László Monostori

Stability-oriented evaluation of rescheduling strategies, by using simulation

The paper proposes a simulation-based evaluation technique for the testing, validation and benchmarking of rescheduling methods. Dynamic and stochastic scheduling problems with possible scheduling environments are presented. Based on our review of the related literature, frequently applied rescheduling approaches for solving dynamic and stochastic job-shop scheduling problems are analyzed. Schedule evaluation techniques, related measures, and simulation-based schedule evaluation solutions are also introduced and categorized. Certain stability-oriented evaluations of periodic and hybrid rescheduling methods are described for both single- and multi-machine (job-shop) cases. Finally, an industrial application of the proposed method is presented.

Cooperative control in production and logistics

Classical applications of control engineering and information and communication technology (ICT) in production and logistics are often done in a rigid, centralized and hierarchical way. These inflexible approaches are typically not able to cope with the complexities of the manufacturing environment, such as the instabilities, uncertainties and abrupt changes caused by internal and external disturbances, or a large number and variety of interacting, interdependent elements. A paradigm shift, e.g., novel organizing principles and methods, is needed for supporting the interoperability of dynamic alliances of agile and networked systems. Several solution proposals argue that the future of manufacturing and logistics lies in network-like, dynamic, open and reconfigurable systems of cooperative autonomous entities. The paper overviews various distributed approaches and technologies of control engineering and ICT that can support the realization of cooperative structures from the resource level to the level of networked enterprises. Standard results as well as recent advances from control theory, through cooperative game theory, distributed machine learning to holonic systems, cooperative enterprise modelling, system integration, and autonomous logistics processes are surveyed. A special emphasis is put on the theoretical developments and industrial applications of Robustly Feasible Model Predictive Control (RFMPC). Two case studies are also discussed: i) a holonic, PROSA-based approach to generate short-term forecasts for an additive manufacturing system by means of a delegate multi-agent system (D-MAS); and ii) an application of distributed RFMPC to a drinking water distribution system.

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

Cyber-physical production systems: Roots from manufacturing science and technology

Abstract One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPS) which are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet. Cyber-Physical Production Systems (CPPS), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other, may lead to the 4th Industrial Revolution, frequently noted as Industrie 4.0. The paper underlines that there are significant roots generally – and particularly in the CIRP community – which point towards CPPS. Expectations and the related new R&D challenges are also outlined.

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

Decision support solutions for factory and network logistics operations

The paper examines the relationship of decision levels, performance measures and modelling and decision support approaches through the example of two implemented decision support systems for manufacturing and logistics application fields. Aside from highlighting the relevance of decision support for making industrial networks fit for emerging challenges, the relevance of the two presented EU FP7 projects VFF and ADVANCE to the Factories of the Future vision is shown. A discussion of the two projects outlines future research, with particular focus on challenges that arise from integration across levels of the decision hierarchy, within an organisationally heterogeneous network.

Data type definition and handling for supporting interoperability across organizational borders

Organisational heterogeneity—especially in networks where new members may join at any time—requires ongoing actions to maintain interoperability. On the level of data interoperability, this highlights the importance of various aspects of data model and dataflow design, as well as handling of data at run-time. The latter is certain to require automated means of data model negotiation, and—while today’s design processes are far from fully automated—such means can leverage productivity and support verification procedures in data modelling and dataflow design as well. The paper presents results in one possible approach to data type definition and manipulation, through the example of the ADVANCE dataflow engine and its type-related features. Aside from an XML-based type system, type inference algorithms are presented which are employed both during design and flow execution.

Generic data structure and validation methodology for simulation of manufacturing systems

In recent years, the analysis and evaluation of manufacturing systems’ behaviour and their performance became essential. Discrete event simulation (DES) as a digital enterprise technology is an effective tool both in production related decision support processes and in structure or performance analysis of manufacturing systems. Building a DES model of a manufacturing system is a difficult task and requires special competence. Reducing the efforts spent on draft simulation is the aim of an ongoing research which is presented in the paper. The developed framework simplifies and accelerates the process of model building. A production oriented implementation of the ANSI/ISA-95 standard was used by the proposed modelling methodology to define a generic data structure supporting the creation of models without specific knowledge related to the simulation software applied. The data structure enables the development and application of proprietary simulation engines tailored for specific problems. On the other hand, numerous objective tests containing simulation model validation methods are also announced through a well-described example problem. By this method, several validating key performance indicators (KPIs) were tested, and it was indirectly determined whether the simulation model adequately represents the behaviour of the real system. The automatic simulation model generation in our approach enables using different simulation tools and presented through the examples of both commercial and self-developed software.