Smart manufacturing scheduling: A literature review

Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.     

A review of digital twin in product design and development

In the era of digitalization, there are many emerging technologies, such as the Internet of Things (IoT), Digital Twin (DT), Cloud Computing and Artificial Intelligence (AI), which are quickly developped and used in product design and development. Among those technologies, DT is one promising technology which has been widely used in different industries, especially manufacturing, to monitor the performance, optimize the progresses, simulate the results and predict the potential errors. DT also plays various roles within the whole product lifecycle from design, manufacturing, delivery, use and end-of-life. With the growing demands of individualized products and implementation of Industry 4.0, DT can provide an effective solution for future product design, development and innovation. This paper aims to figure out the current states of DT research focusing on product design and development through summarizing typical industrial cases. Challenges and potential applications of DT in product design and development are also discussed to inspire future studies.     

MES-integrated digital twin frameworks

Industry 4.0-based manufacturing systems are equipped with Cyber-Physical Systems that are characterized by a strong interlinkage between the real world and the digital one: actions in one world have an impact on the other. In this paradigm, Digital Twins (DT) are defined as simulation models that are both getting data from the field and triggering actions on the physical equipment. However, most of the claimed DT in literature are only replicating the real system in a synchronized fashion, without feeding back actions on the control system of the equipment. In literature, these are referred to as Digital Shadows (DS). The paper proposes a way to integrate a DS simulation model with the Manufacturing Execution System (MES) in this way creating a DT. The MES-integrated DT is used for decision making thanks to the presence of an intelligence layer that hosts the rules and the knowledge to choose among alternatives. The paper proposes two frameworks based on the MES-integrated DT: one for managing error states and one for triggering disassembly processes as a consequence of low assembly quality. The DT simulation is developed and integrated with the MES of the Industry 4.0 Laboratory at the School of Management of Politecnico di Milano, where the proposed frameworks have been tested and validated.     

How to model and implement connections between physical and virtual models for digital twin application

Digital twin (DT) is a virtual mirror (representation) of a physical world or a system along its lifecycle. As for a complex discrete manufacturing system (DMS), it is a digital model for emulating or reproducing the functions or actions of a real manufacturing system by giving the system simulation information or directly driven by a real system with proper connections between the DT model and the real-world system. It is a key building block for smart factory and manufacturing under the Industry 4.0 paradigm. The key research question is how to effectively create a DT model during the design stage of a complex manufacturing system and to make it usable throughout the system’s lifecycle such as the production stage. Given that there are some existing discussions on DT framework development, this paper focuses on the modeling methods for rapidly creating a virtual model and the connection implementation mechanism between a physical world production system at a workshop level and its mirrored virtual model. To reach above goals, in this paper, the discrete event system (DES) modeling theory is applied to the three-dimension DT model. First, for formally representing a manufacturing system and creating its virtual model, seven basic elements: controller, executor, processor, buffer, flowing entity, virtual service node and logistics path of a DMS have been identified and the concept of the logistics path network and the service cell is introduced to uniformly describe a manufacturing system. Second, for implementing interconnection and interaction, a new interconnection and data interaction mechanism between the physical system and its virtual model for through-life applications has been designed. With them, each service cell consists of seven elements and encapsulates input/output information and control logic. All the discrete cells are constructed and mapped onto different production-process-oriented digital manufacturing modules by integrating logical, geometric and data models. As a result, the virtual-physical connection is realized to form a DT model. The proposed virtual modeling method and the associated connection mechanism have been applied to a real-world workshop DT to demonstrate its practicality and usefulness.     

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing system paradigm A new mathematical cost model is proposed for assessing the cost-benefit analysis of introducing Industry 4.0 elements to the manufacturing facility, specifically, integrating and connecting external suppliers as strategic partners and establishing an infrastructure for communicating information between the manufacturing company and its strategic suppliers. The mathematical model takes into consideration the bi-directional relationship between hourly rates and total hours assigned to workcentres/activities in a certain production period. A case study, from a multinational machine builder, is developed and solved using the proposed model. Results suggest that though an additional cost is required to establish infrastructure to connect suppliers, the responsiveness and agility achieved resulting from uncertainty outweighs the additional cost.     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

