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

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Published: 4 June 2025

Last updated: 15 July 2025

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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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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  unbalanced  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.

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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 "Mobile Phone Selection Problem Solved by an Unbalanced Assignment Model by using ROA-Method with Ghadle & Muley Rule in Mat Lab" 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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Yadaiah, Ventepaka, and V. V. Haragopal. "A New Approach of Solving Single Objective Unbalanced Assignment Problem." American Journal of Operations Research 06, no. 01 (2016): 81–89. http://dx.doi.org/10.4236/ajor.2016.61011.

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Rabbani, Quazzafi, Aamir Khan, and Abdul Quddoos. "Modified Hungarian method for unbalanced assignment problem with multiple jobs." Applied Mathematics and Computation 361 (November 2019): 493–98. http://dx.doi.org/10.1016/j.amc.2019.05.041.

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Majumdar, Jayanta, and Asoke Kumar Bhunia. "An alternative approach for unbalanced assignment problem via genetic algorithm." Applied Mathematics and Computation 218, no. 12 (2012): 6934–41. http://dx.doi.org/10.1016/j.amc.2011.12.070.

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Piper, Diana, Karin Schiecke, Herbert Witte, and Lutz Leistritz. "Comparative study of methods for solving the correspondence problem in EMD applications." Current Directions in Biomedical Engineering 2, no. 1 (2016): 225–28. http://dx.doi.org/10.1515/cdbme-2016-0050.

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AbstractWe address the correspondence problem which arises when applying empirical mode decomposition (EMD) to multi-trial and multi-subject data. EMD decomposes a signal into a set of narrow-band components named intrinsic mode functions (IMFs). The number of IMFs and their signal properties can be different between trials, channels and subjects. In order to assign IMFs with similar characteristics to each other, we compare two assignment methods, unbalanced assignment and k-cardinality assignment and two clustering algorithms, namely hierarchical clustering and density-based spatial clustering of applications with noise based on heart rate variability data of children with temporal lobe epilepsy.

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Khandelwal, Anju. "A Fuzzy Assignment Approach: Categorically for Selection Technique." SRMS Journal of Mathmetical Science 3, no. 1 (2017): 35–41. http://dx.doi.org/10.29218/srmsjoms.v3i3.12954.

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The recruitment process in any departments or organizations is usually decided by traditional criteria. Lacking of theoretical basis, it is often difficult to achieve the desired result. In this paper we use triangular fuzzy number in delegation of recruitment process with the help of modified approach of assignment. For finding the optimal assignment, we make the balanced assignment problem, if unbalanced is given, then it is transformed into crisp assignment problem in linear programming by using the Robust’s ranking method. Finally the solution is obtained by modified method of assignment. This method is easier than the existing technique i.e. easier than the Hungarian Method and by this approach the numbers of iterations are reduced so that time taken by this method is less than the Hungarian method

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K. Senbagam. "Solving Fuzzy Assignment Problem of Pythagorean Fuzzy Numbers Exploiting a Software Tool." Communications on Applied Nonlinear Analysis 31, no. 2 (2024): 142–55. http://dx.doi.org/10.52783/cana.v31.526.

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A fresh approach to computing assignment issues in the pythagorean fuzzy domain is the focus of this body of work. Pythagorean fuzzy numbers are utilised in this work for the sake of analysing working hours and working costs. For converting fuzzy sets integers into a crisp number, a new ranking process is used. Optimal solutions have to be found by using software techniques for minimization, maximization and unbalanced assignment problems. In the end, numerical examples are being solved by using the proposed technique.

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Murugesan, R. "IDI-75 - AN OPTIMAL ONES ASSIGNMENT METHOD FOR SOLVING UNBALANCED ASSIGNMENT PROBLEMS." International Journal of Advanced Research 10, no. 07 (2022): 509–19. http://dx.doi.org/10.21474/ijar01/15066.

