Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds

With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds (MCs) can provide a much wider range of choices of services with their real-time status. However, most researchers have neglected the superiority of multiple MCs and failed to make a study of how to utilize the abundant and diverse resources of multiple MCs, let alone the multi-MCs service mode under dynamic environment. Therefore, we first propose a new dynamic SS paradigm that can leverage the abundant services from multiple MCs, real-time sensing ability of the Internet of Things (IoT) and big data analytics technology for knowledge and insights. In this way, providing optimal manufacturing services (with high QoS) for customers can be guaranteed under dynamic environments. In addition, considering that a relatively long time might be spent to complete a complex manufacturing task after SS, a quantified approach, based on the Analytic Hierarchy Process and big data, is proposed to evaluate whether the intended cloud manufacturing services should be reserved to make sure that eligible services are ready to use without compromising cost or time. In this paper, the problem of IoT-enabled dynamic SS across multiple MCs is formulated in detail to enable an event-driven adaptive scheduling when the model is faced with three kinds of uncertainties (of the service market, service execution and the user side respectively). Experiments with different settings are also performed, which show the advantages of our proposed paradigm and optimization model.     

Integrating crowd-/service-sourcing into digital twin for advanced manufacturing service innovation

In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility.     

网络化制造领域本体知识建模的研究

针对目前网络化制造环境下,服务描述、发现过程中语义信息动态表达不足的问题,从Web服务的角度提出了一个基于本体的制造领域知识建模方法,并在此基础上构建制造领域本体模型,最后给出应用实例。重点研究解决了制造服务过程中不同对象间的信息交流、数据交换和知识资源重用三个问题,有效地实现网络制造环境下资源共享与优化配置,为基于语义的网络协同制造服务平台的构建奠定了基础。     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

Logistics service scheduling with manufacturing provider selection in cloud manufacturing

In service-oriented manufacturing models, manufacturing resources in different enterprises are integrated and shared through network, cloud platforms, and logistics. On cloud manufacturing platforms, service providers offer on-demand manufacturing services to service demanders according to supply-demand matching results. As a special type of manufacturing services, logistics services provide transportation capabilities for production services and demanders. It is a critical issue to schedule logistics services efficiently, especially when manufacturer selections have been planned. This research focuses on the logistics scheduling problem in cloud manufacturing with pre-selected manufacturers. We analyze this optimization problem from aspects of tasks, production services, logistics services, and optimization objectives. Then a logistics scheduling method is proposed to reduce the average delivery time from manufacturers to customers. In the proposed method, the total time from start points of logistics to demanders is considered to reduce the average delivery time of all tasks. Based on four different scenarios, we build their scheduling models and run simulations to verify the effectiveness of the proposed method. Results show that the average task delivery time of the proposed method is shorter than three typical strategies.     

Holon制造系统的遗传蚁群算法求解

在市场环境的不确定性、市场竞争加剧的背景下,要求企业之间的远程服务紧密联系。因此,高可靠性的远程服务动态优化协调成为现代企业发展先进智能制造系统所要解决的重要问题。为此,将Holon理论引入到企业远程服务配置中,运用遗传蚁群混合算法的运用来提高Holon制造系统的健壮性,解决跨地域企业之间远程服务的配置和调度过程中的协商问题。     

Workload-based multi-task scheduling in cloud manufacturing

Cloud manufacturing is an emerging service-oriented business model that integrates distributed manufacturing resources, transforms them into manufacturing services, and manages the services centrally. Cloud manufacturing allows multiple users to request services at the same time by submitting their requirement tasks to a cloud manufacturing platform. The centralized management and operation of manufacturing services enable cloud manufacturing to deal with multiple manufacturing tasks in parallel. An important issue with cloud manufacturing is therefore how to optimally schedule multiple manufacturing tasks to achieve better performance of a cloud manufacturing system. Task workload provides an important basis for task scheduling in cloud manufacturing. Based on this idea, we present a cloud manufacturing multi-task scheduling model that incorporates task workload modelling and a number of other essential ingredients regarding services such as service efficiency coefficient and service quantity. Then we investigate the effects of different workload-based task scheduling methods on system performance such as total completion time and service utilization. Scenarios with or without time constraints are separately investigated in detail. Results from simulation experiments indicate that scheduling larger workload tasks with a higher priority can shorten the makespan and increase service utilization without decreasing task fulfilment quality when there is no time constraint. When time constraint is involved, the above strategy enables more tasks to be successfully fulfilled within the time constraint, and task fulfilment quality also does not deteriorate.     

