Relevant bibliographies by topics / Hungarian Algorithm for Assignment Problem

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Hungarian Algorithm for Assignment Problem'

Author: Grafiati
Published: 3 June 2025
Last updated: 11 July 2025

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Journal articles on the topic "Hungarian Algorithm for Assignment Problem"

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Gabrovšek, Boštjan, Tina Novak, Janez Povh, Darja Rupnik Poklukar, and Janez Žerovnik. "Multiple Hungarian Method for k-Assignment Problem." Mathematics 8, no. 11 (2020): 2050. http://dx.doi.org/10.3390/math8112050.

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The k-assignment problem (or, the k-matching problem) on k-partite graphs is an NP-hard problem for k≥3. In this paper we introduce five new heuristics. Two algorithms, Bm and Cm, arise as natural improvements of Algorithm Am from (He et al., in: Graph Algorithms And Applications 2, World Scientific, 2004). The other three algorithms, Dm, Em, and Fm, incorporate randomization. Algorithm Dm can be considered as a greedy version of Bm, whereas Em and Fm are versions of local search algorithm, specialized for the k-matching problem. The algorithms are implemented in Python and are run on three datasets. On the datasets available, all the algorithms clearly outperform Algorithm Am in terms of solution quality. On the first dataset with known optimal values the average relative error ranges from 1.47% over optimum (algorithm Am) to 0.08% over optimum (algorithm Em). On the second dataset with known optimal values the average relative error ranges from 4.41% over optimum (algorithm Am) to 0.45% over optimum (algorithm Fm). Better quality of solutions demands higher computation times, thus the new algorithms provide a good compromise between quality of solutions and computation time.
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Cheng, Yuan Zeng, Pei Chao Zhang, and Bin Qian Cao. "Weapon Target Assignment Problem Solving Based on Hungarian Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 2041–44. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2041.

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Weapon target assignment problem is most critical in modern warfare command decision of a problem for the weapon system and a relatively small number of targets assignment problem, you can use the Hungarian algorithm. Hungarian algorithm can solve the assignment problem, but under normal circumstances, weapon target assignment problem does not have the form of a mathematical model of assignment problem, through dummy weapon system or target method, the weapon target assignment problem is transformed into a standard assignment problem, and then solved by the Hungarian algorithm.
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Amalia, Ivanda Zevi, Ahmad Saikhu, and Rully Soelaiman. "A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems." Jurnal Online Informatika 6, no. 1 (2021): 118. http://dx.doi.org/10.15575/join.v6i1.692.

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The assignment problem is one of the fundamental problems in the field of combinatorial optimization. The Hungarian algorithm can be developed to solve various assignment problems according to each criterion. The assignment problem that is solved in this paper is a dynamic assignment to find the maximum weight on the resource allocation problems. The dynamic characteristic lies in the weight change that can occur after the optimal solution is obtained. The Hungarian algorithm can be used directly, but the initialization process must be done from the beginning every time a change occurs. The solution becomes ineffective because it takes up a lot of time and memory. This paper proposed a fast dynamic assignment algorithm based on the Hungarian algorithm. The proposed algorithm is able to obtain an optimal solution without performing the initialization process from the beginning. Based on the test results, the proposed algorithm has an average time of 0.146 s and an average memory of 4.62 M. While the Hungarian algorithm has an average time of 2.806 s and an average memory of 4.65 M. The fast dynamic assignment algorithm is influenced linearly by the number of change operations and quadratically by the number of vertices.
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Elias, Munapo. "DEVELOPMENT OF AN ACCELERATING HUNGARIAN METHOD FOR ASSIGNMENT PROBLEMS." Eastern-European Journal of Enterprise Technologies 4, no. 4 (106) (2020): 6–13. https://doi.org/10.15587/1729-4061.2020.209172.