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In this article, a new technique namely IDI-75 (named to commemorate the 75th Independence Day of India), which is a simplified form of the existing Ones Assignment Method (OAM) of solving assignment problems, finds the optimal solution to a given unbalanced assignment problem (UAP). In the OAM, it is required to convert the given UAP into a balanced one by introducing one dummy row or column with 1 unit of effectiveness in each cell. But, the IDI-75 technique can be applied directly on the given UAP without converting it into a balanced one. The technique requires only row minimum division operation or only column minimum division operation depending on the size of rows and columns in order to have at least one 1-entry in each of the required number of rows and/or columns only. The assignments are made on the appropriate 1-entry cells of reduced ratio of costs matrix. To test the validity and effectiveness of the IDI-75 technique, 20 benchmark instances with different sizes from the literatures have been tested. Simulation results authenticate that the technique IDI-75 is the best one which produces optimal solution to all 20 instances. Therefore, it is clever to apply the IDI-75 technique to solve the UAPs as it is very simple, easy to understand, easy to apply and consume less time in comparison to the existing OAM.

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Bhunia, Asoke Kumar, Amiya Biswas, and Subhra Sankha Samanta. "A genetic algorithm-based approach for unbalanced assignment problem in interval environment." International Journal of Logistics Systems and Management 27, no. 1 (2017): 62. http://dx.doi.org/10.1504/ijlsm.2017.083222.

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Biswas, Amiya, Subhra Sankha Samanta, and Asoke Kumar Bhunia. "A genetic algorithm-based approach for unbalanced assignment problem in interval environment." International Journal of Logistics Systems and Management 27, no. 1 (2017): 62. http://dx.doi.org/10.1504/ijlsm.2017.10003920.

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Sadeghi Naieni Fard, F., B. Naderi, and A. A. Akbari. "Production Scheduling and Customer Orders Assignment in a Three-Level Supply Chain." ISRN Industrial Engineering 2014 (January 28, 2014): 1–12. http://dx.doi.org/10.1155/2014/163637.

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In the classical production-distribution centers problem, only assignment of customers, distribution centers, and suppliers is determined. This paper extends the problem of production-distribution centers assignment by considering sequencing decisions in the supply network. Nowadays, meeting delivery time of products is a competitive benefit; therefore, the objective is to minimize total tardiness. This problem is mathematically formulated by a mixed integer programming model. Then, using the proposed model, small instances of the problem can be optimally solved by GAMS software. Moreover, two metaheuristics based on variable neighborhood search and simulated annealing are proposed to solve large instances of the problem. Finally, performance of the proposed metaheuristics is evaluated by two sets of balanced and unbalanced instances. The computational results show the superiority of the variable neighborhood search algorithm.

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Vaidyanathan, Balachandran. "Faster strongly polynomial algorithms for the unbalanced transportation problem and assignment problem with monge costs." Networks 62, no. 2 (2013): 136–48. http://dx.doi.org/10.1002/net.21507.

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Ramesh, G., G. Sudha, and K. Ganesan. "Method of finding an optimal solution for interval balanced and unbalanced assignment problem." IOP Conference Series: Materials Science and Engineering 912 (September 12, 2020): 062031. http://dx.doi.org/10.1088/1757-899x/912/6/062031.

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Thenepalle, Jayanth Kumar, and Purusotham Singamsetty. "Lexi-search algorithm for one to many multidimensional bi-criteria unbalanced assignment problem." International Journal of Bio-Inspired Computation 14, no. 3 (2019): 151. http://dx.doi.org/10.1504/ijbic.2019.10025277.

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Wang, Zhibin, and Jianjun Zhu. "Solving Multi-robot Task Assignment Problem based on Improved Genetic Algorithm." Frontiers in Computing and Intelligent Systems 10, no. 3 (2024): 5–10. https://doi.org/10.54097/sctr7h26.

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Aiming at the low efficiency of robot task distribution and unbalanced task distribution in the process of warehouse task scheduling, this paper aims at minimum total task path and balanced task distribution, establishes a warehouse robot scheduling scheme and mathematical model, and proposes an improved genetic algorithm for task allocation. Firstly, greedy algorithm is introduced to optimize the initial population and improve the quality of the initial population. Secondly, the heuristic bidirectional triple crossover operator is designed to expand the search range of the population and improve the accuracy of the optimal solution. Finally, the multi-variant strategy is introduced to solve the problem that the algorithm falls into local optimality. The experimental results show that the improved algorithm has better convergence and balanced distribution ability.

