we give an algorithm, called t*, for finding the k shortest simplepaths connecting a certain pair of nodes, s and t, in a acyclic digraph.first the nodes of the graph are labeled according to the topologicalordering. then for node i an ordered list of simple s − i paths iscreated. the length of the list is at most k and it is created by usingtournament trees. we prove the correctness of t* and ...

In this work, we study the ranking algorithm used by Fédération Internationale de Football Association (FIFA); analyze parameters that it currently uses, show formal probabilistic model from which can be derived, and optimize latter. particular, analyzing games since introduction of in 2018, conclude game’s “importance” (defined FIFA algorithm) is counterproductive point view predictive capacit...

In this study a new approach to rank exponential fuzzy numbers using  -cuts is established. The metric distance of the interval numbers is extended to exponential fuzzy numbers. By using the ranking of exponential fuzzy numbers and using  -cuts the critical path of a project network is solved and illustrated by numerical examples. Keywords: Exponential Fuzzy Numbers,  -cuts, Metric Dista...

In this paper we study a property of time-dependent graphs, dubbed “path ranking invariance”. Broadly speaking, graph is invariant” if the ordering its paths (w.r.t. travel time) independent start time. show that, path invariant, solution large class vehicle routing problems can be obtained by solving suitably defined (and simpler) time-independent problems. We also how checked linear program. ...

by new advancements in vehicle manufacturing vehicle quality evaluation and assurance has become a more critical issue. in present work, the vibration transfer path analysis and vibration path ranking of a car interior has been performed. the method is similar to classical multilevel tpa methods but has distinct differences. the method is named vivs which stands for vehicle interior vibration s...

Ranking algorithms are often introduced with the aim of automatically personalising search results. However, most ranking algorithms developed in the machine learning community rely on a careful choice of some regularisation parameter. Building upon work on the regularisation path for kernel methods, we propose a parameter selection algorithm for ranking SVM. Empirical results are promising.