Cloud Manufacturing Research Articles

Trust and reputation systems for production networks

Trust and reputation in production networks are of increasing importance, both from theoretical and practical points of view. One can find some case studies considering trustfulness in different areas such as supply chains, cloud, distributed and platform-based manufacturing as well – but considering trustfulness is not yet widespread in this field. The aim of the paper is twofold: first, to provide an overview about trust and reputation systems (TRSs) including definitions, classification of systems, rankings, challenges attack types, defense mechanisms and case studies. Second, to compose a requirement list – based on challenges found in the literature and consequences drawn – towards a TRS that can be used between production companies and supply chain members and is able to face the issues found in the scientific papers and introduced by the authors.

An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform

An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform

Інтегральний підхід до логістики постачання, виробництва та дистрибуції на основі формалізації логістичних бізнес-процесів

The article considers the problem of integration of logistic business processes of supply of resources (logistics), production and distribution at an industrial enterprise. The aim of the article is to develop an integrated system of logistic management of a production enterprise on the basis of a study of the mechanism for managing the material flow. As a result of the study, the principles of building a logistic system for integrated management of purchasing, production and distribution logistics of enterprise have been formulated and an integrated management scheme has been elaborated, in which complexes of tasks at certain levels of management have been allocated. At the upper level of management is the distribution of the quarterly production program by months, taking into account production capacities based on the principle of specialization of production in time. At the middle level of management – drawing up interrelated plans and schedules of logistics, manufacture and shipment of finished products. At the lower level of management – the compilation of shift-daily tasks on the basis of operational planning of the work of workshops, sites and individual workplaces. When drawing up interrelated calendar plans-schedules for the release and shipment of products, decisions are made that simultaneously meet the requirements of production and the conditions for the sale of products. These decisions are the basis ones for further specification of them by production units of the enterprise. The proposed scheme provides for communication along a closed loop of the logistic system of integrated management: planning, accounting, control, regulation. To solve problems at all levels of enterprise management, it is advisable to use modern software systems, such as SAP ERP, Oracle Supply Chain Management, Microsoft Dynamics 365 Supply Chain Management, Infor SCM, Plex Manufacturing Cloud.

Dynamic decision-making for knowledge-enabled distributed resource configuration in cloud manufacturing considering stochastic order arrival

The emergence of COVID-19 caused the stagnation of production activities and promoted the changing market demand. These uncertainties not only brought great challenges to the manufacturing approaches led by a single enterprise, but also threatened the stability of inherent supply chain. To maintain market competitiveness, an efficient distributed manufacturing resource allocation method is urgently needed by manufacturers. Cloud manufacturing (CMfg) is an advanced service-oriented manufacturing paradigm that breaks physical space constraints to integrate distributed resources across enterprises, and provides on-demand configuration of manufacturing services for personalized consumer needs in real-time. The focus of this paper is to achieve dynamic configuration of distributed resources in CMfg considering stochastic order arrival, while reducing overall completion time and improving resource utilization. First, a dynamic knowledge graph for distributed resources is constructed, and its definition and construction methods are introduced. Secondly, semantic matching between massive optional manufacturing resources and multiple types of subtasks is achieved through knowledge extraction, thereby obtaining a candidate set of manufacturing resources that meet basic requirements for each subtask. An artificial intelligence (AI) scheduler based on deep reinforcement learning is developed, and order urgency is incorporated into the design of state observation vectors. AI scheduler can generate optimal decision results based on environmental observations, select high-quality manufacturing service compositions over candidate sets, and ultimately achieve efficient distributed resources configuration. Finally, Dueling DQN-based training method is put forward to optimize AI scheduler, enabling adaptable decision-making performance in dynamic environment. In the experiment, a simulation environment with 18 different settings is designed that considers stochastic processing time, random order compositions and various order arrival patterns. The proposed graph-based matching method, scheduling policy learning method and dynamic decision-making method are tested in the simulation environment. The experiment results demonstrate that the cognitive and AI joint driven distributed manufacturing resource configuration method is superior to traditional methods in terms of policy learning speed and scheduling solution quality.