Digital twin-driven manufacturing equipment development

Currently, expectations of shorter time-to-market and improved product performance are placing greater demands on manufacturing companies. However, the optimization and redesign work between the design stage and the prototype design and manufacturing stage in the traditional product development process lengthens the required product development cycle time (which lasts up to several years in extreme cases). The manufacturing phase for the physical prototype of the product is especially time-consuming and costly. The above reasons make the common product development process increasingly unable to meet the demands of market needs. Motivated by this need, the digital twin (DT)-driven manufacturing equipment (ME) development method is studied in this paper. This method contains three main core elements of the design method based on axiomatic design (AD) theory, the construction of DT models related to ME development, and DT-based validation analysis. The advantage of this method is that it can incorporate the physical prototype manufacturing stage into the digital space with the high-fidelity model provided by the DT technology, which ensures the confidentiality of the design scheme validation while freeing it from the physical prototype stage. This avoids the cost of physical prototyping, shortens the product development cycle, and improves the efficiency of new ME development. At the end of this paper, a case study of the development of a virtual machining dynamic performance test bench (VM-TB) is carried out to show the implementation flow of this proposed method, and its operability and effectiveness are verified.     

Ontology-based information modeling method for digital twin creation of as-fabricated machining parts

The Digital Twin concept, as the cutting edge of digital manufacturing solution for modern industries, plays a significant role in the Industry 4.0 era. One key enabling technology for developing a DT is the information modeling of physical products, so as to combine the physical world with the cyberspace more extensively and closely. Therefore, the modeling approach to managing as-fabricated data of physical products, which faithfully reflects the product's physical status, emerges to be pivotal. This paper addresses the problem of modeling as-fabricated parts in the machining process, which is difficult to accomplish by relevant methods, and hinders the long-term data archiving and reuse of process data. Furthermore, to fill the gap, an ontology-based information modeling method of as-fabricated parts is proposed as the recommendation to create DTs for as-fabricated parts. It provides a simple and standardized process for companies to create DTs of as-fabricated parts by specifying the information classification, the contents to be modeled and the modeling method. To validate the effectiveness of the proposed approach, a case study is undertaken in an aviation manufacturing plant at last. The result shows that the proposed information modeling methodology is readily to DT creation of as-fabricated parts.     

Product-based and resource-based heterarchical approaches for dynamic FMS scheduling

Two fundamental approaches can be adopted for heterarchical scheduling of flexible manufacturing systems: resource-based scheduling or product-based scheduling. The first part of this paper defines the basic concepts of these two methods (modeling, structure design methodology, communication protocols). The second describes the specific advantages and drawbacks of these two approaches using the results provided to illustrate the discussion. The final section considers a real implementation of the product-based heterarchical philosophy.     

Design for agility: a scheduling perspective

Agility is the ability of a company to produce a variety of products in a short time and at a low cost. This demands that products and manufacturing systems be simple, robust, and flexible to allow for quick response to the changing market. Scheduling of manufacturing systems in a changing environment is complex. This paper attempts to simplify scheduling of manufacturing systems through appropriate design of products and manufacturing systems. An attempt has been made to generate rules that allow to design products and systems for easy scheduling. Four design for agility rules are proposed in the paper. The first rule deals with decomposition of a manufacturing system. The rule simplifies the scheduling problem and reduces the total changeover cost. The second rule is concerned with design of products with robust scheduling characteristics. Product designs with robust scheduling characteristics can improve the response of a manufacturing system to the changes in the product demand and mix and reconfigurability of the system. The third rule results in a streamlined assembly line which has the type of product flow that simplifies scheduling. The fourth rule emphasizes the reduction of the number of stations in an assembly line. Examples are provided to demonstrate the benefits from using these rules. The implementation of the four rules is also discussed.     

