Search results for " assignment"

showing 10 items of 65 documents

The Multiple Multidimensional Knapsack with Family-Split Penalties

2021

Abstract The Multiple Multidimensional Knapsack Problem with Family-Split Penalties (MMdKFSP) is introduced as a new variant of both the more classical Multi-Knapsack and Multidimensional Knapsack Problems. It reckons with items categorized into families and where if an individual item is selected to maximize the profit, all the items of the same family must be selected as well. Items belonging to the same family can be assigned to different knapsacks; however, in this case, split penalties are incurred. This problem arises in resource management of distributed computing contexts and Service Oriented Architecture environments. An exact algorithm based on the exploitation of a specific combi…

Mathematical optimizationCombinatorial optimizationInformation Systems and ManagementGeneral Computer ScienceComputer scienceKnapsack Problem0211 other engineering and technologiesBenders’ cuts; Combinatorial optimization; Integer programming; Knapsack Problems; Resource assignmentResource assignment02 engineering and technologyManagement Science and Operations ResearchIndustrial and Manufacturing Engineering0502 economics and businessInteger programming050210 logistics & transportation021103 operations research05 social sciencesBenders’ cutInteger programmingSolverKnapsack ProblemsBenders’ cutsExact algorithmKnapsack problemModeling and SimulationCombinatorial optimizationEuropean Journal of Operational Research
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Scheduling shared continuous resources on many-cores

2014

We consider the problem of scheduling a number of jobs on m identical processors sharing a continuously divisible resource. Each job j comes with a resource requirement rj∈[0,1]. The job can be processed at full speed if granted its full resource requirement. If receiving only an x-portion of r_j, it is processed at an x-fraction of the full speed. Our goal is to find a resource assignment that minimizes the makespan (i.e., the latest completion time). Variants of such problems, relating the resource assignment of jobs to their processing speeds, have been studied under the term discrete-continuous scheduling. Known results are either very pessimistic or heuristic in nature. In this paper, …

Mathematical optimizationJob shop schedulingComputer scienceDistributed computingApproximation algorithmJob assignmentUnit sizeCompletion timeResource assignmentMultiprocessor schedulingScheduling (computing)Proceedings of the 26th ACM symposium on Parallelism in algorithms and architectures
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Hybridizing the cross-entropy method: An application to the max-cut problem

2009

Cross-entropy has been recently proposed as a heuristic method for solving combinatorial optimization problems. We briefly review this methodology and then suggest a hybrid version with the goal of improving its performance. In the context of the well-known max-cut problem, we compare an implementation of the original cross-entropy method with our proposed version. The suggested changes are not particular to the max-cut problem and could be considered for future applications to other combinatorial optimization problems.

Mathematical optimizationOptimization problemGeneral Computer ScienceQuadratic assignment problemMaximum cutCross-entropy methodManagement Science and Operations ResearchCross entropyModeling and SimulationCombinatorial optimizationCombinatorial methodMetaheuristicAlgorithmMathematicsComputers & Operations Research
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Applying fuzzy Particle Swarm Optimization to Multi-unit Double Auctions

2010

Abstract In the context of Quadratic Programming Problems, we use a fuzzy Particle Swarm Optimization (PSO) algorithm to analyze a Multi-unit Double Auction (MDA) market. We give also a Linear Programming (LP) based upper bound to help the decision maker in dealing with constraints in the mathematical model. In the computational study, we evaluate our algorithm and show that it is a feasible approach for processing bids and calculating assignments.

Mathematical optimizationParticle Swarm Optimization fuzzy numbers mathematical programming quadratic assignment problemInformation Systems and ManagementLinear programmingQuadratic assignment problemStrategy and ManagementMechanical EngineeringParticle swarm optimizationManagement Science and Operations ResearchSettore MAT/05 - Analisi MatematicaFuzzy numberQuadratic programmingMulti-swarm optimizationSettore MAT/09 - Ricerca OperativaEngineering (miscellaneous)MetaheuristicActive set methodMathematics
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A Memetic Algorithm for Binary Image Reconstruction

2008

This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.

Mathematical optimizationSettore INF/01 - InformaticaQuadratic assignment problemBinary imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMemetic algorithmtomografy reconstructionFlow networkImage (mathematics)Set (abstract data type)Compact spaceMemetic algorithmAlgorithmSelection (genetic algorithm)Mathematics
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A heuristic for fast convergence in interference-free channel assignment using D1EC coloring

2010

This work proposes an efficient method for solving the Distance-1 Edge Coloring problem (D1EC) for the assignment of orthogonal channels in wireless networks with changing topology. The coloring algorithm is performed by means of the simulated annealing method, a generalization of Monte Carlo methods for solving combinatorial problems. We show that the simulated annealing-based coloring converges fast to a suboptimal coloring scheme. Furthermore, a stateful implementation of the D1EC scheme is proposed, in which network coloring is executed upon topology changes. The stateful D1EC is also based on simulated annealing and reduces the algorithm’s convergence time by one order of magnitude in …

