Service-oriented SCADA and MES supporting petri nets based orchestrated automation systems

The fusion of mechatronics, communication, control and information technologies has allowed the introduction of new automation paradigms into the production environment. The virtualization of the production environment facilitated by the application of the service-oriented architecture paradigm is one of major outcomes of that fusion. On one side, service-oriented automation works based on exposition, subscription and use of automation functions represented by e.g. web services. On the other side, the evolution of traditional industrial systems, particularly in the production area, as a response to architectural and behavioural (functional) viewpoints of the ISA95 enterprise architecture, where a close inter-relation between SCADA, DCS and MES systems facilitate the management and control of the production environment. Automation functions are increasingly performed by the composition and orchestration of services. Among other methods, the application of formal Petri net based orchestration approaches is being industrially established. This paper presents the major characteristics that such a Petri net based orchestration presents when it is developed, implemented and deployed in an industrial environmentThe research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 258682 (IMC-AESOP: ArchitecturE for Service-Oriented Process - Monitoring and Control) and 224053 (CONET: Cooperating Objects NETwork of excellence)

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Dynamic hybrid simulation of batch processes driven by a scheduling module

Simulation is now a CAPE tool widely used by practicing engineers for process design and control. In particular, it allows various offline analyses to improve system performance such as productivity, energy efficiency, waste reduction, etc. In this framework, we have developed the dynamic hybrid simulation environment PrODHyS whose particularity is to provide general and reusable object-oriented components dedicated to the modeling of devices and operations found in chemical processes. Unlike continuous processes, the dynamic simulation of batch processes requires the execution of control recipes to achieve a set of production orders. For these reasons, PrODHyS is coupled to a scheduling module (ProSched) based on a MILP mathematical model in order to initialize various operational parameters and to ensure a proper completion of the simulation. This paper focuses on the procedure used to generate the simulation model corresponding to the realization of a scenario described through a particular scheduling

Darwinism, probability and complexity : market-based organizational transformation and change explained through the theories of evolution

The study of transformation and change is one of the most important areas of social science research. This paper synthesizes and critically reviews the emerging traditions in the study of change dynamics. Three mainstream theories of evolution are introduced to explain change: the Darwinian concept of survival of the fittest, the Probability model and the Complexity approach. The literature review provides a basis for development of research questions that search for a more comprehensive understanding of organizational change. The paper concludes by arguing for the development of a complementary research tradition, which combines an evolutionary and organizational analysis of transformation and change