Journal articles on the topic 'Unbalanced optimal transportation'
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Nopiyana, P. Affandi, and A. S. Lestia. "Solving transportation problem using modified ASM method." Journal of Physics: Conference Series 2106, no. 1 (November 1, 2021): 012029. http://dx.doi.org/10.1088/1742-6596/2106/1/012029.
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Abstract Transportation problems are related to activities aimed at minimizing the cost of distributing goods from a source to a destination. One of the methods used to solve transportation problems is the ASM Method as a method capable of producing optimal direct solutions without having to determine the initial basic feasible solution first. Determination of the allocation of goods in the ASM Method uses a reduced cost of 0 by calculating the maximum amount in the allocation of goods. Then the ASM method is modified so that the iteration used is simpler in obtaining the optimal direct solution without calculating the maximum number of row and column elements. The method is called Modified ASM Method. This method also provides more optimal results than the ASM method. This research aimed to solve transportation problems using the Modified ASM method to produce optimal solutions directly. The research procedure identifies and forms a model of transportation problems (variable decisions, objective functions and constraint functions), identifies types of transportation problems (balanced or unbalanced), and obtains direct solutions by solving transportation problems using the Modified ASM method. This research shows that the Modified ASM method successfully solves the problem of balanced and unbalanced transportation by producing optimal solutions in a simpler way than the ASM method.
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Joshi, Nidhi, and Surjeet Singh Chauhan. "A new Approach for Obtaining Optimal Solution of Unbalanced Fuzzy Transportation Problem." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 6 (April 20, 2016): 6824–32. http://dx.doi.org/10.24297/ijct.v15i6.3977.
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The present paper attempts to study the unbalanced fuzzy transportation problem so as to minimize the transportationcost of products when supply, demand and cost of the products are represented by fuzzy numbers. In this paper, authorsuse Roubast ranking technique to transform trapezoidal fuzzy numbers to crisp numbers and propose a new algorithm tofind the fuzzy optimal solution of unbalanced fuzzy transportation problem. The proposed algorithm is more efficient thanother existing algorithms like simple VAM and is illustrated via numerical example. Also, a comparison between the resultsof the new algorithm and the result of algorithm using simple VAM is provided.
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Kumar, Amit, and Amarpreet Kaur. "Methods for Solving Fully Fuzzy Transportation Problems Based on Classical Transportation Methods." International Journal of Operations Research and Information Systems 2, no. 4 (October 2011): 52–71. http://dx.doi.org/10.4018/joris.2011100104.
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There are several methods, in literature, for finding the fuzzy optimal solution of fully fuzzy transportation problems (transportation problems in which all the parameters are represented by fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings, two new methods (based on fuzzy linear programming formulation and classical transportation methods) are proposed to find the fuzzy optimal solution of unbalanced fuzzy transportation problems by representing all the parameters as trapezoidal fuzzy numbers. The advantages of the proposed methods over existing methods are also discussed. To illustrate the proposed methods a fuzzy transportation problem (FTP) is solved by using the proposed methods and the obtained results are discussed. The proposed methods are easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems occurring in real life situations.
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Prokudin, Georgii, Alexey Chupaylenko, Tetiana Khobotnia, Inna Remekh, Andrei Lyamzin, and Marina Kovalenko. "Optimizing unbalanced freight deliveries in transportation networks." Eastern-European Journal of Enterprise Technologies 2, no. 3 (116) (April 28, 2022): 22–32. http://dx.doi.org/10.15587/1729-4061.2022.253791.
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This paper reports a comparative analysis of the known methods for reducing open transportation problems to a balanced form in order to further optimize freight traffic based on them. A series of significant shortcomings have been revealed that largely narrow the scope of their application. A new method has been proposed, termed the method of proportional redistribution of cargo transportation volumes among participants in the transportation process, devoid of the identified shortcomings. The transportation problem is a special case of the general linear programming problem, to which one of the methods for solving it, namely the simplex one, can be applied. A procedure to construct a simplex table based on the data from the transport table has been described, as well as the algorithm of subsequent simplex transformations. A transportation problem is often stated in the form of a map of the location of transport hubs of cargo dispatch and destination. A matrix-network model has been proposed, which makes it possible to reduce the network representation to a matrix form with the subsequent finding of the optimal plan for cargo transportation. In order to identify the priority of methods for reducing open transportation problems to a balanced form, 100 transportation problems that are unbalanced in terms of the volume of cargo transportation were solved. That was done with the help of a designed decision support system for the management of freight transport. As a criterion, the best freight transportation plan was chosen. As a result, the simplex method proved the best in 48 cases, the coefficient method ‒ in 27, the dummy node method ‒ in 16, and the difference method ‒ in 9 cases. The use of a decision support system for the management of freight transport has increased its efficiency by an average of 25 %
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Hamoud, Ahmed, Kirtiwant Ghadle, and Priyanka Pathade. "DA systematic approach for solving mixed constraint fuzzy balanced and unbalanced transportation problem." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 85. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp85-90.
