Comparison of negotiation protocols in dynamic agent-based manufacturing systems

Reaidy, J.; Massotte, Pierre; Diep, Daniel

doi:10.1016/j.ijpe.2004.12.011

Cited by 51 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Diepl and Reaidy [66] investigated the means of co-ordination in a production system based on a hierarchical MAS using game theory. Reaidy et al [67] proposed a negotiation methodology based on a MAS for heterarchical and complex manufacturing control systems. Agnetis et al [68] addressed a deterministic scheduling problem, where two agents compete for the usage of a single machine.…”

Section: B Game Theory For Schedulingmentioning

confidence: 99%

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Wang

Zhang

Liu

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This work addresses this issue and a new multi-agent-based real-time scheduling (MARS) architecture is proposed for an Internet of Things (IoT)-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework (JADE) platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption. be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. Recently, investigations have been made by a number of scholars on multi-agent-based dynamic scheduling [22][23]. However, most of these researches mainly focus on the architectures of multi-agent systems (MAS) and negotiation protocols among the agents, as well as the application of distributed features of MAS for task allocation in a traditional manufacturing shop floor [24]. Few of them consider the real-time-data-based interaction between machines and other distributed resources in an IoT-enabled flexible job shop. As a result, often the performance of efficiency is degraded and

show abstract

Section: B Game Theory For Schedulingmentioning

confidence: 99%

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Wang

Zhang

Liu

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…(3) Negotiation protocol: enhances production on demand, competition power, and reaction to the market via cooperation-based agent-based paradigm (Reaidy et al, 2006). (4) Order management protocol: regulates the interactions between agents through ordering processes, including outsourcing, order acceptance, coordination between supplier and customer, order confirmation, material request, canceling the orders, negotiating on delivery dates, and subcontracting (Huang et al, 2010).…”

Section: Population Managermentioning

confidence: 99%

Optimization and Control

Nof

Ceroni

Jeong

et al. 2015

Revolutionizing Collaboration Through E-Work, E-Business, and E-Service

View full text Add to dashboard Cite

Abstract. Considering the foundations, tools, and emerging discoveries of collaborative e-Work, as discussed in Chapters 1 and 2, it is realized that optimization and control are focused primarily on the core elements of e-Systems; agents, protocols, and workflows. In this chapter, we will show that these elements compose a solid framework for optimization and control of collaboration in emerging distributed e-Work systems. In order to be able to efficiently pass the benefits on to more complex constructs such as autonomous agents systems, production units configuration, highly reactive control protocols, and so on, these elements must be optimized as well. In order to show the evidence of the latest developments in optimization and control involving agent, protocol, and workflow theories, this chapter reviews the state-of-the-art techniques for achieving optimal design and operational control, and collaboration engineering. This chapter covers the incentives to construct autonomous agent-based systems, the key e-Criteria emerging from the transformation from traditional centralized work systems to decentralized e-Work systems, and several real-life applications of agent-based systems. Basic agent-based optimization and control architectures are reviewed along with pioneering bioinspired mechanisms based on swarm intelligence and natural evolution, and their impact on the intelligence and autonomy of agents. Several techniques for protocol and workflow optimization are also discussed. Emerging Impacts of AgentsWith the transformation from traditional monolithic systems to decentralized e-Work networks, optimization and control of e-Systems are facing new challenges. The emerging requirements for timely response to the growing complexity and dynamicity of e-Systems call for higher intelligence and autonomy of agents for smarter interaction and collaboration (Rosenschein and Ephrati, 1993). Classic monolithic and hierarchical systems are no longer sufficient for addressing such requirements, and often lead to situations where the entire system collapses by a single error or conflict at one point of the system hierarchy 116 4 Optimization and Control (Colombo et al., 2006). Various e-Criteria have been defined to formalize the emerging needs of e-Systems at different levels, and with different contextual relationships (Figure 4.1): adaptability, flexibility, reconfigurability, changeability, robustness, and scalability (Putnik et al., 2013). The prerequisites for addressing these requirements, however, mean to enable and delegate intelligence and autonomy to the lowest-level individual units collaborating within the e-System, namely the agents. Fig. 4.1 (a) Flexibility is interpreted as the ability of a system to change its behavior without changing its configuration, and reconfigurability is interpreted as the ability to change the behavior of a system by changing its configuration (both implying a changeable system). Scalability implies changing configuration by adding/removing the configuration elements, which is...

show abstract

“…Dispatching combined with priority rules for resource allocation is the best known heuristic for scheduling (Reaidy et al, 2006). Hybrid dispatching rules, either sequential or integrated, can also be implemented to improve the system response to various types of conditions (Sels et al, 2012).…”

Section: Heuristics and Dispatching Rules Approachesmentioning

confidence: 99%

Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Rey

Bonte

Prabhu

et al. 2014

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

Comparison of negotiation protocols in dynamic agent-based manufacturing systems

Cited by 51 publications

References 10 publications

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Optimization and Control

Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Contact Info

Product

Resources

About