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About 10,000 papers for “Assignment problem”

The assignment problem

Carole Delporte-Gallet, Hugues Fauconnier, Eli Gafni et al. · Theoretical Computer Science · 2021

Cited by 0

Car Selection Problem using Assignment Problem

Muley, Yogesh · Zenodo (CERN European Organization for Nuclear Research) · 2019

In this paper, we tried to solve real life problem i.e. Car selection problem (by taking information of car details throughgoogle) is carried out as per user requirement by using Assignment problem as well Revised one’s Assignment method.

Cited by 0Open access

The Paired Assignment Problem

Vardges Melkonian · Open Journal of Discrete Mathematics · 2014

We consider a variation of the maximum bipartite matching problem where each completed task must have at least two agents assigned to it. We give an integer programming formulation for the problem, and prove that the basic solutions of LP-relaxation are half-integral. It is shown that a fractional basic solution can be further processed to obtain an optimal solution to the problem.

Cited by 1Open access

The hypergraph assignment problem

Heismann, Olga · DepositOnce · 2014

Diese Arbeit beschäftigt sich mit dem Hypergraph Assignment Problem (Abkürzung "HAP", dt.: Zuordnungsproblem auf Hypergraphen), einem Mengenzerlegungsproblem auf einem speziellen Typ von Hypergraphen. Das HAP verallgemeinert das Zuordnungsproblem von bipartiten Graphen auf eine Struktur, die wir bipartite Hypergraphen nennen, und ist durch eine Anwendung in der Umlaufplanung im Schienenverkehr motiviert. Die Hauptresultate betreffen die Komplexität, polyedrische Ergebnisse, die Analyse von Zufallsinstanzen sowie primale Methoden für das HAP. Wir beweisen, dass das HAP NP-schwer und APX-schwer ist, sogar wenn wir uns auf kleine Hyperkantengrößen und Hypergraphen mit einer speziellen, partitionierten Struktur beschränken. Darüber hinaus untersuchen wir die Komplexität der Mengenpackungs- sowie Mengenüberdeckungsrelaxierung und geben für bestimmte Fälle Approximations- und exakte Algorithmen mit einer polynomiellen Laufzeit an. Für das Polytop des Zuordnungsproblems ist eine vollständige lineare Beschreibung bekannt. Wir untersuchen daher auch das HAP-Polytop. Dafür ist die Anzahl der Facettenungleichungen schon für sehr kleine Problemgrößen sehr groß. Wir beschreiben eine Methode zur Aufteilung der Ungleichungen in Äquivalenzklassen, die ohne die Verwendung von Normalformen auskommt. Die Facetten in jeder Klasse können durch Symmetrien ineinander überführt werden. Es genügt, einen Repräsentanten aus jeder Klasse anzugeben, um ein vollständiges Bild der Polytopstruktur zu erhalten. Wir beschreiben den Algorithmus "HUHFA", der diese Klassifikation nicht nur für das HAP, sondern für beliebige kombinatorische Optimierungsprobleme, die Symmetrien enthalten, durchführt. Die größtmögliche HAP-Instanz, für die wir die vollständige lineare Beschreibung berechnen konnten, hat 14049 Facetten, die in 30 Symmetrieklassen aufgeteilt werden können. Wir können 16 dieser Klassen kombinatorisch interpretieren. Dafür verallgemeinern wir Odd-Set-Ungleichungen für das Matchingproblem unter Verwendung von Cliquen. Die Ungleichungen, die wir erhalten, sind gültig für Mengenpackungsprobleme in beliebigen Hypergraphen und haben eine klare kombinatorische Bedeutung. Die Analyse von Zufallsinstanzen erlaubt einen besseren Einblick in die Struktur von Hyperzuordnungen. Eine solche ausführliche Analyse wurde in der Literatur theoretisch und praktisch bereits für das Zuordnungsproblem durchgeführt. Als eine Verallgemeinerung dieser Ergebnisse für das HAP beweisen wir Schranken für den Erwartungswert einer Hyperzuordnung mit minimalen Kosten, die genau die Hälfte der maximal möglichen Anzahl an Hyperkanten, die keine Kanten sind, benutzt. In einem sog. vollständigen partitionierten Hypergraphen G2,2n mit Hyperkantenkosten, die durch unabhängig identisch exponentiell verteilte Zufallsvariablen mit Erwartungswert 1 bestimmt sind, liegt dieser Wert zwischen 0.3718 und 1.8310, wenn die Knotenanzahl gegen unendlich strebt. Schließlich entwickeln wir eine exakte kombinatorische Lösungsmethode für das HAP, die drei Ansätze kombiniert: Eine Nachbarschaftssuche mit Nachbarschaften exponentieller Größe, die Composite-Columns-Methode für das Mengenzerlegungsproblem sowie den Netzwerksimplexalgorithmus.

