A New Proposed Modified Shortest Path Algorithm’s Using Dijkstra’s

Banerjee, Pallab; Kumar, Pappu; Kumar, Biresh; Thakur, Kanika

doi:10.1109/accai58221.2023.10199281

Cited by 1 publication

(1 citation statement)

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“…Dijkstra's algorithm (see, for example, [36][37][38]) solves the single-source shortest-paths problem on a directed and weighted graph, G = (V, E), in the case where all vertices are non-negative. It is assumed that w(u, v) ≥ 0 for every edge (u, v) ∈ E. A set, S, of vertices is maintained, whose total costs of minimum paths from the source s have already been determined.…”

Section: Dijkstra's Algorithmmentioning

confidence: 99%

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Gatti,

Barbierato,

Pozzi

2024

Electronics

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This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.

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Section: Dijkstra's Algorithmmentioning

confidence: 99%