Extending process automation systems with multi-agent techniques

Seilonen, Ilkka; Pirttioja, Teppo; Koskinen, Kari

doi:10.1016/j.engappai.2008.10.007

Cited by 23 publications

(14 citation statements)

References 18 publications

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“…At the same time, the models can cover many different kinds of aspects through the agents. Since the model parameters and even structure can come from different databases, it is possible to have a high amount of versatility in the model (Chatfield et al 2006;Seilonen et al 2009, Hilletofth et al 2010a. A well designed ABDS system is able to use data from various sources and fuse them into a coherent view on the supply chain (Hilletofth et al, 2010a).…”

Section: Discussionmentioning

confidence: 99%

“…For example, they can support managers to find the highest leverage among improvement alternatives, guide managers' instinct through emergent pattern, and enhance managers' understanding of the impact of unscheduled factors (Nilsson, and Darley, 2006). These systems may also sometimes improve predictability (Frayret et al, 2007) and their reconfiguration ability is very high (Seilonen et al 2009). …”

Section: Introductionmentioning

confidence: 99%

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Agent based decision support in the supply chain context

Hilletofth

Lättilä

2012

Industrial Management &Amp; Data Systems

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Purpose: The purpose of this paper is to investigate the benefits and the barriers of agent based decision support (ABDS) systems in the supply chain context. Design/methodology/approach: Two ABDS systems have been developed and evaluated. The first system concerns a manufacturing supply chain while the second concerns a service supply chain. The systems are based on actual case companies. Findings:This research shows that the benefits of ABDS systems in the supply chain context include the possibility to increase versatility of system architecture, to improve supply chain visibility, to conduct experiments and what-if analyses, to improve the understanding of the real system, and the possibility to improve communication within and between organizations in the supply chain. The barriers of ABDS systems in the supply chain context include the difficulty to access data from partners in the supply chain, the difficulty to access data on a higher level of granularity, and the difficulty to retrieve data from other information systems. Research limitations/implications:The research is explorative in nature therefore empirical data from similar and other research settings should be gathered to reinforce the validity of the findings. Practical implications:This research provides knowledge and insights on how ABDS systems may be developed and used in the supply chain context and demonstrates its main benefits and barriers. Originality/value:This research expands the current research of benefits of ABDS systems to the supply chain domain and also addresses the barriers of ABDS systems to a larger extent than previous research. Comparisons to other simulation based decision support systems are also given.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agent based decision support in the supply chain context

Hilletofth

Lättilä

2012

Industrial Management &Amp; Data Systems

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“…In industrial assembly systems this is a novice approach and it has numerous benefits when implemented: it leads to increased flexibility and adaptivity to unexpected changes and uncertainties in the system, i.e. responsiveness [8]. The system becomes insensitive to number of agents (robots) and new assembly tasks can be resolved with less effort.…”

Section: Contribution To Sustainabilitymentioning

confidence: 99%

Autonomous Planning Framework for Distributed Multiagent Robotic Systems

Švaco

Šekoranja

Jerbić

2011

Technological Innovation for Sustainability

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Abstract. In this paper a creative action planning algorithm (CAPA) is presented for solving multiagent planning problems and task allocation. The distributed multiagent system taken in consideration is a system of m autonomous agents. Agents workspace contains simplified blocks which form different space structures. By employing the planning algorithm and through interaction agents allocate tasks which they execute in order to assemble the required space structure. The planning algorithm is based on an inductive engine. From a given set of objects which can differ from the initial set agents need to reach a solution in the anticipated search space. A multiagent framework for autonomous planning is developed and implemented on an actual robotic system consisting of three 6 DOF industrial robots.

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“…The operating system is an imperative component of the system software in a computer system [2]. Time-sharing operating systems schedule tasks for efficient use of the system and may also include accounting for cost allocation of processor time, mass storage, printing, and other resources.…”

Section: Literature Reviewmentioning

confidence: 99%