Cited by 1Open access

The frequency assignment problem

Noble, S, Koller, Angela Erika · OpenGrey (Institut de l'Information Scientifique et Technique) · 2004

This thesis examines a wide collection of frequency assignment problems. One of the largest topics in this thesis is that of L(2,1)-labellings of outerplanar graphs. The main result in this topic is the fact that there exists a polynomial time algorithm to determine the minimum L(2,1)-span for an outerplanar graph. This result generalises the analogous result for trees, solves a stated open problem and complements the fact that the problem is NP-complete for planar graphs. We furthermore give best possible bounds on the minimum L(2,1)-span and the cyclic-L(2,1)-span in outerplanar graphs, when the maximum degree is at least eight. We also give polynomial time algorithms for solving the standard constraint matrix problem for several classes of graphs, such as chains of triangles, the wheel and a larger class of graphs containing the wheel. We furthermore introduce the concept of one-close-neighbour problems, which have some practical applications. We prove optimal results for bipartite graphs, odd cycles and complete multipartite graphs. Finally we evaluate different algorithms for the frequency assignment problem, using domination analysis. We compute bounds for the domination number of some heuristics for both the fixed spectrum version of the frequency assignment problem and the minimum span frequency assignment problem. Our results show that the standard greedy algorithm does not perform well, compared to some slightly more advanced algorithms, which is what we would expect. In this thesis we furthermore give some background and motivation for the topics being investigated, as well as mentioning several open problems.

Cited by 0

The Biobjective Assignment Problem

Anthony Przybylski, Xavier Gandibleux, Matthias Ehrgott · HAL (Le Centre pour la Communication Scientifique Directe) · 2006

In this paper we present a synthesis of the two phase method for the biobjective assignment problem. The method, which is a general technique to solve multiobjective combinatorial optimization (MOCO) problems, has been introduced by Ulungu in 1993. However, no complete description of the method to find all efficient solutions of the biobjective assignment problem (that allows an independent implementation) has been published. First, we present a complete description of a two phase method for the biobjective assignment problem, with an improved upper bound. Second, a combination of this method with a population based heuristic using path relinking is proposed to improve computational performance. Third, we propose a new technique for the second phase with a ranking approach. All of the methods have been tested on instances of varying size and range of objective function coefficients. We discuss the obtained results and explain our observations based on the distribution of objective function values.

Cited by 0Open access

Structural Credit Assignment Problem

Supervised Learning · 2007

39> 46, 50 PatternPool 50 PoolToSampleTop 51 probability transition function 21, 34 propagation 45, 54, 64 pruning techniques 17 R recurrent backpropagation 28 reduction 45, 47, 52, 54, 65 regression 10 reinforcement learning 13 S Sample 51 sample parallelism 27, 41, 42, 44, 53 scalability 19 selection 54 self-organized learning 13 self-organizing discrete manifold 40 self-organizing map 37 sensitivity 19 Session 53 session parallelism 27, 42, 48 short cut 23 signum 8 simulated annealing 33, 62 simultaneous activation 9 stability 19 standard competition rule<F7