基于业务流程的制造云服务组合模型

为了提高云制造系统中制造云服务的组合成功率,实现组合云服务与用户业务需求的准确匹配,在对制造云服务、流程节点任务、云服务的可组合性和流程匹配进行形式化描述的基础上,提出一种基于业务流程的制造云服务组合模型。该模型由业务流程引擎、业务流程、选择逻辑、评估逻辑、监控逻辑、知识库和原子云服务集构成,在功能匹配的基础上,对候选服务的可组合性进行检查,结合负载、服务质量(QoS)和业务流程信息,选择合适的云服务,并将其挂接在业务流程上实现制造云服务的组合。对制造云服务的组合流程进行了详细描述,并给出云服务组合的实现方法。实例分析表明,该模型能够有效地选择满足业务需求的云服务实体并进行组合,从而提高制造云服务的组合成功率,保障用户制造活动的顺利进行。     

面向网络化制造服务的本体知识建模研究*

针对目前网络化制造环境下,服务描述、发现过程中语义信息动态表达不足的问题,从Web服务的角度提出了一个基于本体的制造服务知识建模方法,并在此基础上构建制造本体模型,最后给出服务模糊匹配的评定方法。重点研究解决了制造服务过程中不同对象间的信息交流、数据交换和知识资源重用三个问题,有效地实现网络制造环境下资源共享与优化配置,为语义Web的网络协同制造服务平台的构建奠定了基础。     

制造服务逻辑关系确定方法研究

Web服务环境下,为了快速建立制造服务链,提出了一种制造服务逻辑关系的确定方法。首先分析了产品结构、制造任务和制造服务三者之间的关系,提出了基于产品结构建立制造服务链的过程,然后论述了制造任务时序关系的确定方法,定义了基于产品结构的制造服务分类模型,最后给出了制造服务逻辑关系的确定规则。     

Stable matching of customers and manufacturers for sharing economy of additive manufacturing

With rapid advances in internet and computing technologies, sharing economy paves a new way for people to “share” assets and services with others that disrupts traditional business models across the world. Specifically, rapid growth of additive manufacturing (AM) enables individuals and small manufacturers to own machines and share under-utilized resources with others. Such a decentralized market calls upon the development of new analytical methods and tools to help customers and manufacturers find each other and further shorten the AM supply chain. This paper presents a bipartite matching framework to model the resource allocation among customers and manufacturers and leverage the stable matching algorithm to optimize matches between customers and AM providers. We perform a comparison study with Mix Integer Linear Programming (MILP) optimization as well as the first-come-first-serve (FCFS) allocation strategy for different scenarios of demand-supply configurations (i.e., from 50% to 500%) and system complexities (i.e., uniform parts and manufacturers, heterogeneous parts and uniform manufacturers, heterogeneous parts and manufacturers). Experimental results show that the proposed framework has strong potentials to optimize resource allocation in the AM sharing economy.     

Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation

Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just “old wine in new bottles”? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense.     

Service interaction design: A Hawk-Dove game based approach to managing customer expectations for oligopoly service providers

In the “experience economy”, effectively delivering memorable and exciting customer experiences has become a key issue for service providers. Service experience delivery involves service encounters through which interactions between service providers and customers can be shaped into interactive artifacts managing customer expectations and dynamically delivering suitable services. Service interaction design aims to optimize customer interactions with services to match customer expectations and yield satisfactory service experiences. On the other hand, service providers typically make profits and cost the priority, despite knowing that high service quality can maximize satisfaction, particularly in markets served by an oligopoly, resulting in customers only accepting existing limited-value services. Hence, the oligopoly market can be regarded as a value-bounded context. Additionally, understanding customer expectations regarding a wide range of interactions is crucial to service providers selecting and designing services that match customer expectations. Therefore, this paper presents a service interaction design mechanism to help oligopoly service providers systematically and effectively manage customer expectations in dynamic interactions, even in value-bounded contexts. The proposed mechanism models this service interaction design problem as a series of Hawk-Dove games that approach an evolutionary stable state. The evaluation results suggest that oligopoly service providers should change their mindsets and design service interactions to manage customer expectations associated with service delivery, not only to ensure high satisfaction and profit but also to engage customers in co-creating value.     