Understanding digital transformation in advanced manufacturing and engineering: A bibliometric analysis,topic modeling and research trend discovery

Digital transformation (DT) is the process of combining digital technologies with sound business models to generate great value for enterprises. DT intertwines with customer requirements, domain knowledge, and theoretical and empirical insights for value propagations. Studies of DT are growing rapidly and heterogeneously, covering the aspects of product design, engineering, production, and life-cycle management due to the fast and market-driven industrial development under Industry 4.0. Our work addresses the challenge of understanding DT trends by presenting a machine learning (ML) approach for topic modeling to review and analyze advanced DT technology research and development. A systematic review process is developed based on the comprehensive DT in manufacturing systems and engineering literature (i.e., 99 articles). Six dominant topics are identified, namely smart factory, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business model-centric digital transformation. The study also contributes to adopting and demonstrating the ML-based topic modeling for intelligent and systematic bibliometric analysis, particularly for unveiling advanced engineering research trends through domain literature.     

Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling

Centrifugal impeller (CI) manufacturing is moving toward a new paradigm, with the objective to improve efficiency and competitiveness through Industry 4.0 and smart manufacturing. Making a CI developable and ruled has become a crucial technology to obviously improve machining efficiency and save costs although it may bring negative effects on aerodynamic performance accordingly. Hence, it is extremely challenging to consider and balance both machinability and aerodynamic performance in the process of CI geometric optimization. Digital Twin (DT) provides an attractive option for the integrated design and manufacturing due to multi-dimension and real-time. This paper breaks traditional procedures and presents a DT-based optimization strategy on the consideration of both machining efficiency and aerodynamic performance, as well as builds a reified 5-dimensional DT model. The virtual model consists of three sub-functional modules, including geometric modeling, machining optimization and aerodynamic performance evaluation. A tool-path generation method for CI five-axis flank milling is proposed to improve machining efficiency. The negative influences on aerodynamic performance and internal flow field are simulated and analyzed. Reinforce Learning is introduced to determine the optimization decision-making. Machining experiment and performance test with respect to various CI workpieces are conducted to provide immediate feedback to DT model. Real world and virtual world are combined to make CI geometry dynamically updated and iteratively optimized, which is desirable and significative to effectively shorten cycles and save costs in CI development.     

Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT

The rapid booming of advanced information and communication technologies (ICT) has promoted an encouraging smart, connected product (SCP) market that further triggers the development of manufacturing towards the servitization proposition, viz. smart product-service systems (PSS). Smart PSS aims to provide a solution (product-service) with high satisfaction and less environmental influence by leveraging SCP as the media tool. Its solution design should not just focus on the physical world nor only be enabled by the cloud side, while the cyber world and the edge side must be included in the Industry 4.0. However, only few current researches investigate about the smart PSS, let alone an overall cyber-physical and edge-cloud discussion to support its solution design. In order to fill this gap, this work proposes an edge-cloud orchestration driven solution design based on the cyber-physical systems (CPS) and industrial Internet of Things (IIoT). To make our ideas concrete, a real-life milling process was conducted as an illustrative example. It is hoped that this study can furnish industrial enterprises with meaningful sights in the process of servitization and value co-creation.     

Virtual factory approach for implementation of holonic control in industrial applications: A case study in die-casting industry

Design and implementation of holonic manufacturing control systems for the real industrial applications require risky, careful decisions to ensure that the manufacturing system will successfully satisfy the demands of an ever-changing market. This paper suggests a virtual reality (VR)-based methodology for enhancing the design and implementation process of holonic control systems in manufacturing practice. The major focus has been given to the implementation of holonic control into the small to medium size manufacturing enterprises (SMEs).     

A methodology for implementation of mobile robot in adaptive manufacturing environments

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Flexible job shop scheduling is one of the most effective methods for solving multiple varieties and small batch production problems in discrete manufacturing enterprises. However, limitations of actual transportation conditions in the flexible job shop scheduling problem (FJSP) are neglected, which limits its application in actual production. In this paper, the constraint influence imposed by finite transportation conditions in the FJSP is addressed. The coupling relationship between transportation and processing stages is analyzed, and a finite transportation conditions model is established. Then, a three-layer encoding with redundancy and decoding with correction is designed to improve the genetic algorithm and solve the FJSP model. Furthermore, an entity-JavaScript Object Notation (JSON) method is proposed for transmission between scheduling services and Digital Twin (DT) virtual equipment to apply the scheduling results to the DT system. The results confirm that the proposed finite transportation conditions have a significant impact on scheduling under different scales of scheduling problems and transportation times.