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<p>In the present article, a mixed type transportation problem is considered. Most of the transportation problems in real life situation have mixed type transportation problem this type of transportation problem cannot be solved by usual methods. Here we attempt a new concept of Best Candidate Method (BCM) to obtain the optimal solution. To determine the compromise solution of balanced mixed fuzzy transportation problem and unbalanced mixed fuzzy transportation problem of trapezoidal and trivial fuzzy numbers with new BCM solution procedure has been applied. The method is illustrated by the numerical examples.</p>
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Srinivasan, N., and A. Seethalakshmy. "A Heuristic Approach to Obtain an Optimal Solution for Unbalanced Transportation Problem." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 364. http://dx.doi.org/10.14419/ijet.v7i4.10.20936.
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This Method is proposed for obtaining an optimal solution for transportation problem. This method gives the optimal solution in lesser iteration. Here find the difference between two consecutive maximum for row-wise and column-wise. In that find the maximum value, for which the minimum is allocated by the minimum supply or demand. Illustration for this method is given with some examples at the end.
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Ghadle, Kirtiwant P., and Priyanka A. Pathade. "Optimal Solution of Balanced and Unbalanced Fuzzy Transportation Problem Using Hexagonal Fuzzy Numbers." International Journal of Mathematical Research 5, no. 2 (2016): 131–37. http://dx.doi.org/10.18488/journal.24/2016.5.2/24.2.131.137.
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Bisht, Dinesh C. S., and Pankaj Kumar Srivastava. "One Point Conventional Model to Optimize Trapezoidal Fuzzy Transportation Problem." International Journal of Mathematical, Engineering and Management Sciences 4, no. 5 (October 1, 2019): 1251–63. http://dx.doi.org/10.33889/ijmems.2019.4.5-099.
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This article puts forward a new one point approach to optimize trapezoidal fuzzy transportation problem. It proposes the method having point wise breakup of the trapezoidal number in such a way, that fuzzy transportation problem is converted into four crisp transportation problems. The method is equipped with minimum of supply and demand approach. In the end, the solutions are combined to construct the optimal solution. Modified distribution is applied on each crisp problem to develop optimal solution. The scheme presented is compared with competitive methods available in literature and it is found to be in good coordination with these. The scheme is equally good to be applied on unbalanced problems. Two numerical problems are considered to test the performance of the proposed approach.
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Adhikari, Purnima, and Gyan Bahadur Thapa. "A Note on Feasibility and Optimality of Transportation Problem." Journal of the Institute of Engineering 10, no. 1 (July 31, 2014): 59–68. http://dx.doi.org/10.3126/jie.v10i1.10879.
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Transportation problem is one of the predominant areas of operations research, widely used as a decision making tool in engineering, business management and many other fields. In this paper, we present a brief literature review of transportation problem with its mathematical models in balanced and unbalanced cases. We report the basic feasible solution and hence the methods to attain optimal solution of the balanced transportation problem. Finally, we describe the primal-dual case of the problem with counter examples. DOI: http://dx.doi.org/10.3126/jie.v10i1.10879Journal of the Institute of Engineering, Vol. 10, No. 1, 2014, pp. 59–68
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Gupta, Anila, Amit Kumar, and Amarpreet Kaur. "Mehar’s method to find exact fuzzy optimal solution of unbalanced fully fuzzy multi-objective transportation problems." Optimization Letters 6, no. 8 (July 20, 2011): 1737–51. http://dx.doi.org/10.1007/s11590-011-0367-2.
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Indrawan, Andry Nor, Pardi Affandi, and Oni Soesanto. "PENGGUNAAN JUMAN & HOQUE METHOD (JHM) PADA PENENTUAN SOLUSI AWAL MASALAH TRANSPORTASI." EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN 15, no. 1 (July 16, 2021): 27. http://dx.doi.org/10.20527/epsilon.v15i1.2876.
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Transportation problems are related to the efficient process of distributing goods by a company or industry. The purpose of solving transportation problems is to minimize the costs incurred in the process of distributing goods from several sources (supply) to several destinations (demand). One way to solve transportation problems is to find an initial feasible solution, continued by finding the optimal solution. This research was done by finding an initial feasible solution using the JHM (Juman & Hoque Method) for both the case of solving balanced transportation problems and unbalanced transportation problems. The method has the characteristic in the initial allocation process starting at the cell with the smallest cost in each column as much as the quantity of each demand. In addition, identification of whether the row if occupied or not was done based on the allocation for each row to the quantity of each inventory. This research aimed to explain about solving transportation problems by determining the initial feasible solution using JHM and performing optimality test using potential method. The methods of this research was to identify categories of transportation problems, determine the initial solution using JHM, and test the optimality using potential method. Based on the results of this research, JHM model may be used to solve transportation problems. In the steps of JHM there are explanations of some theorem regarding the selection of the column and row which will be the first to be processed to determine the value of intial solution of transportation problems. The initial solution by using JHM tends to approach the value of optimal solution after test of optimality was done by using the potential method.
