering, enterprise architecture, service engineering, business process management and financial accounting.

The result suggests seven stereotypes of metamodel, characterized by its label: goal, enterprise, business model, service, process, software and system. The aspect of “process” holds a central role in connecting all other aspect in the modelling continuum. Service engineering can be viewed as an alternative abstraction of enterprise engineering in containing the concepts of “business model”, “capability”, “value”, “interaction”, “process” and “software”.

Metamodel collection was performed to emphasize on representativeness rather than comprehensiveness, in which old and unpopular metamodel were disregarded unless it offer unique characteristic not yet represented in the collection. Owing to its bottom-up approach, the paper is not intended to identify a gap in metamodel offering.

This paper produces a structure of metamodel landscape in a graphical format to illustrate correlation between metamodels in which evolutive patterns of metamodel proliferation can be observed. The produced structure can serve as map in metamodel continuum.

Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.

In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

The purpose of this paper is to ease the ISO 31000 standard understanding and provide mechanisms that allow organizations to adopt and adapt this standard to their reality.

The research methodology adopted in this research was the design science research methodology.

Key finding is that enterprise architecture (EA) models and EA tools can help reduce the complexity of the ISO 31000 standard and improve the communication between stakeholders.

The research proposal serves the purpose of supporting the evidence collection for an enterprise risk management (ERM) initiative in an as-was, as-is, or to-be perspective.

Traditional ERM efforts operate on silos, limiting the sharing of risk information and the achievement of an organization-wide view of risks. EA can provide a common way to model complex business systems, from the strategic level to implementation details. This paper proposes the use of an EA model and an EA tool (Atlas) to represent ISO 31000, allowing a better understanding on the value of assets that can be affected from the manifestation of some risks over time.

Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive nature CFD models may not be easily and quickly deployable. In this regard, this study aims to present a support vector machine (SVM)-based metamodelling approach that can be easily trained and quickly deployed for carrying out large-scale studies.

Radial basis function and ε^*-insensitive loss function are used as kernel function and loss function, respectively. To prevent overfitting of the model, five-fold cross-validation root mean squared error is used while training the SVM metamodel. Rather than blindly using any SVM tuning parameters, a particle swarm optimisation (PSO) is used to fine-tune them. The developed SVM metamodel is tested using various error metrics on disjoint test data.

Using the SVM metamodel, a parametric study is conducted to understand the effect of various factors influencing the behaviour of the turbulent fluid flow in the pipe bend with CFD simulation data set. Based on the parametric study carried out, it is seen that the diametric position has the most effect on dimensionless axial velocity, whereas Reynolds number has the least effect.

This paper provides an effective PSO-tuned SVM metamodelling approach, which may be used as a significant cost-saving approach to quickly and accurately estimate fluid flow characteristics that, in general, require the use of expensive CFD models.