A Hybrid Whale Optimization Algorithm for Quality of Service-Aware Manufacturing Cloud Service Composition

Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services (MCSs) to generate an optimized MCSC that can meet the diverse requirements of customers. However, many available MCSs, deployed in the CMfg platform, have the same function but different QoS attributes. It is a great challenge to achieve optimal MCSC with a high QoS. In order to obtain better optimization results efficiently for the QoS-MCSC problems, a whale optimization algorithm (WOA) with adaptive weight, Lévy flight, and adaptive crossover strategies (ASWOA) is proposed. In the proposed ASWOA, adaptive crossover inspired by the genetic algorithm is developed to balance exploration and exploitation. The Lévy flight is designed to expand the search space of the WOA and accelerate the convergence of the WOA with adaptive crossover. The adaptive weight is developed to extend the search scale of the exploitation. Simulation and comparison experiments are conducted on various benchmark functions and different scale QoS-MCSC problems. The QoS attributes of the problems are randomly and symmetrically generated. The experimental results demonstrate that the proposed ASWOA outperforms other compared cutting-edge algorithms.

Sequential auction for cloud manufacturing resource trading: A deep reinforcement learning approach to the lot-sizing problem

Cloud manufacturing is a rapidly growing trend in modern manufacturing, which has transformed the traditional operations and value chain structure of enterprises. It is crucial to develop a rational and effective trading mechanism of cloud manufacturing resources to meet the ever-increasing demands in this new environment. This paper proposes a sequential auction-based paradigm for the trading of manufacturing resources. The main challenge of the design of the paradigm is to determine the dynamic lot size of resources allocated to each auction considering the uncertainty of arriving demands. To achieve this, we first develop a competitive game model to identify optimal bidding strategies of arrived customers and estimate the expected revenue for each round with the given lot size. Secondly, we construct a Markov decision process (MDP) model to characterize the dynamics of the arrival of stochastic demand and the inventory transition of the manufacturing resources in sequential auctions. Lastly, we leverage a data-driven approach by integrating machine learning with an offline deep reinforcement learning (RL) approach. Specifically, we employ a long short-term memory (LSTM) model to predict forthcoming demands in the environment and develop the deep Q-network (DQN) learning algorithm to optimize lot-sizing policy by interacting with the well-learned LSTM environment model. Our simulation experiments validate the effectiveness of our approach and some management insights are given.

Enterprise and service−level scheduling of robot production services in cloud manufacturing with deep reinforcement learning

Enterprise and service−level scheduling of robot production services in cloud manufacturing with deep reinforcement learning

A systematic framework for tackling anomalous pre-welding workpiece postures with regular butt joints based on prototype features

In intelligent welding systems, pre-welding parameter extraction is a foremost technology in upgrading robotic welding and integrating emerging technologies, e.g., digital twins, big data, and cloud manufacturing. However, current workpiece postures still rely on manual judgments based on workers' experience, which has become one of major issues hindering the further advancement of intelligent welding systems towards mass production. To cope with this issue, aiming at four typical regular butt joints, a systematic tackling framework is proposed and carried out from posture description, posture feature construction, posture metrics to visualization model reconstruction. At a core, the prototype feature is proposed to characterize pre-welding workpiece postures and a series of text characters based on it is introduced to describe various anomalous workpiece postures including concurrent tilt, misalignment, seam variation, and stacking between them. A comprehensive process for constructing prototype features is performed from data acquisition, image processing to key point search, among which the algorithms for extracting differential features of different seam types are integrated. Based on the constructed prototype features, several posture metric parameters are defined, and workpiece posture models can be easily reconstructed. In addition, the good generalizability of the proposed framework for seam types with regular edge and seam features is also discussed. Ultimately, experimental results show that the prototype feature-based posture description of the pre-welding workpiece can efficiently and accurately characterize multiple anomalous postures.

