A multi-agent optimisation model for solving supply network configuration problems

Supply chain literature highlights the increasing importance of effective supply network configuration decisions that take into account such realities as market turbulence and demand volatility, as well as ever-expanding global production networks. These realities have been extensively discussed in the supply network literature under the structural (i.e., physical characteristics), spatial (i.e., geographical positions), and temporal (i.e., changing supply network conditions) dimensions. Supply network configuration decisions that account for these contingencies are expected to meet the evolving needs of consumers while delivering better outcomes for all parties involved and enhancing supply network performance against the key metrics of efficiency, speed and responsiveness. However, making supply network configuration decisions in the situations described above is an ongoing challenge. Taking a systems perspective, supply networks are typically viewed as socio-technical systems where SN entities (e.g., suppliers, manufacturers) are autonomous individuals with distinct goals, practices and policies, physically inter-connected transferring goods (e.g., raw materials, finished products), as well as socially connected with formal and informal interactions and information sharing. Since the structure and behaviour of such social and technical sub-systems of a supply network, as well as the interactions between those subsystems, determine the overall behaviour of the supply network, both systems should be considered in analysing the overall system

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

HIERARCHICAL-GRANULARITY HOLONIC MODELLING

This thesis aims to introduce an agent-based system engineering approach, named Hierarchical-Granularity Holonic Modelling, to support intelligent information processing at multiple granularity levels. The focus is especially on complex hierarchical systems. Nowadays, due to ever growing complexity of information systems and processes, there is an increasing need of a simple self-modular computational model able to manage data and perform information granulation at different resolutions (i.e., both spatial and temporal). The current literature lacks to provide such a methodology. To cite a relevant example, the object-oriented paradigm is suitable for describing a system at a given representation level; notwithstanding, further design effort is needed if a more synthetical of more analytical view of the same system is required. In the literature, the agent paradigm represents a viable solution in complex systems modelling; in particular, Multi-Agent Systems have been applied with success in a countless variety of distributed intelligence settings. Current agent-oriented implementations however suffer from an apparent dichotomy between agents as intelligent entities and agents\u2019 structures as superimposed hierarchies of roles within a given organization. The agents\u2019 architectures are often rigid and require intense re-engineering when the underpinning ontology is updated to cast new design criteria. The latest stage in the evolution of modelling frameworks is represented by Holonic Systems, based on the notion of \u2018holon\u2019 and \u2018holarchy\u2019 (i.e., hierarchy of holons). A holon, just like an agent, is an intelligent entity able to interact with the environment and to take decisions to solve a specific problem. Contrarily to agent, holon has the noteworthy property of playing the role of a whole and a part at the same time. This reflects at the organizational level: holarchy functions first as autonomous wholes in supra-ordination to their parts, secondly as dependent parts in sub-ordination to controls on higher levels, and thirdly in coordination with their local environment. These ideas were originally devised by Arthur Koestler in 1967. Since then, Holonic Systems have gained more and more credit in various fields such as Biology, Ecology, Theory of Emergence and Intelligent Manufacturing. Notwithstanding, with respect to these disciplines, fewer works on Holonic Systems can be found in the general framework of Artificial and Computational Intelligence. Moreover, the distance between theoretic models and actual implementation is still wide open. In this thesis, starting from the Koestler\u2019s original idea, we devise a novel agent-inspired model that merges intelligence with the holonic structure at multiple hierarchical-granularity levels. This is made possible thanks to a rule-based knowledge recursive representation, which allows the holonic agent to carry out both operating and learning tasks in a hierarchy of granularity levels. The proposed model can be directly used in terms of hardware/software applications. This endows systems and software engineers with a modular and scalable approach when dealing with complex hierarchical systems. In order to support our claims, exemplar experiments of our proposal are shown and prospective implications are commented