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In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to find solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for finding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion. [ABSTRACT FROM AUTHOR]

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In this paper, a rank-based nonparametric statistical test for measuring the effect of cooperation between optimization agents solving the multi-mode resource-constrained project scheduling problem is presented. To solve this NP-hard optimization problem, different methods are applied including population- and agent-based approaches. One of them is a team of asynchronous agents composed of multiple optimization agents, management agents, and common memories, which through interactions produce solutions of hard optimization problems. Optimization agents represent different methods including local search, path relinking, or tabu search. Interactions are managed through various cooperation strategies based on applying heuristics, reinforcement learning, or population learning. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Review of American Studies is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article proposes a novel approach to the radial basis function network (RBFN) design. Its main idea is to apply the agent-based population learning algorithm to the task of initialization and training RBFNs. The approach allows for an effective network initialization and estimation of its output weights. The initialization involves two stages, where in the first one initial clusters are produced using the similarity-based procedure and next, in the second stage, prototypes (centroids) from the thus-obtained clusters are selected. The agent-based population learning algorithm is used to select prototypes. In the proposed implementation of the algorithm, both tasks—RBFN initialization and RBFN training—are carried out by a team of agents executing various local search procedures and cooperating with a view to determine the solution to the RBFN design problem at hand. The performance of the RBFN constructed using the proposed agent-based approach is analyzed and evaluated. The proposed approach is also compared with different RBFN initialization and training procedures in the literature. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present the application of the A-team approach to solving some machine learning problems belonging to the supervised and unsupervised learning classes. Because the above problems are computationally hard, it is proposed to take advantage of the robustness and flexibility of population-based methods combined with the efficiency of multi-agent systems integrated within the A-team concept. The main part of the article summarizes the experiences of the authors gained while developing various A-teams and includes some examples of population-based multi-agent algorithms for solving problems from the machine learning domain. It can be concluded that population-based multi-agent algorithms can be competitive in comparison with other existing techniques for some machine learning problems. [ABSTRACT FROM AUTHOR]

The paper contains a description of the implementation and performance evaluation of the agent-based population learning algorithm used to train the feed-forward artificial neural networks. The goal of the research was to evaluate efficiency of the agent-based approach and to establish experimentally which different factors representing the A-Team structure and topology affect the performance of the analyzed agent-based algorithm. The paper includes a general overview of the JABAT environment used to deploy the ANN training algorithm, a description of different agents employed and their roles, as well as the computational experiment plan and the discussion of the performance evaluation results. [ABSTRACT FROM AUTHOR]

We present a new agent-based solution approach for the problem of scheduling multiple non-identical machines in the face of sequence dependent setups, job machine restrictions, batch size preferences, fixed costs of assigning jobs to machines and downstream considerations. We consider multiple objectives such as minimizing (weighted) earliness and tardiness, and minimizing job-machine assignment costs. We use an agent-based architecture called Asynchronous Team (A-Team), in which each agent encapsulates a different problem solving strategy and agents cooperate by exchanging results. Computational experiments on large instances of real-world scheduling problems show that the results obtained by this approach are significantly better than any single algorithm or the scheduler alone. This approach has been successfully implemented in an industrial scheduling system. [ABSTRACT FROM AUTHOR]

Industry 4.0, started in Germany in 2011, was fervently promoted by various advanced countries and followed by the introduction of "smart manufacturing". As an innovative strategy of the manufacturing industry "smart manufacturing" has attracted considerable attention in the field of academics and practice. However, the issues of huge investment and differentiation of individual enterprises have created a big gap between policy promotion and production at the site of actual industrial manufacturing and resulted in limited progress in smart manufacturing. In view of the situation, the authors have previously developed in relation to the practice of individual companies in the industry a practical structure of "Lean Smart Manufacturing: a conceptual framework and solution based co-creative platform" combining Lean Production with smart manufacturing to achieve customer value. In addition to filling the gap between government policy and industry in Industry 4.0, the practical structure suggests the possibility of balancing differentiation and open innovation. Taiwan's bicycle industry is one of Taiwan's most internationally competitive industries. GIANT, the leading manufacturer of bicycles in Taiwan, has been actively promoting TPS since 2000. With the development of Industry 4.0, GIANT has been working on the application of Industry 4.0 related technologies in order to further enhance customer value. In the application of the practical structure of "Lean Smart Manufacturing", there are few cases of corporate practice. This study focuses on the A-Team which is constructed by the bicycle industry in Taiwan and GIANT for empirical research, to see how GIANT implement the "Lean Smart Manufacturing" and construct the co-creative platform, to balance differentiation and open innovation, then strengthen its competitiveness. The research results show that although this case is still in the development stage, it has the basic form of "Lean Smart Manufacturing: a conceptual framework and solution based co-creative platform". This case, as we advocate the use of differentiated advantages of "Lean Smart Manufacturing", pragmatically from the establishment of the smart factory platform on the physical system, gradually extended to the strategic co-creative platform of the cyber system. [ABSTRACT FROM AUTHOR]