Facing Big Data by an Agent-Based Multimodal Evolutionary Approach to Classification

Purpose Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

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Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics

IEEE Access ◽

10.1109/access.2017.2764467 ◽

2018 ◽

Vol 6 ◽

pp. 366-379 ◽

Cited By ~ 11

Author(s):

Ivan Garcia-Magarino ◽

Raquel Lacuesta ◽

Jaime Lloret

Keyword(s):

Big Data ◽

Internet Of Things ◽

Data Analytics ◽

Big Data Analytics ◽

Agent Based Simulation ◽

Agent Based

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Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

Complex & Intelligent Systems ◽

10.1007/s40747-021-00595-4 ◽

2021 ◽

Author(s):

Gang Zhang ◽

Hao Li ◽

Rong He ◽

Peng Lu

Keyword(s):

Big Data ◽

Life Cycle ◽

Evolutionary Dynamics ◽

Life Cycles ◽

Agent Based Modeling ◽

Macro Level ◽

Collective Actions ◽

Agent Based ◽

Individual Behaviors ◽

Micro Level

AbstractThe outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

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Semantic Agent Based Knowledge Discovery from Big Data on the Web

10.4197/comp.7-1.3 ◽

2018 ◽

Vol 7 (1) ◽

Author(s):

Raniah Bafaqeer ◽

Fathy Eassa ◽

Kamal Jambi

Keyword(s):

Big Data ◽

Knowledge Discovery ◽

Agent Based ◽

The Web

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Big data-drive agent-based modeling of online polarized opinions

Complex & Intelligent Systems ◽

10.1007/s40747-021-00532-5 ◽

2021 ◽

Author(s):

Peng Lu ◽

Zhuo Zhang ◽

Mengdi Li

Keyword(s):

Big Data ◽

Life Cycle ◽

Evolutionary Dynamics ◽

Life Cycles ◽

Agent Based Modeling ◽

Mobile Internet ◽

Real Case ◽

Agent Based ◽

Individual Behaviors ◽

Life Cycle Process

AbstractUnder the mobile internet and big data era, more and more people are discussing and interacting online with each other. The forming process and evolutionary dynamics of public opinions online have been heavily investigated. Using agent-based modeling, we expand the Ising model to explore how individuals behave and the evolutionary mechanism of the life cycles. The big data platform of Douban.com is selected as the data source, and the online case “NeiYuanWaiFang” is applied as the real target, for our modeling and simulations to match. We run 10,000 simulations to find possible optimal solutions, and we run 10,000 times again to check the robustness and adaptability. The optimal solution simulations can reflect the whole life cycle process. In terms of different levels and indicators, the fitting or matching degrees achieve the highest levels. At the micro-level, the distributions of individual behaviors under real case and simulations are similar to each other, and they all follow normal distributions; at the middle-level, both discrete and continuous distributions of supportive and oppositive online comments are matched between real case and simulations; at the macro-level, the life cycle process (outbreak, rising, peak, and vanish) and durations can be well matched. Therefore, our model has properly seized the core mechanism of individual behaviors, and precisely simulated the evolutionary dynamics of online cases in reality.

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