Cited by 0

Generalized sequential assignment problem

Arash Khatibi · IDEALS (University of Illinois Urbana-Champaign) · 2017

The Sequential Stochastic Assignment Problem (SSAP) deals with assigning sequentially arriving tasks with stochastic parameters to workers with fixed success rates. The reward of each assignment is the product of the worker's success rate and the task value assigned to the worker. The objective is to maximize the total expected reward. There has been a surge of interest in studying sequential assignment problems due to their applications in online matching markets, asset selling, and organ transplant.
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\nThis dissertation studies several variations of SSAP by relaxing the main assumptions. The first part assumes that the workers' success rates are random values coming from a known distribution. This generalization modifies the SSAP from a problem with a single random value (i.e., the task value) at each stage to an online matching problem with several random parameters (i.e., the task value and the workers' success rates). The optimal assignment policy uses backward induction to first solve smaller subproblems, and then use them to optimally assign tasks to workers from the first stage. An approximation algorithm is proposed that achieves a fraction of the optimal reward in a polynomial time. 
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\nAssuming that the value of sequentially arriving elements are independently drawn from a known distribution is unrealistic in many applications. The second part of thesis relaxes this assumption and uses the well-known Secretary Problem to derive constant-competitive algorithms for SSAP with tasks having a random arrival order. Several deterministic and randomized algorithms are proposed and their performance are compared with the maximum offline reward. These algorithms use the first stages of the problem as a training phase to compute thresholds for the task values. These thresholds are used to assign tasks to workers after the training phase. 
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\nThe third part uses the linear programming technique to derive bounds on the performance of optimal policy for several variations of SSAP. Formulating an online matching problem as a linear program is a useful tool. In addition to deriving bounds on performance of optimal policies, the linear programming technique can be used to formulate extensions of the problem as linear programs by simple changes in the objective function and constraints of the basic formulation. The linear programming formulation of the incentive compatible problem and the sequential assignment problem with unknown number of elements are also proposed. The edge-weighted online bipartite matching problem is used to design assignment policies for each of the formulated problems. 
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\nThe last part relaxes the assumption that at most one task must be assigned to each worker in SSAP. It is assumed that a worker is available for possible future assignments after performing the previously assigned task. The number of stages that the worker is not available due to a prior task assignment is referred to as the task duration. This problem is studied under various models for the task duration. First, it is assumed that the task duration is fixed. Then, assignment policies are proposed for the problem with a memoryless model for the task duration. The proposed algorithms are extensions of the optimal algorithm for the sequential assignment problem. They divide the n-stage assignment process to periods whose lengths are equal to the expected task duration. Then, they assign tasks to workers in each period by applying the optimal algorithm of the sequential assignment problem.

Cited by 0Open access

Fair congested assignment problem

Anna Bogomolnaia, Hervé Moulin · arXiv (Cornell University) · 2023

We propose a fair and efficient solution for assigning agents to m posts subject to congestion, when agents care about both their post and its congestion. Examples include assigning jobs to busy servers, students to crowded schools or crowded classes, commuters to congested routes, workers to crowded office spaces or to team projects etc... Congestion is anonymous (it only depends on the number n of agents in a given post). A canonical interpretation of ex ante fairness allows each agent to choose m post-specific caps on the congestion they tolerate: these requests are mutually feasible if and only if the sum of the caps is n. For ex post fairness we impose a competitive requirement close to envy freeness: taking the congestion profile as given each agent is assigned to one of her best posts. If a competitive assignment exists, it delivers unique congestion and welfare profiles and is also efficient and ex ante fair. In a fractional (randomised or time sharing) version of our model, a unique competitive congestion profile always exists. It is approximately implemented by a mixture of ex post deterministic assignments: with an approxination factor equal to the largest utility loss from one more unit of congestion, the latter deliver identical welfare profiles and are weakly efficient. Our approach to ex ante fairness generalises to the model where each agent's congestion is weighted. Now the caps on posts depend only upon own weight and total congestion, not on the number of other agents contributing to it. Remarkably in both models these caps are feasible if and only if they give to each agent the right to veto all but (1/m) of their feasible allocations.

Cited by 0Open access

Conference Paper Assignment Problem

Donald C. Conry, Edward A. Fox, Clifford A. Shaffer et al. · 2009

Conference paper assignment—the task of assigning paper submissions to reviewers—is a key step in the management and smooth functioning of conferences. We study this problem as an application of recommender systems research. Besides the traditional goal of predicting ‘who likes what?’, a conference management system must take into account reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of assignments. In this thesis, we present an integrated study of both aspects. First, due to the sparsity of data (relative to other recommender systems applications), we integrate multiple sources of information to learn reviewer/paper preference models, using methods commonly associated with merging content-based and collaborative filtering in the study of large recommender systems. Second, our models are evaluated not just in terms of prediction accuracy, but also in terms of end-assignment quality, and considering multiple evaluation criteria. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of potential assignments to maximize the overall affinities of papers assigned to reviewers. Finally, we demonstrate encouraging results on real reviewer preference data gathered during the IEEE ICDM 2007 conference, a premier international data mining conference. Our research demonstrates that there are significant advantages to applying recommender system concepts to the conference paper assignment problem. Acknowledgements

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