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Antikah, Antikah, Elis Ratna Wulan, and Fahrudin Muhtarulloh. "Penyelesaian Masalah Penugasan dengan Metode Rafi Aziz Uddin Bhuiyan (RAUB), Hungarian Method, dan Heuristic Method." Jurnal EurekaMatika 10, no. 2 (2022): 91–98. http://dx.doi.org/10.17509/jem.v10i2.45391.

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This study discusses the implementation of the Rafi Aziz Uddin Bhuiyan (RAUB) Method, Hungarian Method, and Heuristic Method in solving assignment problems for both minimization case and maximization case in balanced and unbalanced conditions. To determine the better method in solving the assignment problem, we then compared the three methods. Based on the analysis conducted, the Hungarian Method produces a better optimal solution with fewer number of iterations than the RAUB Method and the Heuristic Method. So, it can be concluded that the assignment problem can be solved better by using the Hungarian Method than the RAUB Method and the Heuristic Method.Keywords: Assignment Problem, Heuristic Method, Hungarian Method, Rafi Aziz Uddin Bhuiyan (RAUB) Method.AbstrakPenelitian ini membahas tentang penerapan Rafi Aziz Uddin Bhuiyan (RAUB) Method, Hungarian Method, dan Heuristic Method dalam menyelesaikan masalah penugasan untuk kasus minimasi dan kasus maksimasi baik dalam keadaan seimbang maupun tidak seimbang. Selanjutnya, untuk menentukan metode yang lebih baik dalam menyelesaikan masalah penugasan, ketiga metode tersebut dibandingkan. Berdasarkan analisis yang dilakukan, Hungarian Method menghasilkan solusi optimal yang lebih baik dengan jumlah iterasi yang lebih sedikit dibandingkan dengan RAUB Method dan Heuristic Method. Sehingga dapat disimpulkan bahwa masalah penugasan dapat diselesaikan dengan lebih baik menggunakan Hungarian Method daripada RAUB Method dan Heuristic Method.

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Koehl, Patrice, and Henri Orland. "A General Statistical Physics Framework for Assignment Problems." Algorithms 17, no. 5 (2024): 212. http://dx.doi.org/10.3390/a17050212.

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Linear assignment problems hold a pivotal role in combinatorial optimization, offering a broad spectrum of applications within the field of data sciences. They consist of assigning “agents” to “tasks” in a way that leads to a minimum total cost associated with the assignment. The assignment is balanced when the number of agents equals the number of tasks, with a one-to-one correspondence between agents and tasks, and it is and unbalanced otherwise. Additional options and constraints may be imposed, such as allowing agents to perform multiple tasks or allowing tasks to be performed by multiple agents. In this paper, we propose a novel framework that can solve all these assignment problems employing methodologies derived from the field of statistical physics. We describe this formalism in detail and validate all its assertions. A major part of this framework is the definition of a concave effective free energy function that encapsulates the constraints of the assignment problem within a finite temperature context. We demonstrate that this free energy monotonically decreases as a function of a parameter β representing the inverse of temperature. As β increases, the free energy converges to the optimal assignment cost. Furthermore, we demonstrate that when β values are sufficiently large, the exact solution to the assignment problem can be derived by rounding off the elements of the computed assignment matrix to the nearest integer. We describe a computer implementation of our framework and illustrate its application to multi-task assignment problems for which the Hungarian algorithm is not applicable.

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Wang, Liuyi, Zongtao He, Chengju Liu, and Qijun Chen. "Graph based twin cost matrices for unbalanced assignment problem with improved ant colony algorithm." Results in Applied Mathematics 12 (November 2021): 100207. http://dx.doi.org/10.1016/j.rinam.2021.100207.

