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Fully memristive spiking neural network for energy-efficient graph learning. [PDF]
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Evaluating the Impact of Aggregation Operators on Fuzzy Signatures for Robot Path Planning. [PDF]
Sensors (Basel)Karadeniz AM +3 more
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The fuzzy inference approach to solve multi-objective constrained shortest path problem
Journal of Intelligent & Fuzzy Systems, 2020The multi-objective constrained shortest path problem is one of the most significant and well-known problems in the field of network optimization which due to its many applications in routing, telecommunication, transportation, scheduling, etc., has ...
A. Sori, A. Ebrahimnejad, H. Motameni
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A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment
International Journal of Fuzzy System Applications, 2020The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat ...
Ranjan Kumar, S. Jha, Ramayan Singh
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On the Robust Shortest Path Problem
Computers & Operations Research, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gang, Yu, Jian, Yang
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A Study of Neutrosophic Shortest Path Problem
, 2020Shortest path problem (SPP) is an important and well-known combinatorial optimization problem in graph theory. Uncertainty exists almost in every real-life application of SPP.
Ranjan Kumar +3 more
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The time-dependent shortest path and vehicle routing problem
INFOR. Information systems and operational research, 2021We introduce the time-dependent shortest path and vehicle routing problem. In this problem, a set of homogeneous vehicles is used to visit a set of customer locations dispersed over a very large network where the travel times are time-dependent and ...
Rabie Jaballah +3 more
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2000
Consider a digraph G = (V, E) with non- negative costs c(e) = c ij (∀ e = (i, j) ∈ E) associated with the edges in G. To simplify further notation we define c ij := ∞ for all (i, j) ∉ E.
Horst W. Hamacher, Kathrin Klamroth
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Consider a digraph G = (V, E) with non- negative costs c(e) = c ij (∀ e = (i, j) ∈ E) associated with the edges in G. To simplify further notation we define c ij := ∞ for all (i, j) ∉ E.
Horst W. Hamacher, Kathrin Klamroth
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Shortest Paths with Shortest Detours
Journal of Optimization Theory and Applications, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carolin Torchiani +3 more
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Computation of the Reverse Shortest-Path Problem
Journal of Global Optimization, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Jianzhong, Lin, Yixun
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