Fairness model considering satisfaction and preferences for service scheduling on electronic platforms in construction industry

Advancements of electronic platforms in construction industry makes it possible for construction service scheduling (CSS). However, fairness concerns in CSS, such as unfairness in scheduling solutions due to information asymmetry, remains inadequately addressed among existing studies. This study proposes a fairness scheduling model that considers the preferences of participants. According to real engineering practices, a multi-level CSS model with 12 objectives is established by thoroughly considering participants’ preferences, aiming to deliver equitable prices and CSS solutions. In solving the model, a clustering operation considering price fairness and inheritance operation considering participants’ preferences are designed for the developed evolutionary algorithm. Finally, a simulated case study derived from a real-world construction project demonstrates the effectiveness of the proposed model and algorithm. The results show the superior performance of the proposed model, as it recommends fairer prices and solutions to the CSS problem over the five comparator models. Meanwhile, the proposed algorithm offers more fair solutions with better comprehensive performance than the NSGA3, MOEA/D, MOEA/DD, and RVEA algorithms. This study can be extended to scheduling fairness issues in other fields (e.g., e-commerce, cloud manufacturing, and energy trading), while providing references for researches on service scheduling.

An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm

The smooth execution of tasks in the cloud manufacturing (CMfg) process can be significantly affected by various real-world uncertainties, resulting in inefficiencies or even failures of the composite manufacturing service (CMS). Nevertheless, current research primarily concentrates on post-exception emergency handling methods, heavily dependent on the planned CMS and real-time status of manufacturing resources, often leading to suboptimal temporary adjustments or even the inability to find a viable solution. Hence, there is an urgent necessity to enhance the robustness of the planned CMS during the service composition and optimal selection (SCOS) phase. In pursuit of this goal, the paper initially presents an adaptive robust service composition and optimization selection (ARSCOS) model. This model dynamically determines whether to await exception resolution or invoke an alternative service, considering the circumstances when a service exception arises and employing task delay time to characterize the robustness of a specific CMS. Subsequently, an enhanced multi-objective artificial hummingbird algorithm (EMOAHA) is devised for this model. Finally, the efficacy of EMOAHA is demonstrated by applying it to nine different scales of civilian unmanned aerial vehicle manufacturing SCOS problems and 17 benchmark functions. The results illustrate that the proposed EMOAHA exhibits superior convergence and diversity in comparison to the other 11 classical intelligent optimization algorithms.

Optimization for Storage Scheduling of Steel Plates Based on Cloud Manufacturing Platform

With the development of cloud manufacturing, the automation of storage scheduling becomes popular in the steel industry. However, the high customization of steel plates makes the storage scheduling too complex to be optimized. To overcome this challenge, we propose to utilize the big data of steel plate orders and warehouse status to optimize the storage scheduling of steel plates. The agglomerative hierarchical clustering is firstly adopted to reduce the complexity of excessive steel plate specifications, then an optimization problem is defined to formulate the storage scheduling of steel plates with safety. A two-stage heuristic (TSH) algorithm is proposed to solve the optimization problem with low complexity. In TSH, steel plates are firstly assigned to multiple stacks, and then the arrangement of each stack is determined. Experiments are executed based on a cloud manufacturing platform for steel plates production and storage, and the results prove the effectiveness of the proposed works.

Decentralised collaborative job reassignments in additive manufacturing

ABSTRACT Cloud Manufacturing (CMfg) is a promising approach that leverages the sharing economy to reduce costs and enhance supply chain flexibility. Particularly, when utilised alongside Additive Manufacturing (AM), CMfg is considered a key enabler for collaborative production (CP) systems. However, there is still a lack of planning models that reduce entry barriers for CP. Therefore, we propose a decentralised CP planning framework for AM. In our approach, machines autonomously select jobs from an existing production plan to forward them to other suppliers that can produce these parts more efficiently. A CMfg platform facilitates job forwarding and creates promising part bundles and manufacturing machines autonomously places bids on the packages via a combinatorial 2 nd price reverse auction. Costs of the reallocated bundles are shared throughout a Shapley value-based approach without the need to disclose critical information. We benchmark our proposed framework against a centralised planning approach and find that it achieves comparable effectiveness as the benchmark solution. We also show that this mechanism promotes individual rationality and that agents particularly benefit when participating in both offering and acquiring production jobs through the auction.