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Biswas, Amiya, Asoke Kumar Bhunia, and Ali Akbar Shaikh. "Multi-objective unbalanced assignment problem with restriction of jobs to agents via NSGA-II." International Journal of Mathematics in Operational Research 13, no. 1 (2018): 107. http://dx.doi.org/10.1504/ijmor.2018.092967.

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Bhunia, Asoke Kumar, Ali Akbar Shaikh, and Amiya Biswas. "Multi-objective unbalanced assignment problem with restriction of jobs to agents via NSGA-II." International Journal of Mathematics in Operational Research 13, no. 1 (2018): 107. http://dx.doi.org/10.1504/ijmor.2018.10013178.

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Fu, Huiling, Benjamin R. Sperry, and Lei Nie. "Operational Impacts of Using Restricted Passenger Flow Assignment in High-Speed Train Stop Scheduling Problem." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/902562.

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One key decision basis to the train stop scheduling process is the passenger flow assignment, that is, the estimated passengers’ travel path choices from origins to destinations. Many existing assignment approaches are stochastic in nature, which causes unbalanced problems such as low efficiency in train capacity occupancy or an irrational distribution of transfer passengers among stations. The purpose of this paper is to propose a train stop scheduling approach. It combines a passenger flow assignment procedure that routes passenger travel paths freely within a train network and is particularly capable of incorporating additional restrictions on generating travel paths that better resemble the rail planner’s purpose of utilizing capacity resources by introducing four criteria to define the feasibility of travel path used by a traveler. Our approach also aims at ensuring connectivity and rapidity, the two essential characteristics of train service increasingly required by modern high-speed rails. The effectiveness of our approach is tested using the Chinese high-speed rail network as a real-world example. It works well in finding a train stop schedule of good quality whose operational indicators dominate those of an existing stochastic approach. The paper concludes with a comprehensive operational impact analysis, further demonstrating the value of our proposed approach.

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Chen, Jie, Kai Xiao, Kai You, Xianguo Qing, Fang Ye, and Qian Sun. "Hierarchical Task Assignment Strategy for Heterogeneous Multi-UAV System in Large-Scale Search and Rescue Scenarios." International Journal of Aerospace Engineering 2021 (July 15, 2021): 1–19. http://dx.doi.org/10.1155/2021/7353697.

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For the large-scale search and rescue (S&R) scenarios, the centralized and distributed multi-UAV multitask assignment algorithms for multi-UAV systems have the problems of heavy computational load and massive communication burden, which make it hard to guarantee the effectiveness and convergence speed of their task assignment results. To address this issue, this paper proposes a hierarchical task assignment strategy. Firstly, a model decoupling algorithm based on density clustering and negotiation mechanism is raised to decompose the large-scale task assignment problem into several nonintersection and complete small-scale task assignment problems, which effectively reduces the required computational amount and communication cost. Then, a cluster head selection method based on multiattribute decision is put forward to select the cluster head for each UAV team. These cluster heads will communicate with the central control station about the latest assignment information to guarantee the completion of S&R mission. At last, considering that a few targets cannot be effectively allocated due to UAVs’ limited and unbalanced resources, an auction-based task sharing scheme among UAV teams is presented to guarantee the mission coverage of the multi-UAV system. Simulation results and analyses comprehensively verify the feasibility and effectiveness of the proposed hierarchical task assignment strategy in large-scale S&R scenarios with dispersed clustering targets.

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Hu, Xijian, Jiaqi Teng, Wei Wu, Yan Li, and Yuhong Sheng. "Research on Airport Scheduling of FGAP Multi-Objective Programming Model Based on Uncertainty Theory." Symmetry 13, no. 10 (2021): 1915. http://dx.doi.org/10.3390/sym13101915.

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Based on the current background of airport management and flight-gate scheduling in China, this paper takes Shanghai Pudong International Airport’s flight number of the rising and landing aircraft in a certain day as the research object, and it establishes an uncertain FGAP (Flight-Gate Assignment Problem) multi-objective programming model under the framework of uncertainty theory. Using genetic algorithm to solve the model, the specific flight-gate assignment scheduling plan is given. The research results show that the model in this paper can effectively alleviate the problems, such as unbalanced flight-gate allocation and excessive operating pressure of a single gate, in the conventional model, and make the allocation and scheduling more reasonable and efficient. Finally, we give the future application of uncertainty theory in finance and management, as well as the prospect of combining it with symmetry in physics.