Mathematical optimizationSettore ING-INF/03 - TelecomunicazioniComputer scienceHeuristic (computer science)Wireless networkTopology (electrical circuits)[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationGreedy coloringEdge coloringStateful firewallSimulated annealingConvergence (routing)Channel assignment Edge coloring Simulated annealing.Algorithm
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Time-Dependent Multiple Depot Vehicle Routing Problem on Megapolis Network under Wardrop's Traffic Flow Assignment

2018

In this work multiple depot vehicle routing problem is considered in case of variable travel times between nodes on a metropolis network. This variant of the classic multiple depot vehicle routing problem is motivated by the fact that in urban contexts variable traffic conditions play an essential role and can not be ignored in order to perform a realistic optimization. Time-travel matrices corresponding to each period of planning horizon were formed by solving the traffic assignment problem in conjunction with shortest path problem. Routing problem instances include from 20 to 100 customers randomly chosen from a road network of Saint-Petersburg. The results demonstrate that taking into ac…

Mathematical optimizationroadsDepotComputer scienceTraffic Flow Assignment0211 other engineering and technologiesTime horizon02 engineering and technologylcsh:Telecommunicationoptimointilcsh:TK5101-67200502 economics and businessVehicle routing problemta113050210 logistics & transportationreititys021103 operations researchtiet05 social sciencesbiological system modelingTraffic flowMultiple Depot Vehicle Routing ProblemVariable (computer science)suunnitteluroutingShortest path problemTime-Dependent Routing ProblemRouting (electronic design automation)planningMegapolis NetworkAssignment problemvehicle routingoptimization
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Branch-and-Bound

2010

We now turn to the discussion of how to solve the linear ordering problem to (proven) optimality. In this chapter we start with the branch-and-bound method which is a general procedure for solving combinatorial optimization problems. In the subsequent chapters this approach will be realized in a special way leading to the so-called branch-and-cut method. There are further possibilities for solving the LOP exactly, e.g. by formulating it as dynamic program or as quadratic assignment problem, but these approaches did not lead to the implementation of practical algorithms and we will not elaborate on them here.

Mathematical optimizationsymbols.namesakeBranch and boundBundle methodQuadratic assignment problemComputer scienceLagrangian relaxationCombinatorial optimization problemsymbolsLinear ordering
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Identification and potential origin of invasive clawed frogsXenopus(Anura: Pipidae) in Sicily based on mitochondrial and nuclear DNA

2013

African clawed frogs of the widespread polytypic species Xenopus laevis Daudin, 1802 (ranging large parts of sub-Saharan Africa) have been spreading since the 1940s, and have established reproductive populations in Europe, Asia and the Americas, where they can have negative impact as competitors of native amphibians and as disease vectors for chytridomycosis or ranaviruses. Here we use two mitochondrial (cytochrome b, 16S rDNA) and one nuclear (RAG 1: Recombination Associated Gene 1) DNA markers to infer the potential origin of invasive clawed frogs from Sicily that represent the largest invasive population in Europe. Identical mtDNA haplotypes match with those of Xenopus laevis, and Sicili…

Mitochondrial DNAeducation.field_of_studybiologyEcologyCytochrome bPipidaeHaplotypePopulationXenopus laeviXenopusZoologyIntroduced speciesbiology.organism_classificationNuclear DNASouth Africainvasive specieAnimal Science and ZoologyeducationPhylogenetic assignmentSicilyItalian Journal of Zoology
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Greedy and K-Greedy algoritmhs for multidimensional data association

2011

[EN] The multidimensional assignment (MDA) problem is a combinatorial optimization problem arising in many applications, for instance multitarget tracking (MTT). The objective of an MDA problem of dimension $d\in\Bbb{N}$ is to match groups of $d$ objects in such a way that each measurement is associated with at most one track and each track is associated with at most one measurement from each list, optimizing a certain objective function. It is well known that the MDA problem is NP-hard for $d\geq3$. In this paper five new polynomial time heuristics to solve the MDA problem arising in MTT are presented. They are all based on the semi-greedy approach introduced in earlier research. Experimen…

OptimizationMathematical optimizationCombinatorial optimizationPolynomial approximationESTADISTICA E INVESTIGACION OPERATIVAAerospace EngineeringApproximation algorithmNP-hardSensor fusionDimension (vector space)Combinatorial optimization problemsMulti-target trackingPolynomial time heuristicsCombinatorial optimizationAlgorithm designElectrical and Electronic EngineeringMultidimensional assignmentObjective functionsHeuristicsGreedy algorithmTime complexityAlgorithmMultidimensional dataAlgorithmsMathematics
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