Resource Scheduling Method for Equipment Maintenance Based on Dynamic Pricing Model in Cloud Manufacturing

Cloud manufacturing, as a novel service mode in the manufacturing field with the features of flexible resource assignment, timely service, and quantity-based pricing, has attracted extensive attention in recent years. The cloud manufacturing industry uses a significant amount of smart equipment. In this context, equipment maintenance resource scheduling (EMRS) is an important subject that needs to be studied. Cloud manufacturing platforms must provide effective services for equipment maintenance in a timely manner. In order to improve the efficiency of cloud manufacturing platforms and meet the needs of users, an effective EMRS scheme is required. In this paper, we propose a dynamic resource allocation model for cloud manufacturing to meet the needs of users and maximize the benefit of a cloud manufacturing platform. The model takes into account the needs of users and the benefits of a cloud production platform. The contributions of this paper are divided into the following three aspects. First, the E-CARGO model using role-based collaboration theory is introduced to formally model EMRS activities, forming a solvable optimization model. Second, a dynamic pricing model with a center symmetric curve is designed to realize the flexible conversion between time, cost, and price. Third, the concept of satisfaction in fuzzy mathematics is introduced, in order to meet the different needs of users and platforms, in terms of time, price, and cost, while ensuring service quality and the platform’s benefits. Finally, an improved genetic algorithm is used to solve the cloud manufacturing resource scheduling problem, and good experimental results are obtained. These results demonstrate that the proposed dynamic pricing model is reasonable, and the allocation scheme obtained through a genetic algorithm is feasible and effective.

Provenance blockchain for ensuring IT security in cloud manufacturing

Provenance blockchain is an evolving concept for protection of production, logistics, and supply chain networks from rogue Industrial Internet of Things (IIoT) devices. Such rogue IIoT devices are a recognized threat in the cloud manufacturing networks. In extreme cases, they can be used to cause industrial accidents. A brief version of provenance is about end-to-end tracking and tracing of data and the nodes involved in creating, modifying, transmitting, storing, and deleting it at specific times and locations. It provides an end-to-end verifiable and controlled computation for ensuring trustworthiness, quality, reliability, and validity of data. Provenance has existed in computing using logging software systems. This research is focused on threats to food supply chains between two countries. A scenario for protecting food supply chain from India to UAE has been modeled. This research recognized the threat of harmful food items getting mixed with flow of genuine products in a supply chain. The IIoT devices used to control the flow can be authenticated using the evolving provenance blockchain technology. With the help of recent design recommendations in the literature, a model design has been created and simulated in this research. Observations from the simulation revealed that TCP congestions and unpredictable turnaround time for assigning cryptographic keys to IIoT device sessions may have to be explored in future. A collaborative design between the two nations has been proposed. All IIoT devices not supporting cryptography will be eliminated from the cloud manufacturing and supply chain networks. Currently, this design may be used for one time registration only. Future studies may provide improved versions in which, repeated authentication and keys replacements may be implemented.

Improved Chimp Optimization Algorithm for Matching Combinations of Machine Tool Supply and Demand in Cloud Manufacturing

Cloud manufacturing is a current trend in traditional manufacturing enterprises. In this environment, manufacturing resources and manufacturing capabilities are allocated to corresponding services through appropriate scheduling, while research on the production shop floor focuses on realizing a basic cloud manufacturing model. However, the complexity and diversity of tasks in the shop floor supply and demand matching environment can lead to difficulties in finding the optimal solution within a reasonable time period. To address this problem, a basic model for dynamic scheduling and allocation of workshop production resources in a cloud-oriented environment is established, and an improved Chimp optimization algorithm is proposed. To ensure the accuracy of the solution, two key improvements to the ChOA are proposed to solve the problem of efficient and accurate matching combinations of tasks and resources in the cloud manufacturing environment. The experimental results verify the effectiveness and feasibility of the improved ChOA (SDChOA) using a comparative study with various algorithms and show that it can solve the workshop supply and demand matching combination problem and obtain the optimal solution quickly.