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Schmidt, C., D. Piper, P. Putsche, et al. "Assignment of Empirical Mode Decomposition Components and Its Application to Biomedical Signals." Methods of Information in Medicine 54, no. 05 (2015): 461–73. http://dx.doi.org/10.3414/me14-02-0024.

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SummaryObjectives: Empirical mode decomposition (EMD) is a frequently used signal processing approach which adaptively decomposes a signal into a set of narrow-band components known as intrinsic mode functions (IMFs). For multi-trial, multivariate (multiple simultaneous recordings), and multi-subject analyses the number and signal properties of the IMFs can deviate from each other between trials, channels and subjects. A further processing of IMFs, e.g. a simple ensemble averaging, should determine which IMFs of one signal correspond to IMFs from another signal. When the signal properties have similar characteristics, the IMFs are assigned to each other. This problem is known as correspondence problem.Methods: From the mathematical point of view, in some cases the correspondence problem can be transformed into an assignment problem which can be solved e.g. by the Kuhn-Munkres algorithm (KMA) by which a minimal cost matching can be found. We use the KMA for solving classic assignment problems, i.e. the pairwise correspondence between two sets of IMFs of equal cardinalities, and for pairwise correspondences between two sets of IMFs with different cardinalities representing an unbalanced assignment problem which is a special case of the k-cardinality assignment problem.Results: A KMA-based approach to solve the correspondence problem was tested by using simulated, heart rate variability (HRV), and EEG data. The KMA-based results of HRV decomposition are compared with those obtained from a hierarchical cluster analysis (state-of-the-art). The major difference between the two approaches is that there is a more consistent assignment pattern using KMA. Integrating KMA into complex analysis concepts enables a comprehensive exploitation of the key advantages of the EMD. This can be demonstrated by non-linear analysis of HRV-related IMFs and by an EMD-based cross-frequency coupling analysis of the EEG data.Conclusions: The successful application to HRV and EEG analysis demonstrates that our solutions can be used for automated EMD-based processing concepts for biomedical signals.

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Wang, Zhaocai, Jun Pu, Liling Cao, and Jian Tan. "A Parallel Biological Optimization Algorithm to Solve the Unbalanced Assignment Problem Based on DNA Molecular Computing." International Journal of Molecular Sciences 16, no. 10 (2015): 25338–52. http://dx.doi.org/10.3390/ijms161025338.

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Xu, Yisong, and Xinyu Zhuang. "Container Shipping Scheduling Method Based on the Evidence Reasoning Approach in Fluctuating CCFI and BDI Cycle." Mathematical Problems in Engineering 2022 (July 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/3997361.

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Due to the COVID-19/Omicron pandemic and the trade war and tariffs between China and America, the supply chain between Asia and America, or Asia and Europe, faced unprecedented challenges. With the outbreak of the Russian-Ukrainian war, the global supply chain has become increasingly unbalanced. In particular, the shortage of container ships and the continuous fluctuation of the BDI and CCFI make shipping scheduling increasingly important for ocean carriers. To solve this problem, in this study, we propose an analytic approach considering the fluctuation of BDI and CCFI based on interval evidence reasoning and the Hungarian algorithm. The proposed approach uses two pairs of nonlinear optimisation models to construct a Nash equilibrium assignment model to compute the shipping company’s scheduling and maximum utility in the CCFI and BDI cycles. Compared to the simulation algorithm, the analytical algorithm can take advantage of stability and efficiency. Finally, a COSCO company’s container shipping scheduling problem was examined to demonstrate the efficiency and effectiveness of the proposed approach.

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AL-Ashari, Omar, and Ahmed Daham. "Using the Geometric Mean Method to Finding the Optimal Solution for the Unbalanced Fuzzy Assignment Problem with Practical Application." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (September 23, 2021): 96–110. http://dx.doi.org/10.55562/jrucs.v48i1.40.

