![]() Additionally, mobile and immobile citizens stuck in impoverished, highly populated areas, so-called ‘islands of inequity’ where often initial infrastructure is missing, need to be able to connect with disaster response teams requiring hybrid communication approaches. This requires communication networks to adapt to these changing spatial-temporal-resource contexts. Citizens are confronted with challenges such as the complicated deployment of these networks, resource-constrained mobile phones and mobility. In a dynamic or disruptive situation, such as a disaster, a fully mobile and decentralized infrastructure-less network seems to be a viable option for communication. The results comparison shows that the proposed optimised empty container repositioning framework can significantly reduce the shipping line’s costs and make full use of empty containers. The model was applied to the Asia–Middle East region to simulate global empty containers repositioning in the region. Using simulated annealing (SA), shipping line agents were able to optimise empty container repositioning to determine the best sequence for moving containers. In the system, ports, shipping companies, customers, and empty containers were identified as critical agents. An agent-based maritime logistic empty container redistribution model was developed to help minimize the total relevant costs for empty container movement in the planning horizon. This research proposes a Maritime Empty Container Reposition Modelling Framework by integrating the agent-based modelling (ABM) paradigm to model the global movements of empty containers. Empty containers accumulated at specific ports cannot only generate profit but also increase the environmental footprint. In addition, wediscuss challenges in simulating 100 million agents.ĭue to an ever-increasing movement of containers across the globe in line with the economic boom, the trade imbalance and issues related to empty containers have become inevitable. ![]() We demonstrate performanceimprovement for Pune and Mumbai cities with 3.2 and 12 million populations respectively. In this paper, we share our ongoing journey of developing it as a highly scalable cloud readyparallel and distributed implementation to simulate up to 100 million agents. However,the current implementation is not scalable for this purpose, since it has a well-tuned serial implementationat its core. Our goal is to simulate larger citieslike Mumbai (with 12 million population) first, and then entire India with its 1+ billion population. We could demonstrate EpiRust scaling up to a few millions of agents, for example a COVID-19 infection spreading through Pune city with its 3.2 million population. It has been developed with three key factors in mind namely 1. EpiRust is an open source, large-scale agent-based epidemiological simulation framework developed usingRust language.
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