Dongming Zhao

Resource Service Composition and Its Optimal-Selection Based on Particle Swarm Optimization in Manufacturing Grid System

In distributed manufacturing systems, especially in a manufacturing grid (MGrid) system, there are primarily two kinds of manufacturing tasks (or resource service requests): (1) single resource service request task (SRSRTask), which can be completed by invoking only one resource service, and (2) multi-resource service request task (MRSRTask), which is completed by invoking several resource services in a certain sequence. For an SRSRTask, the system searches the resource services that are qualified for its function requirements and chooses the optimal one to execute it. For an MRSRTask, in addition to the search for all qualified resource services according to each subtask, the system selects one candidate resource service for each subtask. Then the system generates a new composite resource service (CRS) and selects the optimal resource service composite path from all possible paths to execute the task with the given multi-objective (e.g., time minimization, cost minimization, and reliability maximization) and constraints. The above problem is defined as multi-objective MGrid resource service composition and optimal-selection (MO-MRSCOS) problem in this paper. The formulation is presented for an MO-MRSCOS problem to minimize execution time and cost, and maximize the reliability. The basic resource service composite modes (RSCM) for CRS are described, and the principles for translating a complicated RSCM into a simple sequence RSCM are presented for simplifying the resolving process and complexity of MO-MRSCOS. A new MGrid resource service composition and optimal-selection method, based on the principles of particle swarm optimization (PSO), is then proposed. The PSO follows a collaborative population-based search, which models based on the social behavior of bird flocking and fish schooling. The case study demonstrates that the proposed method is useful in solving MO-MRSCOS problems. The experimental results and performance comparison show that the proposed method is both effective and efficient.

Correlation-aware resource service composition and optimal-selection in manufacturing grid

For a multi-resource service request task (MRSRTask) in manufacturing grid (MGrid) system, in addition to the search for all qualified resource services according to each subtask, the system selects one candidate resource service for each subtask. Then the system generates a new composite resource service (CRS) and selects the optimal resource service composite path from all possible paths to execute the task with the given multi-objective (e.g., time minimization, cost minimization and reliability maximization) and multi-constraints. The above problem is defined as multi-objective MGrid resource service composition and optimal-selection (MO-MRSCOS) problem. The formulation is presented for an MO-MRSCOS problem. The correlations among resource services are taken into account during MGrid resource service composition, and a QoS description mode supporting resource service correlation is presented. The basic resource service composite modes (RSCM) for CRS are described, and the principles for translating a complicated RSCM into a simple sequence RSCM are presented for simplifying the resolving process and complexity of MO-MRSCOS problem. A new method based on the principles of particle swarm optimization (PSO), is proposed for solving MO-MRSCOS problem. Unlike previous works: (a) the proposed PSO algorithms combine the non-dominated sorting technique to achieve the selection of global best position and private best position; (b) the parameters of particle updating formulation in PSO are dynamical generated in order to make a compromise between the global exploration and local exploitation abilities of PSO; (c) permutation-based and objective-based population trimming operators are applied in PSO to maintain diversity of solutions in population. The experimental results and performance comparison show that the proposed method is both effective and efficient.

Research on manufacturing grid resource service optimal-selection and composition framework