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في هذا الورقة البحثية، تم تسليط الضوء على دراسة مشكلة التخصيص الضبابي غير المتوازن بعد اختيار الخط الأول التابع لمصنع دجلة/شركة بغداد للمشروبات الغازية كميدان للتطبيق، اذ ركزت هذه الدراسة على حالة عدم التوازن التي تُعتبر حالة شائعة في واقع الحياة العملية، وكذلك في ظل وجود بيئة ضبابية وعدم دقة في البيانات. للتعامل مع البيانات الضبابية (Fuzzy Data) تم استعمال طريقة ماﮒ (Mag-Method) لتحويلها الى بيانات اعتيادية (Crisp Data) يسهل فهمها والتعامل معها في صنع واتخاذ القرار. بعد الحصول على البيانات الاعتيادية تم تطبيق طريقة الوسط الهندسي (Geometric Mean Method) على البيانات للحصول على الحل الأمثل، وتم أيضاً أضافة إلى ذلك بناء أنموذج رياضي للمشكلة وحله باستعمال برنامج سبايدر (Spyder 3.3.5) بالاِعتماد على دوال برنامج ﮔروبي المُحَسن (Gurobi Optimizer) ومقارنة النتائج المستحصلة من طريقة الوسط الهندسي مع نتائج برنامج سبايدر لمعرفة كفاءة وفاعلية هذه الطريقة في تحقيق الأمثلية. وكانت من أهم النتائج التي تم التوصل إليها هو إعطاء طريقة الوسط الهندسي كفاءة وفاعلية في تحقيق الأمثلية.

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Wang, Qiuzhen, and Xinjun Mao. "Dynamic Task Allocation Method of Swarm Robots Based on Optimal Mass Transport Theory." Symmetry 12, no. 10 (2020): 1682. http://dx.doi.org/10.3390/sym12101682.

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It is difficult for swarm robots to allocate tasks efficiently by self-organization in a dynamic unknown environment. The computational cost of swarm robots will be significantly increased for large-scale tasks, and the unbalanced task allocation of robots will also lead to a decrease in system efficiency. To address these issues, we propose a dynamic task allocation method of swarm robots based on optimal mass transport theory. The problem of large-scale tasks is solved by grouping swarm robots to complete regional tasks. The task reallocation mechanism realizes the balanced task allocation of individual robots. This paper solves the symmetric assignment between robot and task and between the robot groups and the regional tasks. Our simulation and experimental results demonstrate that the proposed method can make the swarm robots self-organize to allocate large-scale dynamic tasks effectively. The tasks can also be balanced allocated to each robot in the swarm of robots.

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Wei, Bangyang, and Daniel(Jian) Sun. "A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram." Journal of Advanced Transportation 2018 (August 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/8616120.

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Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

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Batlajery, Hardy, and Venn Ilwaru. "Penugasan Tutor Sebaya dengan Metode Pinalti." Contemporary Mathematics and Applications (ConMathA) 4, no. 2 (2022): 105–12. http://dx.doi.org/10.20473/conmatha.v4i2.39524.

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This research focuses on the assignment of peer tutors in the Mathematics Study Program, Mathematics Department, FMIPA Unpatti. The purpose of this study was to obtain the completion of peer tutor assignments with the penalty method. The penalty method is a method used to solve the problem of unbalanced assignments. The processed two parts, namely finding the initial solution and finding the optimal solution. The column penalty method or the row penalty method is used to get the initial solution. In this study, the row penalty method is used because the number of rows is less than the number of columns, and the optimal solution is sought. The data used are student names, courses, and final grades. The results obtained using the Penalty method are that David becomes a tutor in the Statistical Method course, Alfito becomes a tutor in the Operations Research course, Christin becomes a tutor in the Linear Program course, Gabriella becomes a tutor in the Analytical Geometry course and Navila becomes a tutor in the course Elementary Statistics.

