Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.

Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory

A class of nonlinear multiagent systems with time delays and external noises is investigated, and a distributed adaptive robust control protocol is developed. It is the first time for a class of multiagent systems to take both time delays and external noises into consideration. By virtue of Lyapunov–Krasovskii functional and Young’s inequality, the effects of time delay can be eliminated. Then, to exclude external noises, a robustifying term is introduced to eliminate the negative effects of these noises. Moreover, neural networks are utilized to learn the unknown nonlinear terms to adapt to the complex external environment. Finally, a numerical simulation is conducted to validate the effectiveness of our distributed control protocol.

Neural-Network-Based Distributed Adaptive Robust Control for a Class of Nonlinear Multiagent Systems With Time Delays and External Noises

: The Director of Engineering tasked the Functional Area Management Office (FAMO), to establish a Human Resource Database. This resulted after FAMO's initial study, Personnel Planning, Identification and Projection (PPIP), to assess resources required to fulfill and maintain ERDEC's workforce. The PPIP outlines current and next generation manpower needs within the framework of the Department of Defense (DoD) Fy94-FY99 Program Objective Memorandum (POM) period. It is based on the 17 June 1994 mini-POM, and customer programs excluding research, development, technology, and engineering (RDT&E). The PPIP study served as an eye opener. The urgency of establishing a human resource database for planning became apparent as DoD downsized, increased budget cuts, and instituted hiring freezes. "Learning to do more with less" has forced our organization into a position to improve resource management. More critically, the unpredictable changes in restrictions placed on limited resources that we face leave us no other alternative than to optimize how we use our resources. The database established for the PPIP study proved to be a useful planning tool that aided in allocating and organizing resources. Moreover, human resource allocation became an integral part in our organization's strategic planning.

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Human Resource Planning.

Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

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Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

This paper focuses on interaction protocols and topologies of multiagent systems (MASs) for task allocation, particularly in manufacturing application. Resource agents in manufacturing are members of a network whose possible logical topologies and governing interaction protocol influence the scheduling and control in the MAS. Four models are presented in this paper, each having specific rules and characteristics for scheduling and task allocation. Two models out of the four use a well-known standard interaction method [contract-net protocol (CNP)], while the others are proposed in this paper. The newly proposed models are based on ring topology and algorithms developed in the research. A Java-based MAS was also developed to simulate different scenarios of task allocation and to compare the four models in terms of some scheduling performance indicators, using cases from manufacturing. The results produced meaningful differences between the four models, including their strengths and weaknesses. Two models, namely, modified ring and CNP-based peer-to-peer, gave superior performance compared with the others. Furthermore, the proposed modified ring exhibits significant potential in handling manufacturing task allocation applications.

Agent-Based Interaction Protocols and Topologies for Manufacturing Task Allocation

Market-based mechanisms such as the contract net protocol (CNP) are very popular for dynamic job allocation in distributed manufacturing control and scheduling. The CNP can be deployed with different configurations of the system elements. Every configuration corresponds to a basic or a hybrid topology. The subject of topology is generally discussed in the field of “distributed systems.” Inspired from the notion of topology in the distributed systems, this paper proposes a ring-like model as a competitor for the web-like CNP-based job allocation within the concept of holonic manufacturing systems. Details of the algorithm for scheduling and assignment of jobs to resources in the ring structure is presented and its performance is compared with both CNP-based distributed model, and the centralized conventional scheduling of a real manufacturing case study involving a major turbine production plant. Comparison of performance indicators such as time and cost of operations shows that the distributed models clearly outperform the conventional practice with meaningful impact on the production economy. As a possible implementation strategy, a hybrid switching model, composed of both competing models, is proposed.

A New Agents-Based Model for Dynamic Job Allocation in Manufacturing Shopfloors

This paper presents a critical and focused review of definitions, challenges and developments of the workforce allocation system in manufacturing shop floors with a particular emphasis on response to changes and disturbances. The continuously changing requirements in production environments have been the driving force behind several stages of evolution experienced by management of workforce allocation. The paper includes an in-depth examination of the phenomena of changes and disturbances in the production shop floor. This is followed by an assessment of the readiness of workforce allocation systems to respond to such events with respect to both workforce perception and system architecture. Recent developments of the workforce allocation models suggest that the use of intelligent and distributed systems and techniques offer solutions that are responsive, flexible, and multi-criteria. To this end, an overview of a recent study in the application of the principles of holonic manufacturing systems, as an exa...

Challenges and trends in the allocation of the workforce in manufacturing shop floors

This paper attempts to model the formation of a manufacturing network based on the holonic manufacturing concept. An industrial case study involving a large network of Small and Medium-sized Enterprises (SMEs) has been carried out to investigate the processes underpinning the network formation and job allocation activities. An algorithm using agent-based (Contract Net Protocol) features has been developed to validate the holonic model with real-life industrial data. The simulation software was calibrated so that the model responds with a lead time value close to that obtained in the real life network by altering the network capacity. The results proved that the software and the model possess a good potential, as a benchmark, for the comparison of optimised and new experimental models of a manufacturing network based on holonic system.

A holonic systems approach to the formation of manufacturing networks

Productivity in labour-intensive manufacturing shopfloors is highly influenced by availability and conditions of skilled workers. As operations intensify, the need for the right people at the right time performing the right job becomes increasingly important in order to respond rapidly to any disturbances caused by changing customer demands or other factors in the manufacturing environment. In this paper, a flexible workforce allocation model is proposed and presented in the context of holonic manufacturing system. The model is designed to respond to changes and disturbances in the availability of the workforce. Data from a real manufacturing shopfloor are used to test the model through a database system development and discrete-event simulation platform. The results show that the workforce allocation suggested by the holonic model in this paper is better able to respond to workforce change and disturbance than the company's conventional experience-based, decision-making process.

Mohammad Owliya

Papers

Agent-Based Interaction Protocols and Topologies for Manufacturing Task Allocation

A New Agents-Based Model for Dynamic Job Allocation in Manufacturing Shopfloors

Challenges and trends in the allocation of the workforce in manufacturing shop floors

A holonic systems approach to the formation of manufacturing networks

A holonic shop floor workforce allocation system