In order to address the resource service optimal-selection (RSOS) and composition problem in manufacturing grid (MGrid) system and provide high-quality service to users, an MGrid RSOS and composition framework (MGrid-RSOSCF) is investigated in this study. The process of RSOS and composition is divided into the following five steps in MGrid-RSOSCF: (1) decomposing the submitted manufacturing task into several subtasks (i.e. single resource service requested task) if the submitted task is a multiple resource service requested task; (2) searching out the qualified resource service for each decomposed subtask and generating the corresponding candidate resource service set; (3) retrieving, evaluating and comparing the quality of service (QoS) for each candidate resource service, and provide data for service optimal-selection and composition –if the submitted task is a single resource service requested task; (4) evaluating synthetically the overall quality of each candidate resource service and ranking them, and selecting the optimal one for the task – if the submitted manufacturing task is an multiple resource service requested task; (5) selecting one candidate resource service from each candidate resource service set and constructing a new composite resource service according to the submitted task requirements, and collecting all the possible resource service composite execution paths (RSCEP) and selecting the optimal paths to execute the task. The proposed MGrid-RSOSCF consists of five layers and each layer provides the corresponding necessary services and algorithms to address one problem mentioned above. The five layers are: (1) T-layer, responsible for MGrid task decomposition; (2) S-layer, responsible for resource service match and search; (3) Q-layer, responsible for QoS processing; (4) O-layer, responsible for evaluating and ranking the candidate resource service and (5) C-layer is responsible for resource service composition and optimal-selection. The case study and comparison of performances of the algorithms demonstrate that the proposed methods are sound on success rate and executing efficiency.

Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system

In manufacturing grid (MGrid) system, according to functional requirements of a task, there exist a lot of resource services which have similar functional characteristics. Multiple resource services with similar functional characteristics raise the concern over resource service optimal-selection (RSOS). It is important to select the optimal resource service according to their non-functionality characteristics or quality of service (QoS). However, QoS attributes are not easy to measure due to their complexity and involvement of ill-structured information. In this study, user’s feeling is taken into account in RSOS in an MGrid system. The non-functionality QoS evaluation of resource services is based on users’ feeling and transaction experiences using intuitionistic fuzzy set (IFS). Furthermore, the dynamics of non-functionality QoS is considered, and a time-decay function is introduced into non-functionality QoS evaluation. A new method is proposed for RSOS based on IFS and non-functionality QoS, and the procedures are presented in detail. A practice case study is used to illustrate the proposed method and procedure. The performance and advantage of the proposed method are discussed.

Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system

In order to realize the manufacturing resource sharing and optimal allocation, many advanced manufacturing modes and technologies have been proposed and researched, but few works have been emphasized on resource service and capability transaction and scheduling management. With these conditions, in this article, the concept of cloud manufacturing mode is first briefly introduced, and a general cloud manufacturing resource service scheduling model is established. Then, the comprehensive utility models, which consider energy consumption, cost, and risk for the three sides (i.e. provider, consumer, and operator), are established in the resource service scheduling process in cloud manufacturing system. Four kinds of resource service scheduling modes, which are provider centered, consumer centered, operator centered, and system centered, are studied. These modes are based on the utility models that are investigated, as well as the system-centered cooperative scheduling method that is designed with risk-sharing strategy. The experimental results show that the system-centered scheduling method has the highest potential for realizing the aim of cloud manufacturing, and the system-centered cooperative scheduling method is rational to obtain the higher maximal utilities of the whole system and the three key users at the same time. It promotes the higher efficiency and utility, a higher rate of sharing, green and on-demand use of decentralized manufacturing resources and capability services in cloud manufacturing systems.

An approach to manufacturing grid resource service scheduling based on trust-QoS

Manufacturing grid (MGrid) aims to solve the problem of distributed and heterogeneous manufacturing resource sharing and collaborative operation. Compared with traditional scheduling methods in distributed computing system such as computing grid, which emphasises execution time, the resource service scheduling in MGrid emphasises the manufacturing ability sharing, and requires higher resource service ability and reliability, therefore demands higher trust in MGrid resource service scheduling. In this paper, the measurement and evaluation models of trust-QoS in MGrid are proposed. A trust-QoS based MGrid resource service scheduling framework is proposed, followed by the scheduling algorithms. Simulation and algorithms performance comparison show that the proposed methods are sound on success rate and execution efficiency.

