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This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

The purpose of this paper is to propose an integrated approach to process integration, automation, and optimization through enhanced business process models.

The approach is based on a framework of process integration for functional applications, automation for business workflows, and additional functionalities for process optimization. The proposed approach is illustrated using enhanced process models over business integration, automation, and optimization with data elements, structures, and organizational elements. The standard sales order process cycle, quotation approval process, and production order cycle are chosen for illustrating process integration, automation, and optimization, respectively.

The proposed approach combines applications and workflows using integrated process/data models and forms a foundation for business process optimization. It is shown that the integrated approach can improve existing business processes in enterprise resource planning (ERP), beyond business process re‐engineering (BPR) principles, once enhanced business process models are implemented. This approach eliminates need for a hierarchical representation of business processes and highlights the flexibility and visibility of business process implementation in ERP system environment.

Although process integration, automation, and optimization are illustrated using selected business process examples, it requires generalization of these enhancements over entire business blueprint of ERP system. Thus, one key limitation of this research is that it is not generalized for the entire business blueprint of ERP. This also requires changes to data structures beyond current relational data in many ERP systems.

This research provides an integrated approach to business process modeling beyond traditional functional and workflow applications by eliminating hierarchical nature of process and data elements.

Presents a review on implementing finite element methods on supercomputers, workstations and PCs and gives main trends in hardware and software developments. An appendix included at the end of the paper presents a bibliography on the subjects retrospectively to 1985 and approximately 1,100 references are listed.

Optimization of energy planning for growth and sustainable development has become very important in the context of climate change mitigation imperatives in developing countries. Existing models do not capture developing country realities adequately. The purpose of this paper is to conceptualizes a framework for energy strategy optimization of the Indian energy sector, which can be applied in all emerging economies.

Hierarchical multi‐objective policy optimization methodology adopts a policy‐centric approach and groups the energy strategies into multi‐level portfolios based on convergence of objectives appropriate to each level. This arrangement facilitates application of the optimality principle of dynamic programming. Synchronised optimization of strategies with respect to the common objectives at each level results in optimal policy portfolios.

The reductionist policy‐centric approach to complex energy economy modelling, facilitated by the dynamic programming methodology, is most suitable for policy optimization in the context of a developing country. Barriers to project implementation and cost risks are critical features of developing countries which are captured in the framework in the form of a comprehensive risk barrier index. Genetic algorithms are suitable for optimization of the first level objectives, while the efficiency approach, using restricted weight stochastic data envelopment analysis, is appropriate for higher levels of the objective hierarchy.

The methodology has been designed for application to the energy sector planning for India's 12th Five Year Plan for which the objectives of faster growth, better inclusion, energy security and sustainability have been identified. The conceptual framework combines, within the policy domain, the bottom‐up and top‐down processes to form a hybrid modelling approach yielding optimal outcomes, transparent and convincing to the policy makers. The research findings have substantial implications for transition management to a sustainable energy framework.

The methodology is general in nature and can be employed in all sectors of the economy. It is especially suited to policy design in developing countries with the ground realities factored into the model as project barriers. It offers modularity and flexibility in implementation and can accommodate all the key strategies from diverse sectors along with multiple objectives in the policy optimization process. It enables adoption of an evidence‐based and transparent approach to policy making. The research findings have substantial value for transition management to a sustainable energy framework in developing countries.

The purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs).

The model contains a cost objective function, generalized precedence relationship constraints, activity duration and start time constraints, lag/lead time constraints, execution mode (EM) constraints, project duration constraints, working time unit assignment constraints and resource constraints. The mixed-integer nonlinear programming (MINLP) superstructure of discrete solutions covers time–cost–resource options related to various EMs for project activities as well as variants for production process implementation.

The proposed model provides the exact optimal output data for project management, such as network diagrams, Gantt charts, histograms and S-curves. In contrast to classic scheduling approaches, here the optimal project structure is obtained as a model-endogenous decision. The project planner is thus enabled to achieve optimization of the production process simultaneously with resource-constrained scheduling of activities in discrete time units and at a minimum total cost.

A set of application examples are addressed on an actual construction project to display the advantages of proposed model.

The unique value this paper contributes to the body of knowledge reflects through the proposed MINLP model, which is capable of performing the exact cost optimization of production process (where presence and number of activities including their mutual relations are dealt as feasible alternatives, meaning not as fixed parameters) simultaneously with the associated resource-constrained project scheduling, whereby that is achieved within a uniform procedure.

Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be remedied by including a proper physical constraint in the parameter-fitting optimization algorithm. This paper aims to implement the constraint in the meta-heuristic simulated annealing (SA) optimization and Nelder–Mead simplex (NMS) algorithms to find JA model parameters that yield a physical hysteresis loop. The quasi-static B(H)-characteristics of a non-oriented (NO) silicon steel sheet are simulated, using existing measurements from a single sheet tester. Hysteresis loops received from the JA model under modified logistic function and piecewise cubic spline fitted to the average M(H) curve are compared against the measured minor and major hysteresis loops.

