Ping Lou

Agile Supply Chain Management over the Internet of Things

A supply chain (SC) is a network of supplier, factories, warehouse, distribution centers, retailers, and customers, through which raw materials are acquired, transformed, produced, and delivered to the customers. As a complex network of suppliers, factories, warehouses, distributors, and retailers, the highly effective and efficient operations of a SC depend on the speedily moving of information flows and material flows. The rapid advances in information technology (IT) have always resulted in innovation, such as the Internet, that is terms of its potential as a channel for collecting, transmitting and storing information. As is well-known, the emergence of Internet has made a significant impact on the rapid growth of Internet-based information transfer between companies, their suppliers, and their customers. The Internet has emerged as an effective means of driving information integration and sharing for a supply chain, as well as has supported various coordination mechanisms across a supply chain. To make decisions promptly and to accelerate material flows via the integration and sharing of information flows improve the effectiveness and efficiency of a supply chain execution. However there exists a gap between the material flows and the information flows in a supply chain because of the information flows always can not reflect the wave of material flows real-timely, as well as it is impossible to understand the process of a supply chain execution real-timely. Being the next generation network of the Internet, the Internet of Things (IoT), which is a comprehensive extension of the Internet and also can achieve the pervasive connections between objects (things) and objects (things), information automotive collecting and real-time processing, as well as ubiquitous computing, close the gap between objects in the material world and their representation in information systems. It is able to real-timely monitor the process of a supply chain execution and also to further improve the efficiency and effectiveness of supply chain over the IoT. In this paper, the authors mainly discuss the characteristics of the IoT and enabling technologies, and explore the distributed architecture of SCM over the IoT.

Manufacturing intelligence for industrial engineering : methods for system self-organization, learning, and adaptation

Artificial neural networks Communication and interaction protocol Computing intelligence Cooperation, negotiation, and behavior coordination Industrial engineering Intelligent activities in manufacturing Intelligent agents Intelligent technology applications in manufacturing Knowledge acquisition and management Methods of data fusion Modern manufacturing Multi-sensor information fusion Problem solving and interaction mechanism Theories of multi-agent systems

A Forager Adjustment Strategy Used by the Bees Algorithm for Solving Optimization Problems in Cloud Manufacturing

Intelligent technologies have become increasingly important in manufacturing nowadays. Optimal service management and allocation in current cloud manufacturing model are impossible without applications of appropriate intelligent tools. The Bees Algorithm (BA) is a swarm-based intelligent optimizer that provides support for smart decision-making process in manufacturing models. A novel forager adjustment strategy (FAS) is proposed in this paper to manage the forager division in the algorithm, so as to make the entire colony perform with higher efficiency. The proposed FAS based Bees Algorithm (FAS-BA) is able to realize flexible allocation of its forager resources between different roles in accordance with the solution fitness sampled by current scout population. The proposed algorithm is presented in detail. Experiments are conducted based on a set of well-known benchmark functions and a case study. Comparisons between FAS-BA and an improved Bees Algorithm are made to highlight the effectiveness of FAS. The results demonstrate that the proposed algorithm requires less function evaluation cost than the improved version but is capable of obtaining at least the same optimal solution to a problem.Copyright

Production-outsourcing supply chain quality management based on multi-agent system

Quality is one of the most important factors for an enterprise to win the competition. Production-outsourcing supply chain (POSC) is a kind of special supply chain in which the core enterprise transfer the manufacturing of products to the other enterprises. It results in the separation of two important stages — product designing and product manufacturing for forming product quality, that is product design and product manufacture are completed by different enterprises respectively. Traditional quality management which focuses on the inside of enterprise and emphasizes the coordination and cooperation of processes, elements and department inside an enterprise cannot satisfy the requirements of this kind of POSC quality management. In this paper, the architecture of a distributed cooperative quality management based on multi-agent system is presented. And an incentive mechanism is put forward to achieve the distributed cooperative quality management for this kind of POSC based on the information sharing through this architecture.

An Improved Search Strategy for Even Degree Distribution Networks

Many efforts in studying network structure and dynamics have been engaged, among which the research on the node search or navigation is one of the most important branches. With a brief analysis of the existing search strategies, the MDS (maximum degree strategy) is found not applicable to large-scale with even degree distribution networks. By importing the minimum cluster coefficient as one parameter for the node search, the MCMDS ( M inimum C luster C oefficient and M aximum D egree S trategy) is presented in order to better its search performance for networks of high power-law exponents. Specific implementation steps for the MCMDS are provided. T he strategy is simulated and the results are analyzed. In the end a test to verify the efficiency of the MCMDS on the networks with real data is employed. Simulation results show that the MCMDS presented in the paper can improve the performance in both search steps and search time for its even degree distribution.

Distributed production scheduling in an open manufacturing environment

In this epoch of global economic markets, an intense world-wide competition has impelled manufacturing industry to move toward the agile and high-effective manufacturing paradigm. All require a flexible, requirement-driven, and reconfigurable production system to adapt to this open manufacturing environment and to be subject to the requirements of the increasing reduction in product life cycle and rapid changes in market demands. The advent and development of network technology (especially Internet is more popular) and distributed computing technology provide enabling technology for the geographically dispersed manufacturing resources to be integrated and employed effectively and efficiently. And manufacturing enterprises can expand/improve their throughput within a short time through outsourcing or leasing equipments/product lines. Hence, there are some new requirements for production scheduling in this open manufacturing environment. Owing to the manufacturing environment more open and the changes in market demands more quick, the traditional centralized management and scheduling approaches which assume knowledge of all jobs and manufacturing resources are in control of a decision maker are not suitable for this open distributed manufacturing environment. And the traditional approaches that are inflexible and slowly responsive to emergencies (such as machine failure, operator absence, material shortage, and etc.) can not satisfy the needs of unforeseen dynamic situation. This paper presents a multi-agent system model, selection mechanisms for manufacturing resources and an effective negotiation technique for production scheduling. The model involves some task agents and resource agents. A solution to a production scheduling problem is the result of negotiation-based conflict resolution in the iterative and asynchronous multi-agent decision making process.