Utility modelling, equilibrium, and coordination of resource service transaction in service-oriented manufacturing system

The resource service transaction in service-oriented manufacturing systems is one of the key issues in deciding its practical implementation and application. In order to enhance the benefit of the three sides or users (i.e. resource service provider, resource service demander, and resource service agent or operator (agent)) using service-oriented manufacturing systems, the comprehensive utility models consider the revenue, time, and reliability for the three sides in the resource service-transaction process faced with uncertain factors under decentralized decision-making conditions are established first. Then the related comprehensive utility model for the resource service transaction chain of ‘multiple resource service providers – one agent – multiple resource service demanders’ under a centralized decision-making condition is proposed and discussed. The existence of utility equilibrium between resource service provider and agent is studied and proved. As is the optimal decision of a resource service transaction chain under the decentralized decision-making condition, as well as the utility equilibrium between agent and resource service demander. A utility coordination method is proposed, with the aim of improving the comprehensive utility of the resource service transaction chain under a decentralized decision-making condition. The case study illustrates that the proposed approach is practicable and effective.

Study on the utility model and utility equilibrium of resource service transaction in cloud manufacturing

As a new service-oriented network manufacturing model, cloud manufacturing (CMfg) can accelerate the transformation from production-oriented to service-oriented manufacturing. Study of resource service transaction (RST) is one of the key issues that deciding the practical application of CMfg. In this study, the comprehensive utility models considered the revenue, time, and reliability for the three sides, i.e., resource service provider (RSP), resource service demander (RSD), and resource service agent (Agent) in the RST process in CMfg under the decentralized decision-making condition are first established, respectively. Then the comprehensive utility model for the RST chain (RSTC) of “multiple RSP-one Agent-multiple RSD” under the centralized decision-making condition is proposed and discussed. The existence of utility equilibriums between RSP and Agent, Agent and RSD are proved in the optimal decision of the RSTC under the decentralized decision-making condition.

Study of failure detection and recovery in manufacturing grid resource service scheduling

A Manufacturing Grid (MGrid) system is different from a conventional distributed computing system due to its focus on larger-scale, distributed and heterogeneous manufacturing resource and service sharing, including equipment resources, application system resources, material resources, technical resources, public service resources, etc., where remote resource service scheduling (e.g., remote accessing, information communication) has a large influence on the MGrid quality of service (QoS). The probability of failure is higher in MGrid resource service scheduling and failures affect task execution and the quality of service of the MGrid fatally. Therefore, this paper focuses on failures in the MGrid resource service scheduling process. The potential failures that can occur during MGrid resource service scheduling are investigated. Thirteen failures are first defined in detail and are classified into four categories: (a) virtual-link-related failures, (b) resource-service-related failures, (c) task-related failures, and (d) application-related failures. A failure management system of the MGrid system is presented associated with its architecture. Corresponding detection mechanisms and methods for each defined failure are presented in detail, as well as the corresponding failure recovery methods. The implementation and simulation results indicate that our approaches are sound for promoting a successful scheduling rate and shortening the total execution time of the MGrid resource service.

Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things

ABSTRACT After investigation on the existing advanced manufacturing systems (AMSs), it is found that supply–demand matching of manufacturing resource is one of the common issues to be addressed in all AMSs, and methods for addressing this issue have evolved from P2P (peer-to-peer)-based, to information centre-based, and to platform (or system)-based matching, and are moving towards socialisation and service-based solutions. In order to adapt to this trend, a new method for manufacturing resource supply–demand matching based on complex networks and Internet of Things (IoT) is proposed, and a four-layered architecture for implementing this method is designed. In this method, IoT technology is employed to realise the intelligent perception and accessing of various manufacturing resources and capabilities (MR&C), which enables logical aggregation of various distributed MR&C in the form of services. Then complex networks model and theory are used to realise the efficient manufacturing service management, optimal-allocation, and supply–demand matching. In this article, the specific key technologies for implementing the method are presented, including key technologies for manufacturing service generation and aggregation, manufacturing demand/task management, supply–demand matching of MR&C in the form of services, and value/utility adding based on manufacturing service network (MSN), manufacturing task network (MTN) and manufacturing enterprises collaborative network (ECN).