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The Hungarian method is a well-known method for solving the assignment problem. This method was developed and published in 1955. It was named the Hungarian method because two theorems from two Hungarian mathematicians were used. In 1957, it was noticed that this algorithm is strongly polynomial and has a complexity of order O(n<sup>4</sup>) This is the reason why the Hungarian method is also known as the Kuhn-Munkres algorithm. Later on, in 1971 the complexity of the method was improved to order O(n<sup>3</sup>) A smallest uncovered element is selected to create a single zero at every iteration. This is a weakness and is alleviated by selecting more than one smallest uncovered element thus creating more than one zero at every iteration to come up with what we now call the Accelerating Hungarian (AH) method. From the numerical illustration of the Hungarian method given in this paper, we require 6 iterations to reach optimality. It can also be shown that selecting a single smallest uncovered element (e<sub>s</sub>) makes the Hungarian method inefficient when creating zeros. From the same numerical illustration of the proposed algorithm (AH) also given in this paper, it can be noted that only one iteration is required to reach optimality and that a total of six zeros are created in one iteration. Assignment model and the Hungarian method have application in addressing the Weapon Target Assignment (WTA) problem. This is the problem of assigning weapons to targets while considering the maximum probability of kill. The assignment problem is also used in the scheduling problem of physicians and medical staff in the outpatient department of large hospitals with multi-branches. The mathematical modelling of this assignment problem results in complex problems. A hybrid meta-heuristic algorithm SCA&ndash;VNS combining a Sine Cosine Algorithm (SCA) and Variable Neighbourhood Search (VNS) based on the Iterated Hungarian algorithm is normally used
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Jyoti, Arora, and Sharma Surbhi. "Solving Task Assignment Problem Using Branch and Bound Method." Journal of Applied Mathematics and Statistical Analysis 4, no. 1 (2023): 1–7. https://doi.org/10.5281/zenodo.7813430.

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<em>The main aim of this paper is to present an algorithm of Branch and Bound method for solving task assignment problem. The branch and bound approach is based on divide and conquer method in which large problem is divided into smaller subsets of solutions. Our algorithm calculates lower bounds on solutions to the Task Assignment problem, which is further divided into subsets. Experimental results show that this Branch and Bound method to solve Task Assignment problem shows better results than standard Hungarian Method. A Numerical example is taken to illustrate the solution procedure</em>
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Mie, Mie Aung, Yin Cho Yin, Htay Khin, and Soe Myint Khin. "Minimization of Assignment Problems." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1360–62. https://doi.org/10.5281/zenodo.3590803.

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The assignment problem is a special type of linear programming problem and it is sub class of transportation problem. Assignment problems are defined with two sets of inputs i.e. set of resources and set of demands. Hungarian algorithm is able to solve assignment problems with precisely defined demands and resources.Nowadays, many organizations and competition companies consider markets of their products. They use many salespersons to improve their organizations marketing. Salespersons travel form one city to another city for their markets. There are some problems in travelling which salespeople should go which city in minimum cost. So, travelling assignment problem is a main process for many business functions. Mie Mie Aung | Yin Yin Cho | Khin Htay | Khin Soe Myint &quot;Minimization of Assignment Problems&quot; Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26712.pdf
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Vishal, Mehta, and Saxena Rajendra. "Role of the Assignment Problem in Resource Optimization: A Case Study of a Clothing manufacturing company." Career Point International Journal of Research(CPIJR) 4, no. 3 (2025): 7–11. https://doi.org/10.5281/zenodo.15054398.

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Mathematics, particularly optimization techniques, plays a crucial role in management, helping to allocate resources efficiently. This paper explores the use of the Assignment Problem in operations management, where products or tasks must be assigned to resources such as workers, machines, or locations. Through a case study of a clothing manufacturing company, we demonstrate how the Assignment Problem can optimize production assignments for different machines to minimize overall production costs. The Hungarian algorithm is applied to solve the problem.
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R.Jeba, Mary, and Devi K.Aasha. "Aberrant Method of Solving the Assignment Problem." Journal of Statistics and Mathematical Engineering 4, no. 3 (2018): 8–13. https://doi.org/10.5281/zenodo.1477228.

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Assignment downside may be a specific case of the transportation downside. It helps as to minimizing the time or cost of manufacturing the products by allocating one job to one person or one person to one job or one destination to one origin or one origin to one destination only. Normally, assignment model is a minimization model. In this article we have a tendency to initiate new technique to resolve assignment downside referred to as aberrant technique of determination assignment downside. By solving an assignment problem using aberrant method of solving assignment problem and Hungarian method and compared it&rsquo;s results.
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VIZUETE-LUCIANO, Emili, José M. MERIGÓ, Anna M. GIL-LAFUENTE, and Sefa BORIA-REVERTER. "DECISION MAKING IN THE ASSIGNMENT PROCESS BY USING THE HUNGARIAN ALGORITHM WITH OWA OPERATORS." Technological and Economic Development of Economy 21, no. 5 (2015): 684–704. http://dx.doi.org/10.3846/20294913.2015.1056275.

