Integrating continuous-time and discrete-event concepts in modelling and simulation of manufacturing machines

Using simulation models for the development and testing of control systems can have significant advantages over using real machines. This paper demonstrates the suitability of the ¿ language for modelling, simulation and control of manufacturing machines. The language integrates a small number of powerful orthogonal continuous-time and discrete-event concepts. The continuous-time part of ¿ is based on DAEs; the discrete-event part is based on a CSP-like concurrent programming language. Models are specified in a symbolic mathematical notation. A case study is presented of a transport system consisting of conveyor belts.

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis

Advances in Environmental Informatics: Integration of Discrete Event Simulation Methodology with ecological Material Flow Analysis for Modelling eco-efficient Systems

AbstractIn the first section we provide a short review of the rather young field of Environmental Informatics. We argue that Environmental Informatics methods and tools are powerful means in environmental information processing for supporting environmental protection and sustainable development. In the second part we introduce an integrated approach for modeling ecoefficient systems such as complex production systems under an economic as well as an ecological view. This modeling approach allows for the implementation of a combined model representing material and energy flows and bottle necks in machine capacities at the same time (instead of using two different models with different software tools). Finally present a case study from a real waver production site as a proof of concept for this substantial contribution to Environmental Informatics

Valid Methodology for Using Discrete Event Simulation to Improve the Resource Consumption for the Manufacturing of Masonry Units

AbstractOwing to a high inflexibility of the factory layout, manufacturers of masonry units are bound to organizational adjustments seizing optimization measures. Regarding such plants, having a given complexity based on a rigid concatenation of heterogeneous sub-processes with heavy goods to be transported, conventional measures such as Lean Management principles involve great efforts in execution. Therefore, an IT solution for planning and controlling the operational processes is to be developed. This solution will be implemented through simulation-supported optimization to support dealing with a higher complexity and setting up a more resource-efficient manufacturing process.As a basis, a corresponding factory is mapped sufficiently accurate in every detail in a discrete event analysis (DEA) model. In this paper, a methodology, how to configure an arbitrary calcium silicate masonry unit (CS) plant in a simulation model, is presented for the first time. Relevant data is cataloged and modelling approaches for the controlling methods are pointed out.Special regard is paid to optimization measures at the crucial point of the transition from bulk material to piece goods, which has not been regarded yet in discrete event simulation modelling. The major aspect is a comparison of a unit-based approach and a variable-controlled approach, regarding the runtime.A case study follows conclusively, which aided in validating the methodology by simulating various scenarios. As a result, several strategic and operational optimization potentials were identified

A Virtual factory data model as a support tool for the simulation of manufacturing systems

The design of a manufacturing systems is a complex and critical activity entailing decisions with an impact on a long time horizon and a major commitment of financial resources. Indeed, the modelling, simulation and evaluation of manufacturing systems are relevant activities both in the design and the operational phases of a factory. This paper grounds on the results of the Virtual Factory Framework (VFF) Project and addresses the use of an ontology based model of a production system to support the construction of a performance evaluation model

Advances in Environmental Informatics: Integration of Discrete Event Simulation Methodology with ecological Material Flow Analysis for Modelling eco-efficient Systems

AbstractIn the first section we provide a short review of the rather young field of Environmental Informatics. We argue that Environmental Informatics methods and tools are powerful means in environmental information processing for supporting environmental protection and sustainable development. In the second part we introduce an integrated approach for modeling ecoefficient systems such as complex production systems under an economic as well as an ecological view. This modeling approach allows for the implementation of a combined model representing material and energy flows and bottle necks in machine capacities at the same time (instead of using two different models with different software tools). Finally present a case study from a real waver production site as a proof of concept for this substantial contribution to Environmental Informatics

Simulation-based impact analysis for sustainable manufacturing design and management

This research focuses on effective decision-making for sustainable manufacturing design and management. The research contributes to the decision-making tools that can enable sustainability analysts to capture the aspects of the economic, environmental and social dimensions into a common framework. The framework will enable the practitioners to conduct a sustainability impact analysis of a real or proposed manufacturing system and use the outcome to support sustainability decision. In the past, the industries had focused more on the economic aspects in gaining and sustaining their competitive positions; this has changed in the recent years following the Brundtland report which centred on incorporating the sustainability of the future generations into our decision for meeting today’s needs (Brundtland, 1987). The government regulations and legislation, coupled with the changes in consumers’ preference for ethical and environmentally friendly products are other factors that are challenging and changing the way companies, and organisations perceive and drive their competitive goals (Gu et al., 2015). Another challenge is the lack of adequate tools to address the dynamism of the manufacturing environment and the need to balance the business’ competitive goal with sustainability requirements. The launch of the Life Cycle Sustainability Analysis (LCSA) framework further emphasised the needs for the integration and analysis of the interdependencies of the three dimensions for effective decision-making and the control of unintended consequences (UNEP, 2011). Various studies have also demonstrated the importance of interdependence impact analysis and integration of the three sustainability dimensions of the product, process and system levels of sustainability (Jayal et al., 2010; Valdivia et al., 2013; Eastwood and Haapala, 2015). Although there are tools capable of assessing the performance of either one or two of the three sustainability dimensions, the tools have not adequately integrated the three dimensions or address the holistic sustainability issues. Hence, this research proposes an approach to provide a solution for successful interdependence impact analysis and trade-off amongst the three sustainability dimensions and enable support for effective decision-making in a manufacturing environment. This novel approach explores and integrates the concepts and principles of the existing sustainability methodologies and frameworks and the simulation modelling construction process into a common descriptive framework for process level assessment. The thesis deploys Delphi study to verify and validate the descriptive framework and demonstrates its applicability in a case study of a real manufacturing system. The results of the research demonstrate the completeness, conciseness, correctness, clarity and applicability of the descriptive framework. Thus, the outcome of this research is a simulation-based impact analysis framework which provides a new way for sustainability practitioners to build an integrated and holistic computer simulation model of a real system, capable of assessing both production and sustainability performance of a dynamic manufacturing system