Relevant bibliographies by topics / Unbalanced Assignment Problem (UBAP)

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Academic literature on the topic 'Unbalanced Assignment Problem (UBAP)'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Unbalanced Assignment Problem (UBAP)"

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M.Venkatachalapathy, S.Venkatesh R.Neelambari S.Revathi S.Anupriya. "An Optimal Solution for Balanced and Unbalanced Assignment Problems under Crisp and Trapezoidal Fuzzy Numbers Using Row Minimum Allocation Method." Multicultural Education 7, no. 9 (2021): 168. https://doi.org/10.5281/zenodo.5500716.

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<em>In this paper , to solving an Balanced and Unbalanced Assignment Problem. This process involves providing an IBFS optimality. I introduce the Row Minimum Allocation Method (RMAM) in order to maintain an optimum balalanced and&nbsp; unbalanced&nbsp; assignment problems. To less reductions and steps we get the optimum. The proposed algorithms are clear to understand and easy to access when it comes to discussing the trapezoidal fuzzy number balanced and unbalanced assignment problems in real life situations.</em>
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Gowri., C., and K. M. Manikandan. "Mobile Phone Selection Problem Solved by an Unbalanced Assignment Model by using ROA Method with Ghadle and Muley Rule in Mat Lab." International Journal of Trend in Scientific Research and Development 3, no. 6 (2019): 1065–71. https://doi.org/10.5281/zenodo.3589333.

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Assignment problem is one of the most important in the theory of optimization in operation research, in which a group of workers has to perform a set of task. The problem is to assign each worker to a distinct task so as to minimize the total cost. In this paper we discussed about an application of an unbalanced assignment problem by using revised ones assignment method with Ghadle and Muley rule in Matlab coding. Mobile phones are assigning one - to -one assignment for the required users at minimum cost. Gowri. C | Manikandan. K. M &quot;Mobile Phone Selection Problem Solved by an Unbalanced Assignment Model by using ROA-Method with Ghadle &amp; Muley Rule in Mat Lab&quot; Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29324.pdf
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Betts, Nathan, and Francis J. Vasko. "Solving the Unbalanced Assignment Problem: Simpler Is Better." American Journal of Operations Research 06, no. 04 (2016): 296–99. http://dx.doi.org/10.4236/ajor.2016.64028.

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Jain, Ekta, Kalpana Dahiya, and Vanita Verma. "A priority based unbalanced time minimization assignment problem." OPSEARCH 57, no. 1 (2019): 13–45. http://dx.doi.org/10.1007/s12597-019-00399-8.

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Khandelwal, Anju. "An amalgamated approach for solving unbalanced assignment problem." Malaya Journal of Matematik 06, no. 02 (2018): 321–25. http://dx.doi.org/10.26637/mjm0602/0003.

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Prakash, A., Uruturu Balakrishna, and Jayanth Kumar Thenepalle. "An exact algorithm for constrained k-cardinality unbalanced assignment problem." International Journal of Industrial Engineering Computations 13, no. 2 (2022): 267–76. http://dx.doi.org/10.5267/j.ijiec.2021.10.002.

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An assignment problem (AP) usually deals with how a set of persons/tasks can be assigned to a set of tasks/persons on a one-to-one basis in an optimal manner. It has been observed that balancing among the persons and jobs in several real-world situations is very hard, thus such scenarios can be seen as unbalanced assignment models (UAP) being a lack of workforce. The solution techniques presented in the literature for solving UAP’s depend on the assumption to allocate some of the tasks to fictitious persons; those tasks assigned to dummy persons are ignored at the end. However, some situations in which it is inevitable to assign more tasks to a single person. This paper addresses a practical variant of UAP called k-cardinality unbalanced assignment problem (k-UAP), in which only of persons are asked to perform jobs and all the persons should perform at least one and at most jobs. The k-UAP aims to determine the optimal assignment between persons and jobs. To tackle this problem optimally, an enumerative Lexi-search algorithm (LSA) is proposed. A comparative study is carried out to measure the efficiency of the proposed algorithm. The computational results indicate that the suggested LSA is having the great capability of solving the smaller and moderate instances optimally.
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Mondal, Ranjan Kumar, Payel Ray, Enakshmi Nandi, Biswajit.Biswas, Manas Kumar Sanyal, and Debabrata Sarddar. "Load Balancing of Unbalanced Assignment Problem With Hungarian Method." International Journal of Ambient Computing and Intelligence 10, no. 1 (2019): 46–60. http://dx.doi.org/10.4018/ijaci.2019010103.

