Titlebook: Engineering Applications of Social Welfare Functions; Generic Framework of Francisco Munoz,Ashutosh Nayak,Seokcheon Lee Book 2023 The Edito

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More about the Learning Process,porated into an algorithm, originally called ., which allows to route data efficiently on a distributed control approach maximizing the lifetime of the network. Robustness and efficiency are achieved by emergence of the system, where each sensor makes routing decisions based on maximization of an en

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https://doi.org/10.1007/978-1-349-15839-3ystem to serve future calls within a service area. Different ways of aggregating area preparedness are proposed based on . functions. Simulated experiments show that the preparedness-based algorithm significantly reduces response time in comparison with the conventional method of dispatching the clo

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The Cultivation of Character Strengthshe algorithm uses a . metric to make decisions in real-time allowing to pursue simultaneously resource consumption minimization and task completion maximization. The implementation of the algorithm is explained in detail using numerical examples, and its flexibility and advantages over other market-

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https://doi.org/10.1007/1-4020-3772-4 of this framework is that it uses a social welfare function to decide how to allocate shared resources in complex CPSs, which allows to maximize the overall utility for the system from decisions made by local coordinators. RSBF maps tasks and resources into networks, creating a flexible framework t

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https://doi.org/10.1007/978-3-031-20545-3Social Welfare Functions; Dynamic Resource Allocation; Engineered Systems; Automation Systems; E-Service

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,Routing in Wireless Sensor Networks—Energy Welfare,porated into an algorithm, originally called ., which allows to route data efficiently on a distributed control approach maximizing the lifetime of the network. Robustness and efficiency are achieved by emergence of the system, where each sensor makes routing decisions based on maximization of an en