model predictive control tools for evolutionary plants

The analysis and design of control system configurations for automated production systems is generally a challenging problem, in particular given the increasing number of automation devices and the amount of information to be managed. This problem becomes even more complex when the production system is characterized by a fast evolutionary behaviour in terms of tasks to be executed, production volumes, changing priorities, and available resources. Thus, the control solution needs to be optimized on the basis of key performance indicators like flow production, service level, job tardiness, peak of the absorbed electrical power and the total energy consumed by the plant. This paper proposes a prototype control platform based on Model Predictive Control (MPC) that is able to impress to the production system the desired functional behaviour. The platform is structured according to a two-level control architecture. At the lower layer, distributed MPC algorithms control the pieces of equipment in the production system. At the higher layer an MPC coordinator manages the lower level controllers, by taking full advantage of the most recent advances in hybrid control theory, dynamic programming, mixed‐integer optimization, and game theory. The MPC-based control platform will be presented and then applied to the case of a pilot production plant

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Modelling flexible manufacturing systems through discrete event simulation

As customisation and product diversification are becoming standard, industry is looking for strategies to become more adaptable in responding to customer’s needs. Flexible manufacturing systems (FMS) provide a unique capability where there is a need to provide efficiency through production flexibility. Full potential of FMS development is difficult to achieve due to the variability of components within this complex manufacturing system. It has been recognised that there is a requirement for decision support tools to address different aspects of FMS development. Discrete event simulation (DES) is the most common tool used in manufacturing sector for solving complex problems. Through systematic literature review, the need for a conceptual framework for decision support in FMS using DES has been identified. Within this thesis, the conceptual framework (CF) for decision support for FMS using DES has been proposed. The CF is designed based on decision-making areas identified for FMS development in literature and through industry stakeholder feedback: set-up, flexibility and schedule configuration. The CF has been validated through four industrial simulation case studies developed as a part of implementation of a new FMS plant in automotive sector. The research focuses on: (1) a method for primary data collection for simulation validated through a case study of material handling robot behaviour in FMS; (2) an approach for evaluation of optimal production set-up for industrial FMS with DES; (3) a DES based approach for testing FMS flexibility levels; (4) an approach for testing scheduling in FMS with the use of DES. The study has supported the development of systematic approach for decision making in FMS development using DES. The approach provided tools for evidence based decision making in FMS

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

Intelligent flexible manufacturing system control

This research proposes a generic decision making system structure for real time despatch control in small Flexible Manufacturing Systems. This is to satisfy the requirement for low cost control systems that can be flexibly adapted to a wide range of production environments. A simulation environment has been developed to emulate the detail real time despatch control of flexible manufacturing systems. This environment allows analysis of the decision making process and its effects. A model of a modular type FMS is used to study decision making in real time FMS control. Real time control is dynamic, the decision criteria change with the production states of the system. Decision making is based on both quantitative and qualitative factors. Apart from production quantity and time which are quantitative, there are installation dependent and production situations better expressed in states which are non-numeric. The knowledge based representation developed from artificial intelligence work is superior in modelling both mathematical scheduling research and discrete states information. Recognising the importance of system particular knowledge to effective control of the system, system independent functions are separated out to form elements of a generic control system architecture. This generic architecture contains elements of information handling to process information to service the scheduling decision making element. A core for regulating information flow and a data interface definintion allows this control architecture to be hardware independent. The decision making mechanism dependent on machinery hardware configuration and particular production characteristics can then be designed and interface to the architecture to form a complete control system. A decision design methodology has been designed to guide the design of the scheduling decision making system. The methodology addresses the design of work queue formation timing and the characteristics for each resource in the system. These are then integrated into a complete work flow control system by the resolution of contentions between the individual queues. The application of the design methodology and control system architecture is illustrated.Ph

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

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