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Liu, Juan, Jing Yu Su, Wei Wang, and Dong Hui Ma. "Information Entropy Method for Evaluating Regional Earthquake Relative Disaster-Carrying Capability." Applied Mechanics and Materials 166-169 (May 2012): 2070–73. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.2070.

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In order to enhance the accuracy of regional disaster-carrying capability assessment, the dissipative structure of the regional urban earthquake relative disaster-carrying system is highly complicated and nonlinear. Moreover, the problem is multi-attribute, multi-index and multilevel. Based on the evaluation index system of regional relative disaster-carrying capability, This paper used the entropy weight assignment method to work out each index’s objective weight, quantitatively researched the 29 cities’ earthquake relative disaster-carrying capability, worked out every city’s relative disaster-carrying capability and divided its grades using the mean deviation method. The research results showed that the earthquake relative disaster-carrying capability of our country was much unbalanced. It was positively correlation with the economic performance, the eastern, central and western cities’ earthquake relative disaster-carrying capabilities were mainly distributed in the shape of the ladder. The eastern cities’ capabilities were much higher than the western cities’. So, we suggested that the eastern cities should further improve its earthquake disaster-carrying capability and the central and western cities should spend more efforts to develop its earthquake disaster-carrying capability. Only in this way, our country’s urban earthquake disaster reduction can develop evenly.

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Ali Ahmed, Karan Kumar, Noman Khokhar, and Nelson Alfonso. "Credit Card Fraudulent Transactions Detection Using Machine Learning." Physical Education, Health and Social Sciences 3, no. 2 (2025): 14–26. https://doi.org/10.63163/jpehss.v3i2.242.

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With the rapid growth of the e-commerce industry, the use of credit cards for online purchases has increased significantly. Unfortunately, credit card fraud has also become increasingly prevalent in recent years, creating complications for banks trying to detect fraudulent activity within the credit card system. To overcome this hardship Machine learning plays an eminent role in detecting the credit card fraud in the transactions. Modeling prior credit card transactions with data from ones that turned out to be fraudulent is part of the Card Fraud Detection Problem. In Machine learning the machine is trained at first to predict the output so, to predict the various bank transactions various machine learning algorithms are used. The SMOTE approach was employed to oversample the dataset because it was severely unbalanced. This paper the examines and overview the performance of K-nearest neighbors, Decision Tree, Logistic regression and Random forest, XGBoost for credit card fraud detection. The assignment is implemented in Python and uses five distinct machine learning classification techniques. The performance of the algorithm is evaluated by accuracy score, confusion matrix, f1-score, precision and recall score and auc-roc curve as well.

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Ramachandran, Dhanagopal, R. Suresh Kumar, Ahmed Alkhayyat, et al. "Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer." Computational Intelligence and Neuroscience 2022 (July 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/6348424.

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Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such as voice recognition and prediction, the long short-term memory (LSTM) fully connected layer (FCL) and the two convolutional neural networks (CNNs) have shown superior performance over deep learning networks (DLNs). DNNs are suitable for making high points for a more divisible region and CNNs are suitable for reducing recurrence types, LSTMs are appropriate for temporary displays, in the same way as CNNs are appropriate for reducing recurrence types. The CNN, LSTM, and DNN algorithms are acceptable for viewing. The complementarity of DNNs, CNNs, and LSTMs was investigated in this research by bringing them all together under the single architectural company. The researchers got the ECG data from the MIT-BIH arrhythmia database as a result of the investigation. Our results demonstrate that the approach proposed may expressively describe ECG series and identify abnormalities via scores that outperform existing supervised and unsupervised methods in both the short term and long term. The LSTM network and FCL additionally demonstrated that the unbalanced datasets associated with the ECG beat detection problem could be consistently resolved and that they were not susceptible to the accuracy of ECG signals. It is recommended that cardiologists employ the unique technique to aid them in performing reliable and impartial interpretation of ECG data in telemedicine settings.

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Wang, Yajing, Dongyue Liang, and Weihua Yang. "Belief Propagation for Unbalanced Assignment Problem." Asia-Pacific Journal of Operational Research, September 23, 2022. http://dx.doi.org/10.1142/s0217595922500373.

