Dechen Zhan

Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.

Scheduling Methodology for Production Services in Cloud Manufacturing

Cloud Manufacturing (CMfg) is the delivery of resources control and abilities for manufacturing through the internet. It offers a personalized manufacturing service through several processes (e.g. the order decomposition into tasks, the selection of one or several providers to perform them, the scheduling of the whole manufacturing process). The current paper aims to provide an optimized methodology for task scheduling. In a first time, it identifies the background and general concept of CMfg, focuses further on the scheduling challenges and the environmental data and constraints to propose an optimized methodology for the scheduling process.

COLLABORATIVE PLANNING IN SUPPLY CHAINS BY LAGRANGIAN RELAXATION AND GENETIC ALGORITHMS

A collaborative planning framework combining the Lagrangian relaxation method and genetic algorithms is developed to coordinate and optimize the production planning of the independent partners linked by material flows in multiple tier supply chains. Linking constraints and dependent demand constraints were added to the monolithic multi-level, multi-item capacitated lot sizing problem (MLCLSP) for supply chains. Model MLCLSP was Lagrangian relaxed and decomposed into facility-separable sub-problems. Genetic algorithms was incorporated into Lagrangian relaxation method to update Lagrangian multipliers, which coordinated decentralized decisions of the facilities in supply chains. Production planning of independent partners could be appropriately coordinated and optimized by this framework without intruding their decision authorities and private information. This collaborative planning scheme was applied to a large set problem in supply chain production planning. Experimental results show that the proposed coordination mechanism and procedure come close to optimal results as obtained by central coordination in terms of both performance and robustness

Digital enterprise management in China: current status and future development

Digital management plays an important role in modern enterprise management. Supported by the Chinese National High-Tech R&D Program on CIMS, the techniques and software of enterprise information systems and digital management systems have been studied and applied in China for more than 20 years. The paper provides a comprehensive review on the development, current status, and future development of enterprise information systems and digital management systems in China, as well as the software products and the market for digital management.

GRAI-ICE Model Driven Interoperability Architecture for Developing Interoperable ESA

This paper presents GRAI-ICE Model Driven Interoperability Architecture (MDI) which is developed based on MDA (Model Driven Architecture) of OMG and some initial works performed in INTEROP NoE. This MDI architecture aims at supporting the development of changeable on-demand and interoperable ESA (Enterprise Software Application). The architecture defined five modelling levels, i.e., Top CIM, Bottom CIM, Object oriented PIM, Pattern oriented PSM, and Component and configuration oriented CODE. This paper presents in detail core concepts and rational of each modeling level. An application example in nuclear equipment industry is outlined.

An IoT Service Framework for Smart Home: Case Study on HEM

Nowadays, most of smart home applications are either ad hoc or close/monolithic. So it is critical to study novel software architecture/frameworks which are open, connecting multiple heterogonous devices and multiple networks, supporting multiple concurrent applications, enabling reliable data/command delivery among home gateway and cloud/smart phone, and easy to develop third party service business applications. To achieve the above demand, this paper proposes a service framework for smart home which well combines Lab of Things (HomeOS), MQTT and Azure cloud. To validate this service frameworks rationality and combine high energy consumption of the whole society, based on this service framework, we developed a smart home energy management service system, which includes two sub-systems, i.e. Home energy management application running on home gateway, and energy service system for multi-homes running on Azure cloud and operated by specialized 3rd party energy service provider.

A feature-oriented approach to platform-specific modelling of coarse-grained components

Coarse-grained-component-based software development improves efficiency when developing large-scale software for enterprises. However, there are no rigorous models to describe the component because of its structural complexity. Aiming to obtain a correct and formal model, this paper presents a feature-based platform-specific modelling approach for coarse-grained components. The component is described by the Feature Relationship Tree (FRT) from the perspectives of function and implementation. By establishing mapping between the feature model and the PSM, the meta-PSM of the component is defined on the basis of typical patterns. A practical case is described to validate the method in this paper.

Research framework for decentralized multi-project scheduling problem

Multi-project environment is quite common in modern day, and the decentralized multi-project scheduling problem has been a hot research direction since last decade. Compared with centralized multi-project problem, decentralized multi-project problem has new characteristics in these fields such as decision maker, decision mode, and decision objective. Existing research works most focus on one particular issue of the problem, and has not formed a generally accepted definition and classification for the problem. In this paper, we propose the definition of decentralized multi-project scheduling problem and analyze the characteristics, and then propose the research framework based on the analysis. We map and locate some existing works in this research framework. At last give a short conclusion.

A Novel Genetic Simulated Annealing Algorithm for the Resource-Constrained Project Scheduling Problem

A novel hybrid meta-heuristic algorithm, entitled as RCPSPGSA, is proposed for solving the resource-constrained project scheduling problem (RCPSP) in this paper. The algorithm incorporates the simulated annealing algorithm (SA) into genetic algorithm in order to improve local searching performance and boost up evolution capability. In each evolution iteration GA generates a new temporary population, and after that SA is used for improving every individual in it and at the mean time the next gap population is generated. For the sake of keeping the same convergence direction and speed of GA and SA, the cooling procedure occurs at the end of each evolution iteration. Simulation experiments are performed on the standard project instance sets of PSPLIB, and orthogonal experiment method is introduced to solve the parameter selection problem. Parameter combinations selected by this method are proved to be outperformed. Experimental results show that RCPSPGSA improves solution quality for J30, J60, J90 sets and not bad for J120.

Coevolutionary Computation Based Iterative Multi-Attribute Auctions

Multi-attribute auctions extend traditional auction settings. In addition to price, multi-attribute auctions allow negotiation over non-price attributes such as quality, terms-ofdelivery, and promise to improve market efficiency. Multi-attribute auctions are central to B2B markets, enterprise procurement activity and negotiation in multi-agent system. A novel iterative multi-attribute auction mechanism for reverse auction settings with one buyer and many sellers is proposed based on competitive equilibrium. The auctions support incremental preference elicitation and revelation for both the buyer and the sellers. Coevolutionary computation method is incorporated into the mechanism to support economic learning and strategies for the sellers. The myopic best-response strategy provided by it is in equilibrium for sellers assuming a truthful buyer strategy. Moreover, the auction are nearly efficient. Experimental results show that the coevolutionary computation based iterative multi-attribute auction is a practical and nearly efficient mechanism. The proposed mechanism and framework can be realized as a multi-agent based software system to support supplier selection decision and/or deal decision for both the buyer and the suppliers in B2B markets and supply chain.