91,864 research outputs found

    Adaptive tension, self-organization and emergence : A complex system perspective of supply chain disruptions

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    The purpose of this thesis was to explore how microstate human interactions produce macro level self-organization and emergence in a supply disruption scenario, as well as discover factors and typical human behaviour that bring about disruptions. This study argues that the complex adaptive system’s view of complexity is most suited scholarly foundation for this research enquiry. Drawing on the dissipative structure based explanation of emergence and self-organization in a complex adaptive system, this thesis further argues that an energy gradient between the ongoing and designed system conditions, known as adaptive tension, causes supply chains to self-organize and emerge. This study adopts a critical realist ontology operationalized by a qualitative case research and grounded theory based analysis. The data was collected using repertory grid interviews of 22 supply chain executives from 21 firms. In all 167 cases of supply disruptions were investigated. Findings illustrate that agent behaviours like loss of trust, over ambitious pursuit, use of power and privilege, conspiring against best practices and heedless performance were contributing to disruption. Impacted by these behaviours, supply chains demonstrated impaired disruption management capabilities and increased disruption probability. It was also discovered that some of these system patterns and microstate agent behaviours pushed the supply chains to a zone of emergent complexity where these networks self-organized and emerged into new structures or embraced changes in prevailing processes or goals. A conceptual model was developed to explain the transition from micro agent behaviour to system level self-organization and emergence. The model described alternate pathways of a supply chain under adaptive tension. The research makes three primary research contributions. Firstly, based upon the theoretical model, this research presents a conceptualization of supply chain emergence and self-organization from dissipative structures and adaptive tension based view of complexity. Secondly, it formally introduces and validates the role of behavioural and cognitive element of human actions in a supply chain scenario. Lastly, it affirms the complex adaptive system based conceptualization of supply chain networks. These contributions succeed in providing organizations with an explanation for observed deviations in their operations performance using a behavioural aspect of human agents

    Modelling Fresh Strawberry Supply "From-Farm-to-Fork" as a Complex Adaptive Network

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     The purpose of this study is to model and thereby enable simulation of the complete business entity of fresh food supply. A case narrative of fresh strawberry supply provides basis for this modelling. Lamming et al. (2000) point to the importance of discerning industry-specific product features (or particularities) regarding managing supply networks when discussing elements in "an initial classification of a supply network" while Fisher (1997) and Christopher et al. (2006, 2009) point to the lack of adopting SCM models to variations in products and market types as an important source of SCM failure. In this study we have chosen to move along a research path towards developing an adapted approach to model end-to-end fresh food supply influenced by a combination of SCM, system dynamics and complex adaptive network thinking...

    From supply chains to demand networks. Agents in retailing: the electrical bazaar

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    A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

    Efficacy of landfill tax and subsidy policies for the emergence of industrial symbiosis networks: An agent-based simulation study

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    Despite the theoretical value of industrial symbiosis (IS), this approach appears to be underdeveloped in terms of practical applications. Different attempts to stimulate IS in practice are noticed, one of them consisting in the application of adequate policy measures. This paper explores the efficacy of two specific policies (landfill tax and economic subsidy for IS exchanges) in supporting the emergence of self-organized industrial symbiosis networks (ISNs). We frame the ISNs as complex adaptive systems and we design an agent-based model to simulate their emergence. We use a real case study and, by means of the simulation model, we assess how the two policy measures are able to enhance the formation of spontaneous IS relationships, thereby forcing the emergence of the ISN. Results show that both policy measures have a positive effect in all scenarios considered, but the extent is strictly dependent on the environmental conditions in which IS relationships occur. The economic implications for the government are finally discussed

    Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study

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    This paper models a supply network as a complex adaptive system (CAS), in which firms or agents interact with one another and adapt themselves. And it applies agent-based social simulation (ABSS), a research method of simulating social systems under the CAS paradigm, to observe emergent outcomes. The main purposes of this paper are to consider a social factor, trust, in modeling the agents\' behavioral decision-makings and, through the simulation studies, to examine the intermediate self-organizing processes and the resulting macro-level system behaviors. The simulations results reveal symmetrical trust levels between two trading agents, based on which the degree of trust relationship in each pair of trading agents as well as the resulting collaboration patterns in the entire supply network emerge. Also, it is shown that agents\' decision-making behavior based on the trust relationship can contribute to the reduction in the variability of inventory levels. This result can be explained by the fact that mutual trust relationship based on the past experiences of trading diminishes an agent\'s uncertainties about the trustworthiness of its trading partners and thereby tends to stabilize its inventory levels.Complex Adaptive System, Agent-Based Social Simulation, Supply Network, Trust

    Designing Fully Distributed Consensus Protocols for Linear Multi-agent Systems with Directed Graphs

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    This paper addresses the distributed consensus protocol design problem for multi-agent systems with general linear dynamics and directed communication graphs. Existing works usually design consensus protocols using the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph, which however is global information. In this paper, based on only the agent dynamics and the relative states of neighboring agents, a distributed adaptive consensus protocol is designed to achieve leader-follower consensus for any communication graph containing a directed spanning tree with the leader as the root node. The proposed adaptive protocol is independent of any global information of the communication graph and thereby is fully distributed. Extensions to the case with multiple leaders are further studied.Comment: 16 page, 3 figures. To appear in IEEE Transactions on Automatic Contro

    Who to listen to: Exploiting information quality in a ZIP-agent Market

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    Market theory is often concerned only with centralised markets. In this paper, we consider a market that is distributed over a network, allowing us to characterise spatially (or temporally) segregated markets. The effect of this modification on the behaviour of a market populated by simple trading agents was examined. It was demonstrated that an agent’s ability to identify the optimum market price is positively correlated with its network connectivity. A better connected agent receives more information and, as a result, is better able to judge the market state. The ZIP trading agent algorithm is modified in light of this result. Simulations reveal that trading agents which take account of the quality of the information that they receive are better able to identify the optimum price within a market
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