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Kumar, Dr. P. Senthil. "An Algorithm for Solving Unbalanced Intuitionistic Fuzzy Assignment Problem Using Triangular Intuitionistic Fuzzy Number." June 8, 2016. https://doi.org/10.5281/zenodo.887442.

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In solving real life assignment problem, we often face the state of uncertainty as well as hesitation due to varies uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. In this paper, computationally a simple method is proposed to find the optimal solution for an unbalanced assignment problem under intuitionistic fuzzy environment. In conventional assignment problem, cost is always certain. This paper develops an approach to solve the unbalanced assignment problem where the time/cost/profit is not in deterministic numbers but imprecise ones. In this assignment problem, the elements of the cost matrix are represented by the triangular intuitionistic fuzzy numbers. The existing Ranking procedure of Varghese and Kuriakose is used to transform the unbalanced intuitionistic fuzzy assignment problem into a crisp one so that the conventional method may be applied to solve the AP . Finally the method is illustrated by a numerical example which is followed by graphical representation and discussion of the finding.

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Fouad, Faten, AllaEldin H. Kassam, and Sawsan S. Al-Zubaidi. "A New Heuristic Method for Solving Unbalanced Multi-Objective Assignment Problem." Engineering Research Express, November 28, 2024. http://dx.doi.org/10.1088/2631-8695/ad9888.

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Abstract In the era of globalization, institutions face increasing challenges in effectively managing their resources to achieve diverse and differentiated objectives. The unbalanced multi-objective assignment problem reflects these challenges, as the distribution of limited resources requires achieving a balance between objectives such as minimizing time, cost, etc. In this study, an application case in the industrial field of rolling process was investigated, where the objective of the assignment problem is to minimize total assignment. In order to tackle this problem, the multi-objective problem was addressed by converting it into a single-objective problem through the weighted sum method by assigning a weight to each objective. A new heuristic method was then applied to solve the unbalanced single-objective assignment problem where the machines were loaded with more than one job. The Hungarian method was also used to solve the problem in order to assess the proposed method, and the outcomes were then compared. The proposed method proved efficient as it reduced idle time by 28%, increased machine efficiency by 5% and reduced costs by an average of 15%.

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Koehl, Patrice, and Henri Orland. "Physicist's view on the unbalanced k -cardinality assignment problem." Physical Review E 110, no. 1 (2024). http://dx.doi.org/10.1103/physreve.110.014108.

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R., Murugesan. "IDI-75 - AN OPTIMAL ONES ASSIGNMENT METHOD FOR SOLVING UNBALANCED ASSIGNMENT PROBLEMS." July 5, 2022. https://doi.org/10.5281/zenodo.6997697.

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In this article, a new technique namely IDI-75 (named to commemorate the 75th Independence Day of India), which is a simplified form of the existing Ones Assignment Method (OAM) of solving assignment problems, finds the optimal solution to a given unbalanced assignment problem (UAP). In the OAM, it is required to convert the given UAP into a balanced one by introducing one dummy row or column with 1 unit of effectiveness in each cell. But, the IDI-75 technique can be applied directly on the given UAP without converting it into a balanced one. The technique requires only row minimum division operation or only column minimum division operation depending on the size of rows and columns in order to have at least one 1-entry in each of the required number of rows and/or columns only. The assignments are made on the appropriate 1-entry cells of reduced ratio of costs matrix. To test the validity and effectiveness of the IDI-75 technique, 20 benchmark instances with different sizes from the literatures have been tested. Simulation results authenticate that the technique IDI-75 is the best one which produces optimal solution to all 20 instances. Therefore, it is clever to apply the IDI-75 technique to solve the UAPs as it is very simple, easy to understand, easy to apply and consume less time in comparison to the existing OAM.

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Mastan, Shaik. "CONSTRAINED LOAD BALANCING SOLID UNBALANCED ASSIGNMENT PROBLEM: AN EXACT ENUMERATIVE ALGORITHM." SSRN Electronic Journal, 2025. https://doi.org/10.2139/ssrn.5043617.

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