云制造环境下基于双层规划的资源优化配置模型

为了解决云制造环境下制造资源的优化配置问题,综合考虑需求与服务双方以及云平台运营方的利益,提出了一种基于双层规划的资源优化配置模型。该模型以前景理论结合多约束多属性评价体系求解出的供需双方满意度作为上层规划的优化目标;以云平台资源利用率最大化为下层规划的优化目标;通过双层规划并采用改进的i-NSGA-II-JG算法对多目标制造资源配置问题进行求解。最后,通过算例仿真实验证明了该模型的可行性和优越性。     

Evolutionary game-based incentive models for sustainable trust enhancement in a blockchained shared manufacturing network

Shared manufacturing (SM) is an advanced manufacturing mode to solve the problems of information asymmetry and resource imbalance in the complex supply and demand relationship under the background of mass personalization. Blockchain can provide technical solutions for trust issues between manufacturing service providers and demanders, but less research has focused on the stable maintenance of a blockchained shared manufacturing network (BSMN). Industrial knowledge graph and cognitive intelligence play an important role in designing incentive mechanisms for BSMN. Based on the evolutionary game theory, this paper designs various incentive models and develops the corresponding smart contracts to encourage different enterprises to participate in the “accounting” operations, to enhance the trust and maintain the operational stability of BSMN. Firstly, the evolution mechanism of BSMN was analyzed. Then, an evolutionary game model between shared manufacturing enterprise nodes was established to deduce the stable state of BSMN. Based on the analysis of the evolutionarily stable state, an incentive model with three strategies to encourage different enterprise nodes to participate in “accounting” operations was determined, and smart contracts of the incentive models were developed. Finally, the effectiveness of the proposed incentive models was verified by simulation experiments.     

Decentralized cloud manufacturing-as-a-service (CMaaS) platform architecture with configurable digital assets

Contemporary Cloud Manufacturing-as-a-Service (CMaaS) platforms now promise customers instant pricing and access to a large capacity of manufacturing nodes. However, many of the CMaaS platforms are centralized with data flowing through an intermediary agent connecting clients with service providers. This paper reports the design, implementation and validation of middleware software architectures which aim to directly connect client users with manufacturing service providers while improving transparency, data integrity, data provenance and retaining data ownership to its creators. In the first middleware, clients have the ability to directly customize and configure parts parametrically, leading to an instant generation of downstream manufacturing process plan codes. In the second middleware, clients can track the data provenance generated in a blockchain based decentralized architecture across a manufacturing system. The design of digital assets across a distributed manufacturing system infrastructure controlled by autonomous smart contracts through Ethereum based ERC-721 non-fungible tokens is proposed to enable communication and collaboration across decentralized CMaaS platform architectures. The performance of the smart contracts was evaluated on three different global Ethereum blockchain test networks with the centrality and dispersion statistics on their performance provided as a reference benchmark for future smart contract implementations.     

On the Simulation of Grid Market Coordination Approaches

Grid computing has recently become an important paradigm for managing computationally demanding applications, composed of a collection of services. The dynamic discovery of services, and the selection of a particular service instance providing the best value out of the discovered alternatives, poses a complex multi-attribute n:m allocation decision problem, which is often solved using a central resource broker. However, decentralized approaches to this service allocation problem represent a much more flexible alternative, thus promising improvements in the efficiency of the resulting negotiations and service allocations. This paper compares centralized and decentralized service allocation mechanisms in Grid market scenarios according to a defined set of metrics.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

基于LG-MMAS算法的制造云服务优化组合研究

制造云服务组合是一种提高云制造资源利用率,实现制造资源增值的新技术,对云制造产业的快速发展具有重要的支撑作用。随着云制造技术的日益成熟,网络上出现了大量具有相同制造功能和不同服务质量的制造云服务,如何通过这些制造云服务构建出既能满足用户制造需求,又具有最优服务质量的组合服务是云制造领域面临的难题。针对这一问题,将协作学习、变异和精英保留机制引入最大最小蚁群算法,构造了具有学习和变异能力的最大最小蚁群算法,并使用该算法求解服务质量感知的制造云服务优化组合问题。仿真实验结果验证了算法的有效性。     

An agent-based framework for collaborative negotiation in the global manufacturing supply chain network

This paper applies the multi-agent system paradigm to collaborative negotiation in a global manufacturing supply chain network. Multi-agent computational environments are suitable for dealing with a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving agents. An agent-based multi-contract negotiation system is proposed for global manufacturing supply chain coordination. Also reported is a case study of mobile phone global manufacturing supply chain management.