Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

Modular industrial equipment in cyber-physical production system: Architecture and integration

The design of numerical control systems for industrial machinery is a difficult task, especially when you create universal modular equipment with computer numerical control (CNC). This article presents a modular approach to the design of such systems. The modular control system under consideration is based on a multi-agent network, in which each entity (module) acts as an integral and indivisible part of the object as well as the enlarged structure. This approach allows one to combine the advantages of classical hierarchical control systems with the flexibility and reliability of decentralized multi-agent networks and also to carry out seamless integration of equipment built on the basis of this architecture into a cyber-physical production system (CPPS). The proposed architecture is implemented in the control system of a universal industrial platform. As an example, the apparatus for selective laser curing of a photopolymer to the surfaces of arbitrary shapes is represented. The general structure of the installation determined by the basic hardware and software modules and the network communication protocol are described

Background, Systematic Review, Challenges and Outlook

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe

Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation

[EN] Production planning and control faces increasing uncertainty, dynamics and complexity. Autonomous control methods proved themselves as a promising approach for coping with these challenges. However, there is a lack of knowledge regarding the interaction between autonomous control and precedent functions of production planning and control. In particular, up to now previous research has paid no attention to the influence of order release methods on the efficiency of autonomous control methods. Thereby, many researchers over the last decades provided evidence that the order release function has great influence on the logistic objective achievement in conventional production systems. Therefore, this paper examines the influence of order release methods on the efficiency of autonomous control methods by both theoretic evaluation and discrete event simulation. The simulation results indicate an overall high influence. Moreover, the logistic performance differs considerably depending on the implemented order release methods and the combinations of order release methods with autonomous control methods. The findings highlight demand for further research in this field.This research was funded by the German Research Foundation (DFG) under the reference number SCHO 540/26-1 “Methods for the interlinking of central planning and autonomous control in production”.Grundstein, S.; Schukraft, S.; Scholz-Reiter, B.; Freitag, M. (2015). Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation. International Journal of Production Management and Engineering. 3(1):43-56. https://doi.org/10.4995/ijpme.2015.3199SWORD435631Park, H.-S., & Tran, N.-H. (2012). An autonomous manufacturing system based on swarm of cognitive agents. Journal of Manufacturing Systems, 31(3), 337-348. doi:10.1016/j.jmsy.2012.05.002Pinedo, M. L. (2008). Scheduling. theory, algorithms and systems. New York, USA: Springer.Rekersbrink, H. (2012). Methoden zum selbststeuernden Routing autonomer logistischer Objekte. (doctoral disserta-tion). Universität Bremen, Bremen, Germany.Scholz-Reiter, B., Böse, F., Jagalski, T., & Windt, K. (2007a). Selbststeuerung in der betrieblichen Praxis. Ein Framework zur Auswahl der passenden Selbststeuerungsstrategie. Industrie Management, 23(3), 7-10.Scholz-Reiter, B., Freitag, M., de Beer, C., & Jagalski, T. (2006). The influence of production network's complexity on the performance of autonomous control methods. Proceedings of the 5th CIRP International Seminar on Computation in Manufacturing engineering, 317-320.Scholz-Reiter, B., Freitag, M., de Beer, C., & Jagalski, T. (2005b). Modelling and Analysis of Autonomous Shop Floor Control. Proceedings of 38th CIRP International Seminar on Manufacturing Systems, 16-18.Scholz-Reiter, B., & Scharke, H. (2000). Reaktive Planung. Industrie Management, 16(2), 21-26.Weng, M. X., Wu, Z., Qi, G., & Zheng, L. (2008). Multi-agent-based workload control for make-to-order manufacturing. International Journal of Production Research, 46(8), 2197-2213. doi:10.1080/00207540600969758Westphal, J. R. (2001). Komplexitätsmanagement in der Produktionslogistik - ein Ansatz zur flussorientierten Gestal-tung und Lenkung heterogener Produktionssysteme. Wiesbaden, Germany: Deutscher Universitäts Verlag.Wiendahl, H.-P. (Ed.). (1991). Anwendung der belastungsorientierten Auftragsfreigabe. Munich, Germany: Carl Hanser.Wiendahl, H.-P. (1997). Fertigungsregelung. Logistische Beherrschung von Fertigungsabläufen auf Basis des Trich-termodells. Munich, Germany: Carl Hanser.Wiendahl, H.-P. (Ed.). (2005). Betriebsorganisation für Ingenieure. Munich: Hanser.Wyssusek, B. (1999). Grundlagen der Systemanalyse. In Krallmann, H., Frank, H., & Gronau, N. (Eds.), Sytemanalyse im Unternehmen (pp. 19-43). Munich, Germany: Oldenbourg

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents. Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical. The proposed architecture has been tested to illustrate its feasibility and practicality

Collaborative approaches in sustainable and resilient manufacturing

Publisher Copyright: © 2022, The Author(s).In recent years, the manufacturing sector is going through a major transformation, as reflected in the concept of Industry 4.0 and digital transformation. The urge for such transformation is intensified when we consider the growing societal demands for sustainability. The notion of sustainable manufacturing has emerged as a result of this trend. Additionally, industries and the whole society face the challenges of an increasing number of disruptive events, either natural or human-caused, that can severely affect the normal operation of systems. Furthermore, the growing interconnectivity between organizations, people, and physical systems, supported by recent developments in information and communication technologies, highlights the important role that collaborative networks can play in the digital transformation processes. As such, this article analyses potential synergies between the areas of sustainable and resilient manufacturing and collaborative networks. The work also discusses how the responsibility for the various facets of sustainability can be distributed among the multiple entities involved in manufacturing. The study is based on a literature survey, complemented with the experience gained from various research projects and related initiatives in the area, and is organized according to various dimensions of Industry 4.0. A brief review of proposed approaches and indicators for measuring sustainability from the networked manufacturing perspective is also included. Finally, a set of key research challenges are identified to complement strategic research agendas in manufacturing.publishersversionpublishe

Agent-based manufacturing — review and expert evaluation

The advent of smart manufacturing and the exposure to a new generation of technological enablers have revolutionized the way manufacturing process is carried out. Cyber-Physical Production Systems (CPPS) are introduced as main actors of this manufacturing shift. They are characterized for having high levels of communication, integration and computational capabilities that led them to a certain level of autonomy. Despite the high expectations and vision of CPPS, it still remains an exploratory topic. Multi-Agent Systems (MAS) have been widely used by software engineers to solve traditional computing problems, e.g., banking transactions. Because of their high levels of distribution and autonomous capabilities, MAS have been considered by the research community as a good solution to design and implement CPPS. This work first introduces a collection of requirements and characteristics of smart manufacturing. A comprehensive review of various research applications is presented to understand the current state of the art and the application of agent technology in manufacturing. Considering the smart manufacturing requirements and current research application, a SWOT analysis was formulated which identifies pros and cons of the implementation of agents in industry. The SWOT analysis was further validated by an industrial expert evaluation and the main findings and discussion of the results are presented