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Assignment processes permit to coordinate two set of variables so each variable of the first set is connected to another variable of the second set. This paper develops a new assignment algorithm by using a wide range of aggregation operators in the Hungarian algorithm. A new process based on the use of the ordered weighted averaging distance (OWAD) operator and the induced OWAD (IOWAD) operator in the Hungarian algorithm is introduced. We refer to it as the Hungarian algorithm with the OWAD operator (HAOWAD) and the Hungarian algorithm with the IOWAD operator (HAIOWAD). The main advantage of this approach is that we can provide a parameterized family of aggregation operators between the minimum and the maximum. Thus, the information can be represented in a more complete way. Furthermore, we also present a general framework by using generalized and quasi-arithmetic means. Therefore, we can consider a wide range of particular cases including the Euclidean and the Minkowski distance. The paper ends with a practical application of the new approach in a financial decision making problem regarding the assignment of investments.
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Yadav, Satyendra Singh, Paulo Alexandre Crisóstomo Lopes, Aleksandar Ilic, and Sarat Kumar Patra. "Hungarian algorithm for subcarrier assignment problem using GPU and CUDA." International Journal of Communication Systems 32, no. 4 (2018): e3884. http://dx.doi.org/10.1002/dac.3884.

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Dissertations / Theses on the topic "Hungarian Algorithm for Assignment Problem"

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Wikström, Anders. "Resource allocation of drones flown in a simulated environment." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105379.

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In this report we compare three different assignment algorithms in how they can be used to assign a set of drones to get to a set of goal locations in an as resource efficient way as possible. An experiment is set up to compare how these algorithms perform in a somewhat realistic simulated environment. The Robot Operating system (ROS) is used to create the experimental environment. We found that by introducing a threshold for the Hungarian algorithm we could reduce the total time it takes to complete the problem while only sightly increasing total distance traversed by the drones.
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HIRATA, Tomio, and Takao ONO. "An Improved Algorithm for the Net Assignment Problem." Institute of Electronics, Information and Communication Engineers, 2001. http://hdl.handle.net/2237/15062.

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Koller, Angela Erika. "The frequency assignment problem." Thesis, Brunel University, 2004. http://bura.brunel.ac.uk/handle/2438/4967.

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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.
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Arslanoglu, Yilmaz. "Genetic Algorithm For Personnel Assignment Problem With Multiple Objectives." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606880/index.pdf.

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This thesis introduces a multi-objective variation of the personnel assignment problem, by including additional hierarchical and team constraints, which put restrictions on possible matchings of the bipartite graph. Besides maximization of summation of weights that are assigned to the edges of the graph, these additional constraints are also treated as objectives which are subject to minimization. In this work, different genetic algorithm approaches to multi-objective optimization are considered to solve the problem. Weighted Sum &ndash<br>a classical approach, VEGA - a non-elitist multi-objective evolutionary algorithm, and SPEA &ndash<br>a popular elitist multi-objective evolutionary algorithm, are considered as means of solution to the problem, and their performances are compared with respect to a number of multi-objective optimization criteria.
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Qi, Xin, and Lan Xu. "The Application of Genetic Algorithm in Teaching Assignment Problem." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/271938.

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Genetic Algorithm is a search and optimization technique that is based on the principle of natural selection. In this thesis, a genetic algorithm is used to solve the teacher assignment problem. The result shows that the genetic algorithm is an efficient method to solve teacher assignment problem.
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Gulek, Mehmet. "Assignment Problem And Its Variations." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609145/index.pdf.

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We investigate the assignment problem, which is the problem of matching two sets with each other, optimizing a given function on the possible matchings. Among different definitions, a graph theoretical definition of the linear sum assignment problem is as follows: Given a weighted complete bipartite graph, find a maximum (or minimum) one-to-one matching between the two equal-size sets of the graph, where the score of a matching is the total weight of the matched edges. We investigate extensions and variations like the incremental assignment problem, maximum subset matching problem, maximum-weighted tree matching problem. We present a genetic algorithm scheme for maximum-weighted tree matching problem, and experimental results of our implementation.
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Brown, Eric L. "A quadratic partial assignment and packing model and algorithm for the airline gate assignment problem." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-07212009-040541/.

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Maach, Fouad. "Bi-objective multi-assignment capacitated location-allocation problem." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/31558.

