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Software Agents Mediated Decision Simulation in Supply Chains
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Author(s): Kamalendu Pal (City, University of London, UK)
Copyright: 2019
Pages: 17
Source title:
Emerging Applications in Supply Chains for Sustainable Business Development
Source Author(s)/Editor(s): M. Vijaya Kumar (National Institute of Technology Warangal, India), Goran D. Putnik (University of Minho, Portugal), K. Jayakrishna (Vellore Institute of Technology University, India), V. Madhusudanan Pillai (National Institute of Technology Calicut, India)and Leonilde Varela (University of Minho, Portugal)
DOI: 10.4018/978-1-5225-5424-0.ch003
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Abstract
The concept of software agent has become important in both artificial intelligence and mainstream computer science. Multi-agent systems (MAS) are providing the way to design and implement information system solutions that exhibit flexibility, adoptability and reconfigurability in a distributed environment, which are main benefits over traditional centralized software systems. The analysis, design, deployment and testing of such distributed agent-based software systems, particularly those exhibiting intelligent decision-making properties, are usually a challenging task. Simulation plays a key role to analyse the behaviour of MAS solutions during the analysis and design phase of automated software solution. This chapter uses the concept of multi-agent computing and presents software architecture for green supply chain management, in particular carbon footprint assessment planning for a multi-modal transportation problem. In this architecture, all the software agents' operations are governed by a hybrid knowledge-based which utilizes case-based reasoning (CBR) and rule-based reasoning (RBR). The describe architecture accepts a transportation service request and plans a transportation strategy with a minimum environmental impact (i.e. CO2 footprint), by retrieving best practices (from a carbon footprint perspective) for each route leg, from a repository of best practiced cases. Carbon footprint best practices from each route leg in a multi-modal transportation scenario are used to minimize environmental impact and thus demonstrate system functionality.
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