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The cloud computing presents a type of assignments and systems which occupy distributed resources to execute a role in a distributed way. Cloud computing make use of the online systems on the web to assist the implementation of complicated assignments; that need huge-scale computation. It was said with the intention of in our living world; we can find it challenging to balance workloads of cloud computing among assignments (jobs or tasks) and systems (machines or nodes), so the majority of the time we have to promote a condition to unbalanced assignment problems (unequal task allocations). The present article submits a new technique to solve the unequal task allocation problems. The technique is offered in an algorithmic model and put into practice on the several groups of input to investigate the presentation and usefulness of the works. An evaluation is prepared with the presented approach. It makes sure that the proposed approach provides a better outcome by comparing with some other existing algorithms.
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You, Jiangwei, Jianfang Jia, Xiaoqiong Pang, Jie Wen, Yuanhao Shi, and Jianchao Zeng. "A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy." Electronics 12, no. 18 (2023): 3842. http://dx.doi.org/10.3390/electronics12183842.

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A practical task assignment is one of the core issues of a multi-robot system. In this paper, a multi-robot task assignment strategy based on load balancing is proposed to effectively balance and plan out the execution cost of each robot when it has a large number of working task points. Considering the variability of the execution task cost in practical situations with different task point categories, the multi-robot task assignment (MRTA) problem is transformed into a multiple traveling salesman problem (MTSP) using a multi-angle K-means clustering algorithm. To solve the problem of unbalanced cost consumed by each robot after clustering assignment, which leads to low efficiency of system completion, a two-stage load-balancing strategy is presented. The first stage of this strategy makes a large adjustment to the unbalanced task set, and the second stage achieves a small fine-tuning to the unbalanced task set. The experimental results show that the standard deviation of the cost ratio of each set decreases when four robots perform the task between 100 and 550 work points using the load-balancing strategy. The reduction in standard deviation is between 3.53% and 83.44%. The maximum cost of individual robots decreases between 0.18% and 14.27%. The proposed method can effectively solve the uneven execution cost of each robot in the task assignment process and improve the efficiency of the system in completing tasks.
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Wang, Wenfei, Maolong Lv, Le Ru, Bo Lu, Shiguang Hu, and Xinlong Chang. "Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases." Aerospace 9, no. 9 (2022): 491. http://dx.doi.org/10.3390/aerospace9090491.

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Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.
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EVIPANIA, RENALDO, G. K. GANDHIADI, and I. WAYAN SUMARJAYA. "OPTIMALISASI MASALAH PENUGASAN TIDAK SEIMBANG MENGGUNAKAN MODIFIED HUNGARIAN METHOD." E-Jurnal Matematika 10, no. 1 (2021): 26. http://dx.doi.org/10.24843/mtk.2021.v10.i01.p316.

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Assignment problem is one of the cases that found in linear programming. Assignment problem is related to the allocation of workers for available jobs. From several sources, Hungarian method is more often used to solve the assignment problem. In the Hungarian method, if an unbalanced problem is found, the lack of the source or destination will be added to the dummy variable so that the case becomes balanced. The jobs that executed on the dummy machine will be ignored. In real-life situations, it is impossible for companies to ignore existing work because of a lack of workers. Therefore, the Hungarian method is modified to resolve this condition, so that the total assignment cost can still be optimized.
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Book chapters on the topic "Unbalanced Assignment Problem (UBAP)"

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Vera Ramos José Antonio, Kreuzthaler Markus, and Schulz Stefan. "Supervised ICD Code Assignment to Short Clinical Problem List Entries." In Studies in Health Technology and Informatics. IOS Press, 2019. https://doi.org/10.3233/978-1-61499-959-1-184.

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Clinical information systems contain free-text entries in different contexts to be used in a variety of application scenarios. In this study we investigate to what extent diagnosis codes using the disease classification system ICD-10 can be automatically post-assigned to patient-based short problem list entries, (50 characters maximum). Classifiers using random forest and Adaboost performed best with an F-measure of 0.87 and 0.85 running against an unbalanced data set, and an F-measure of 0.88 and 0.94 using a balanced data set, respectively.
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