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Optimization problems of location-assignment correspond to a wide range of real situations, such as factory network design. However most of the previous works seek in most cases at minimizing a cost function. Traffic incidents routinely impact the performance and the safety of the supply. These incidents can not be totally avoided and must be regarded. A way to consider these incidents is to design a network on which multiple assignments are performed. <p> Precisely, the problem we focus on deals with power supplying that has become a more and more complex and crucial question. Many international companies have customers who are located all around the world; usually one customer per country. At the other side of the scale, power extraction or production is done in several sites that are spread on several continents and seas. A strong willing of becoming less energetically-dependent has lead many governments to increase the diversity of supply locations. For each kind of energy, many countries expect to deal ideally with 2 or 3 location sites. As a decrease in power supply can have serious consequences for the economic performance of a whole country, companies prefer to balance equally the production rate among all sites as the reliability of all the sites is considered to be very similar. Sharing equally the demand between the 2 or 3 sites assigned to a given area is the most common way. Despite the cost of the network has an importance, it is also crucial to balance the loading between the sites to guarantee that no site would take more importance than the others for a given area. In case an accident happens in a site or in case technical problems do not permit to satisfy the demand assigned to the site, the overall power supply of this site is still likely to be ensured by the one or two available remaining site(s). It is common to assign a cost per open power plant and another cost that depends on the distance between the factory or power extraction point and the customer. On the whole, such companies who are concerned in the quality service of power supply have to find a good trade-off between this factor and their overall functioning cost. This situation exists also for companies who supplies power at the national scale. The expected number of areas as well that of potential sites, can reach 100. However the targeted size of problem to be solved is 50. <p> This thesis focuses on devising an efficient methodology to provide all the solutions of this bi-objective problem. This proposal is an investigation of close problems to delimit the most relevant approaches to this untypical problem. All this work permits us to present one exact method and an evolutionary algorithm that might provide a good answer to this problem.<br>Master of Science
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Macdonald, Edward A. "Multi-robot assignment and formation control." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41200.

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Our research focuses on one of the more fundamental issues in multi-agent, mobile robotics: the formation control problem. The idea is to create controllers that cause robots to move into a predefined formation shape. This is a well studied problem for the scenario in which the robots know in advance to which point in the formation they are assigned. In our case, we assume this information is not given in advance, but must be determined dynamically. This thesis presents an algorithm that can be used by a network of mobile robots to simultaneously determine efficient robot assignments and formation pose for rotationally and translationally invariant formations. This allows simultaneous role assignment and formation sysnthesis without the need for additional control laws. The thesis begins by introducing some general concepts regarding multi-agent robotics. Next, previous work and background information specific to the formation control and assignment problems are reviewed. Then the proposed assignment al- gorithm for role assignment and formation control is introduced and its theoretical properties are examined. This is followed by a discussion of simulation results. Lastly, experimental results are presented based on the implementation of the assignment al- gorithm on actual robots.
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Karabulut, Ozlem. "Multi Resource Agent Bottleneck Generalized Assignment Problem." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611790/index.pdf.

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In this thesis, we consider the Multi Resource Agent Bottleneck Generalized Assignment Problem. We aim to minimize the maximum load over all agents. We study the Linear Programming (LP) relaxation of the problem. We use the optimal LP relaxation solutions in our Branch and Bound algorithm while defining lower and upper bounds and branching schemes. We find that our Branch and Bound algorithm returns optimal solutions to the problems with up to 60 jobs when the number of agents is 5, and up to 30 jobs when the number of agents is 10, in less than 20 minutes. To find approximate solutions, we define a tabu search algorithm and an &amp<br>#945<br>approximation algorithm. Our computational results have revealed that these procedures can find high quality solutions to large sized instances very quickly.
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Book chapters on the topic "Hungarian Algorithm for Assignment Problem"

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Shah, Kartik, Praveenkumar Reddy, and S. Vairamuthu. "Improvement in Hungarian Algorithm for Assignment Problem." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2126-5_1.

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Gil-Aluja, Jaime. "The Hungarian assignment algorithm." In Applied Optimization. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3329-6_24.

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Kuhn, Harold W. "The Hungarian Method for the Assignment Problem." In 50 Years of Integer Programming 1958-2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-68279-0_2.

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Prabha, S. Krishna, and S. Vimala. "Neutrosophic Assignment Problem via BnB Algorithm." In Trends in Mathematics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01120-8_37.

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Yuan, Mindi, Chong Jiang, Shen Li, Wei Shen, Yannis Pavlidis, and Jun Li. "Message Passing Algorithm for the Generalized Assignment Problem." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44917-2_35.

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Gutin, Gregory, and Daniel Karapetyan. "A Memetic Algorithm for the Multidimensional Assignment Problem." In Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03751-1_12.

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Paldrak, Mert, and Mustafa Arslan Örnek. "Whale Optimization Algorithm for Airport Gate Assignment Problem." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24457-5_39.

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Achatz, H., P. Kleinschmidt, and K. Paparrizos. "A dual forest algorithm for the assignment problem." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, 1991. http://dx.doi.org/10.1090/dimacs/004/01.

