greedy heuristic
A greedy heuristic for workforce scheduling and routing with time dependent activities constraints
... deterministic greedy heuristic (GHI) to tackle the workforce scheduling and rout- ing problem ...proposed heuristic needs only two ...tailored heuristic is able to tackle time-dependent ...
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Genetic Algorithm with Fast Greedy Heuristic for Clustering and Location Problems
... ger alphabet (number of vertices of the network) in "chro- mosomes" (interim solutions) of the GA. Its version for pla- nar location problems [42] uses integer alphabet for coding numbers of data vectors used as ...
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A Greedy Heuristic Algorithm for Flip-Flop Replacement Power Reduction in Digital Integrated Circuits
... Fig. 1: Single-Bit Flip-Flop.. Each 1-bit flip-flop contains two inverters, master-latch and slave-latch. Due to the manufacturing rules, inverters in flip- flops tend to be ove[r] ...
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NODES PRIORITIZATION TRAVELLING SALESMAN PROBLEM
... This paper focus on optimisation of navigation routes plied by delivering agents using distance and demand as the problem parameters in finding an optimal location of warehouse/depot to serve a given locality. To ...
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Phrase Table Pruning via Submodular Function Maximization
... Phrase table pruning is the act of re- moving phrase pairs from a phrase table to make it smaller, ideally removing the least useful phrases first. We propose a phrase table pruning method that formu- lates the task as a ...
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View pdf
... Software testing is the most challenging and dominating activity used by industry, therefore, improvement in its effectiveness, both with respect to the time and resources, is taken as a major factor by many researchers. ...
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Algorithm for Linear Number Partitioning into Maximum Number of Subsets
... The Greedy heuristic and complete greedy algorithm has multi-ways version for partitioning problem but both algorithm complexity is much higher to partition a multiset into k ...subsets. ...
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Fitting the Three-parameter Weibull Distribution by using Greedy Randomized Adaptive Search Procedure
... randomized greedy heuristic; and (ii) local search phase in which a heuristic is applied to current solution in hope of achieving a better ...
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Engineering A Compiler pdf
... find greedy heuristic searches that explore large solution spaces, finite automata that recognize words in the input, fixed-point algorithms that help reason about program behavior, simple theorem provers and ...
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Exact and Heuristic Data Workflow Placement Algorithms for Big Data Computing in Cloud Datacenters
... Abstract. Several big data-driven applications are currently carried out in collaboration using distributed infrastructure. These data-driven applications usually deal with experiments at massive scale. Data generated by ...
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Simple hyper-heuristics control the neighbourhood size of randomised local search optimally for LeadingOnes
... of heuristic selection methods in the literature apply machine learning techniques that gen- erate scores for each heuristic based on their past ...simpler heuristic selection methods have been ...
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Allocation models and heuristics for the outsourcing of repairs for a dynamic warranty population
... developed heuristic policies for the alloca- tion of newly purchased items to one of a collec- tion of external vendors contracted to an equipment manufacturer to conduct repairs under ...dynamic greedy ...
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Application of Algorithms with Variable Greedy Heuristics for k-Medoids Problems
... compromise heuristic algorithms that provide a fairly quick solution with minimal ...the Greedy Heuristic Method which use the idea of the Variable Neighborhood Search (VNS) algorithms for solving ...
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Strategies to Automatically Derive A Process Model From A Configurable Process Model Based on Event Data
... and greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific ...
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Fast Heuristics for Large Instances of the Euclidean Bounded Diameter Minimum Spanning Tree Problem
... the BDST whose depth is greater than 1 (essentially cov- ering all vertices that are not either the root(s) or the ver- tices immediately connected to the root(s)) whether it can be reattached to a BDST vertex of depth ...
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Modeling Infant Word Segmentation
... explore multiple hypotheses at once, using a sim- ple beam search. New hypotheses are added to support multiple possible subtractive segmentations. For example, using the utterance above, at the be- ginning of ...
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Ant Colony Optimization Algorithm with Pheromone Correction Strategy for the Minimum Connected Dominating Set Problem
... The ant colony optimization (ACO) is a meta heuristic that has been de- veloped by Doringo for the traveling salesman problem [9]. ACO and other evolutionary algorithms have been proven to be effective on a wide ...
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Selection of Most Responsible Genes for Cancer Disease from Large Attributed Dataset Using Hybrid Approach
... TS algorithm is a meta heuristic method that guides the search for the optimal solution making use of flexible memory, which exploits the search history. TS is based on the assumption that solutions with higher ...
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Randomized heuristics for the Capacitated Clustering Problem
... the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem ...multi-start heuristic search methods when solving this NP-hard ...a ...
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Heuristic Evaluation Quality Score (HEQS): Defining Heuristic Expertise
... catastrophic issues preventing users from accomplishing goals, major issues or issues causing users to waste time or increase learning significantly, and irritants or cosmetic issues violating minor usability guidelines. ...
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