Scheduling tasks with different structures and arrival times in cloud manufacturing systems by considering combined logistics

Scheduling tasks with different structures and arrival times in cloud manufacturing systems by considering combined logistics

Discrete-time state delayed systems with saturation arithmetic: overflow oscillation-free realization

ABSTRACT In many smart systems, such as cloud computing, networked control, manufacturing, and telecommunication, the effects of time delays are unavoidable. Saturation nonlinearity is common in many smart systems (e.g. electric systems with limited actuator power supplies, mechanical systems with position and speed constraints, fixed-point digital filters, etc.). Therefore, the study of stability of discrete-time systems (DTSs) with time delay and saturation is of high importance in practice. The global asymptotic stability (GAS) problem of such systems is investigated in this paper. The saturation nonlinearity is constrained by a convex hull with a free matrix whose infinity norm is smaller than or equal to unity. A new Lyapunov-based GAS criterion for DTSs with time-varying delay and state saturation is established. The GAS problem for DTSs with constant delay and saturation is also discussed. The obtained results can be used to ensure the absence of overflow oscillations in the considered system. In comparison to several existing criteria, the approach yields improved stability results. An example is provided to demonstrate the importance of the presented results.

Column generation for service assignment in cloud-based manufacturing

Cloud-based manufacturing has gained significant attention in academia and industry, presenting opportunities for enhanced operational efficiency. This paper addresses two critical challenges in cloud manufacturing: service assignment and transportation within hybrid hub-and-spoke networks. We propose a novel approach to minimize the total cost, encompassing manufacturing and transportation costs, through a mixed integer programming model and a column generation-based algorithm. Numerical experiments are conducted to validate the efficacy of the proposed model and algorithm, considering different system sizes. The results demonstrate the tangible benefits of our approach. By introducing a hybrid hub-and-spoke network, we achieve a substantial average reduction of 7.46% in the total cost of the cloud manufacturing system. Furthermore, our proposed algorithm outperforms existing methods such as Particle Swarm Optimization (PSO) and CPLEX. Particularly, in large-scale instances where CPLEX fails to solve, our algorithm outperforms PSO by an additional 3.43% in terms of cost reduction. Moreover, sensitivity analysis provides valuable insights for cloud manufacturing, contributing to its effective implementation.

Research on cloud manufacturing service recommendation based on graph neural network.

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes' vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.

An improved deep reinforcement learning-based scheduling approach for dynamic task scheduling in cloud manufacturing

ABSTRACT Dynamic task scheduling problem in cloud manufacturing (CMfg) is always challenging because of changing manufacturing requirements and services. To make instant decisions for task requirements, deep reinforcement learning-based (DRL-based) methods have been broadly applied to learn the scheduling policies of service providers. However, the current DRL-based scheduling methods struggle to fine-tune a pre-trained policy effectively. The resulting training from scratch takes more time and may easily overfit the environment. Additionally, most DRL-based methods with uneven action distribution and inefficient output masks largely reduce the training efficiency, thus degrading the solution quality. To this end, this paper proposes an improved DRL-based approach for dynamic task scheduling in CMfg. First, the paper uncovers the causes behind the inadequate fine-tuning ability and low training efficiency observed in existing DRL-based scheduling methods. Subsequently, a novel approach is proposed to address these issues by updating the scheduling policy while considering the distribution distance between the pre-training dataset and the in-training policy. Uncertainty weights are introduced to the loss function, and the output mask is extended to the updating procedures. Numerical experiments on thirty actual scheduling instances validate that the solution quality and generalization of the proposed approach surpass other DRL-based methods at most by 32.8% and 28.6%, respectively. Additionally, our method can effectively fine-tune a pre-trained scheduling policy, resulting in an average reward increase of up to 23.8%.