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Salami, Mehrdad, and Greg Cain. "Genetic Algorithm Processor for the Frequency Assignment Problem." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-42.

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Wisittipanich, Warisa, and Pongsakorn Meesuk. "Differential Evolution Algorithm for Storage Location Assignment Problem." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47200-2_29.

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Conference papers on the topic "Hungarian Algorithm for Assignment Problem"

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Li, Jixu, Li Yang, Jinwen Wang, and Liu He. "Based on Subtraction-Average Assignment Optimization Algorithm for Weapon Target Assignment Problem." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661824.

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Moftah, Mohammad E., and Mohamed Ghoneimy. "Solving Hospital Layout Problem as Quadratic Assignment Problem by Marine Predators Algorithm." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783568.

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Ghasempour Shirazi, S. A. "Fixed Channel Assignment Problem Using a New Hybrid Algorithm." In 2006_EMC-Europe_Barcelona. IEEE, 2006. https://doi.org/10.23919/emc.2006.10813271.

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Shopov, Ventseslav Kirilov, and Vanya Dimitrova Markova. "Application of Hungarian Algorithm for Assignment Problem." In 2021 International Conference on Information Technologies (InfoTech). IEEE, 2021. http://dx.doi.org/10.1109/infotech52438.2021.9548600.

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Handojo, Andreas, Nyoman Pujawan, Budi Santosa, and Moses Laksono Singgih. "Job Assignment Problem on Online Transportation Order Using Hungarian Algorithm." In 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2022. http://dx.doi.org/10.1109/icsintesa56431.2022.10041526.

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Shi, Yuyang, and Yajun Mei. "Efficient Sequential UCB-based Hungarian Algorithm for Assignment Problems." In 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2022. http://dx.doi.org/10.1109/allerton49937.2022.9929380.

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Gungor, I., and M. Gunes. "Fuzzy multiple criteria assignment problems for fusion: the case of Hungarian algorithm." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.862706.

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Selmair, Maximilian, Sascha Hamzehi, and Klaus-Juergen Meier. "Evaluation Of Algorithm Performance For Simulated Square And Non-Square Logistic Assignment Problems." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0016.

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The optimal allocation of transportation tasks to a fleet of vehicles, especially for large-scale systems of more than 20 Autonomous Mobile Robots (AMRs), remains a major challenge in logistics. Optimal in this context refers to two criteria: how close a result is to the best achievable objective value and the shortest possible computational time. Operations research has provided different methods that can be applied to solve this assignment problem. Our literature review has revealed six commonly applied methods to solve this problem. In this paper, we compared three optimal methods (Integer Linear Programming, Hungarian Method and the Jonker Volgenant Castanon algorithm) to three three heuristic methods (Greedy Search algorithm, Vogel’s Approximation Method and Vogel’s Approximation Method for non-quadratic Matrices). The latter group generally yield results faster, but were not developed to provide optimal results in terms of the optimal objective value. Every method was applied to 20.000 randomised samples of matrices, which differed in scale and configuration, in simulation experiments in order to determine the results’ proximity to the optimal solution as well as their computational time. The simulation results demonstrate that all methods vary in their time needed to solve the assignment problem scenarios as well as in the respective quality of the solution. Based on these results yielded by computing quadratic and non-quadratic matrices of different scales, we have concluded that the Jonker Volgenant Castanon algorithm is deemed to be the best method for solving quadratic and non-quadratic assignment problems with optimal precision. However, if performance in terms of computational time is prioritised for large non-quadratic matrices (50×300 and larger), the Vogel’s Approximation Method for non-quadratic Matrices generates faster approximated solutions.
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Xiao, Shanzhu, Huamin Tao, Luping Zhang, and Xinglin Shen. "DP-TBD algorithm based on Hungarian assignment algorithm." In Workshop on Electronics Communication Engineering (WECE 2023), edited by Weilin Xu. SPIE, 2024. http://dx.doi.org/10.1117/12.3015991.

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Makohon, Ivan, Mecit Cetin, Duc T. Nguyen, and ManWo Ng. "Hungarian optimum assignment algorithm with Java computer animation." In SoutheastCon 2016. IEEE, 2016. http://dx.doi.org/10.1109/secon.2016.7506731.

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Reports on the topic "Hungarian Algorithm for Assignment Problem"

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Balas, Egon, and Matthew J. Saltzman. An Algorithm for the Three-Index Assignment Problem. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada205172.

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Balas, Egon, Donald Miller, Joseph Pekny, and Paolo Toth. A Parallel Shortest Augmenting Path Algorithm for the Assignment Problem. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada233588.

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