A physical constraint takes into account the anhysteretic susceptibility at the origin. This helps in the optimization decision-making, whether to accept or reject randomly generated parameters at a given iteration step. A combination of global and local heuristic optimization methods is used to determine the parameters of the JA hysteresis model. First, the SA method is applied and after that the NMS method is used in the process.

The implementation of a physical constraint improves the robustness of the parameter fitting and leads to more physical hysteresis loops. Modeling the anhysteretic magnetization by a spline fitted to the average of a measured major hysteresis loop provides a significantly better fit with the data than using analytical functions for the purpose. The results show that a modified logistic function can be considered a suitable anhysteretic (analytical) function for the NO silicon steel used in this paper. At high magnitude excitations, the average M(H) curve yields the proper fitting with the measured hysteresis loop. However, the parameters valid for the major hysteresis loop do not produce proper fitting for minor hysteresis loops.

The physical constraint is added in the SA and NMS optimization algorithms. The optimization algorithms are taken from the GNU Scientific Library, which is available from the GNU project. The methods described in this paper can be applied to estimate the physical parameters of the JA hysteresis model, particularly for the unidirectional alternating B(H) characteristics of NO silicon steel.

The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design space exploration in the early design phase and an in operational use product configurator, supporting the drafting and detailing of the solution predominantly in the later design phase.

Based on the comparison of modeled as-is and to-be processes of ascent assembly designs with and without design automation tools, an automation roadmap is developed. Using qualitative and quantitative assessments, the potentials and benefits, as well as acceptance and usage aspects, are evaluated.

Engineers tend to consider design automation for routine tasks. Yet, prototypical implementations support the communication and identification of the potential for the early stages of the design process to explore solution spaces. In this context, choosing from and interactively working with automatically generated alternative solutions emerged as a particular focus. Translators, enabling automatic downstream propagation of changes and thus ensuring consistency as to change management were also evaluated to be of major value.

A systematic validation of design automation in design practice is presented. For generalization, more case studies are needed. Further, the derivation of appropriate metrics needs to be investigated to normalize validation of design automation in future research.

Integration of design automation in early design phases has great potential for reducing costs in the market launch. Prototypical implementations are an important ingredient for potential evaluation of actual usage and acceptance before implementing a live system.

There is a lack of systematic validation of design automation tools supporting early design phases. In this context, this work contributes a systematically validated industrial case study. Early design-phases-support technology transfer is important because of high leverage potential.

This study primarily explores the influence of green warehousing, logistics optimization and social values and ethics on supply chain sustainability and economic performance. The study further examines the mediating role of supply chain sustainability between economic performance and green warehousing, logistics optimization and social values and ethics.

The study employs a quantitative research approach where survey data are collected from 200 managers of manufacturing companies in Ghana. The dataset is analyzed using partial least square structural equation modeling software (PLS-SEM) SmartPLS 3.

The results show that green warehousing and logistics optimization negatively influence economic performance but improves economic performance through supply chain sustainability. It is further discovered that social values and ethics have a positive influence on supply chain sustainability and economic performance.

This paper proposes and tests a theoretical model that explores the relationships between green warehousing, supply chain sustainability, economic performance, logistics optimization and social values and ethics through the resource dependency theory (RDT) in the manufacturing firms in Ghana.

This paper is a synthesis paper which seeks to discuss an optimisation framework using software components, which is a new emerging paradigm in computer science.

The goal of this paper is to show the efficiency of the software component approach for the implementation of optimisation frameworks for engineering systems in general, and electromagnetic systems in particular.

This paper highlights the component standard, a generator based on analytical expressions of the system, and an optimization service. References and examples show application in the area of electromagnetic components and systems.

This paper presents CADES, a framework dedicated to system design, based on optimization needs. The framework is defined with a standard implementing the software component paradigm and a pattern to use it. Indeed, this pattern details how to create and use a component (the model of the device to design).

This paper shows how the new emerging paradigm of software components can be used for building new generations of optimisation environment allowing capitalisation and reuse by combination of software components containing models and optimisation algorithms.

The purpose of this paper is to present an ontology‐based approach of context‐sensitive computing for the optimization of flexible manufacturing systems (FMS).

A context‐sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology‐based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA‐based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported.

Continuous improvement of the factory can be enhanced utilizing context‐sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.

The performance of context‐sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade‐off should be in between the extent of context processing and the required outcome of the support applications.

This paper includes the practical implications of context‐sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS.

Reported results provide a modular approach of context‐sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.