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Pronevich, O. B., and M. V. Zaitsev. "Intelligent methods for improving the accuracy of prediction of rare hazardous events in railway transportation." Dependability 21, no. 3 (September 21, 2021): 54–64. http://dx.doi.org/10.21683/1729-2646-2021-21-3-54-65.
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The paper Aims to examine various approaches to the ways of improving the quality of predictions and classification of unbalanced data that allow improving the accuracy of rare event classification. When predicting the onset of rare events using machine learning techniques, researchers face the problem of inconsistency between the quality of trained models and their actual ability to correctly predict the occurrence of a rare event. The paper examines model training under unbalanced initial data. The subject of research is the information on incidents and hazardous events at railway power supply facilities. The problem of unbalanced data is expressed in the noticeable imbalance between the types of observed events, i.e., the numbers of instances. Methods. While handling unbalanced data, depending on the nature of the problem at hand, the quality and size of the initial data, various Data Science-based techniques of improving the quality of classification models and prediction are used. Some of those methods are focused on attributes and parameters of classification models. Those include FAST, CFS, fuzzy classifiers, GridSearchCV, etc. Another group of methods is oriented towards generating representative subsets out of initial datasets, i.e., samples. Data sampling techniques allow examining the effect of class proportions on the quality of machine learning. In particular, in this paper, the NearMiss method is considered in detail. Results. The problem of class imbalance in respect to the analysis of the number of incidents at railway facilities has existed since 2015. Despite the decreasing share of hazardous events at railway power supply facilities in the three years since 2018, an increase in the number of such events cannot be ruled out. Monthly statistics of hazardous event distribution exhibit no trend for declines and peaks. In this context, the optimal period of observation of the number of incidents and hazardous events is a month. A visualization of the class ratio has shown the absence of a clear boundary between the members of the majority class (incidents) and those of the minority class (hazardous events). The class ratio was studied in two and three dimensions, in actual values and using the method of main components. Such “proximity” of classes is one of the causes of wrong predictions. In this paper, the authors analysed past research of the ways of improving the quality of machine learning based on unbalanced data. The terms that describe the degree of class imbalances have been defined and clarified. The strengths and weaknesses of 50 various methods of handling such data were studied and set forth. Out of the set of methods of handling the numbers of class members as part of the classification (prediction of the occurrence) of rare hazardous events in railway transportation, the NearMiss method was chosen. It allows experimenting with the ratios and methods of selecting class members. As the results of a series of experiments, the accuracy of rare hazardous event classification was improved from 0 to 70-90%.
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Biswas, Animesh, and Nilkanta Modak. "On Solving Multiobjective Transportation Problems with Fuzzy Random Supply and Demand Using Fuzzy Goal Programming." International Journal of Operations Research and Information Systems 8, no. 3 (July 2017): 54–81. http://dx.doi.org/10.4018/ijoris.2017070104.
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In this article a fuzzy goal programming model is developed to solve multiobjective unbalanced transportation problems with fuzzy random parameters. In model formulation process the cost coefficients of the objectives are considered as fuzzy numbers and the supplies and demands are considered as fuzzy random variables with known fuzzy probability distribution from the view point of probabilistic as well as possibilistic uncertainties involved with the model. A fuzzy programming model is first constructed by applying chance constrained programming methodology in fuzzy environment. Then, the model is decomposed on the basis of the tolerance ranges of the fuzzy numbers associated with it. The individual optimal solution of each decomposed objectives is found in isolation to construct the membership goals of the objectives. Finally, priority based fuzzy goal programming technique is used to achieve the highest degree of each of the defined membership goals to the extent possible by minimizing the under deviational variables and thereby obtaining optimal allocation of products by using distance function in a cost minimizing decision making environment. An illustrative example is solved and compared with existing technique to explore the potentiality of the proposed methodology.
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Liu, Yi, Qi Chang, Jiaxin Luo, LinLi, Junfeng Man, FenWei, Qinlin Chen, and Yiping Shen. "Comprehensive Evaluation Model of Bearing Transportation Protection Effect of Bogie Traction Motor under Data Imbalance." Journal of Sensors 2021 (December 6, 2021): 1–19. http://dx.doi.org/10.1155/2021/4690369.
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Under different transportation protection, the sample data of bogie traction motor bearings of urban rail vehicles are seriously unbalanced, and the fault diagnosis ability and generalization effect are poor, which makes it difficult to evaluate the protection effect of bearings effectively. In this paper, a multimeasure hybrid evaluation model based on compressed sensing is proposed to evaluate the effect of bearing transportation protection under data imbalance. Firstly, bearing vibration signals under different transport protection conditions were compressed and sampled, and the original high-Witt collection in time domain, frequency domain, and time-frequency domain was extracted. Then, a multimeasure mixed feature evaluation model of correlation, distance, and signal was constructed, and the optimal multimeasure combination strategy was optimized by using comprehensive sensitivity score evaluation index. Finally, an evaluation model of bearing protection effect based on unified feature index was constructed by using the best feature subset evaluated, and the unified indicator was quantified to characterize the protection effect of different protection states. The experimental results show that the model can effectively evaluate bearings under different transport protection.
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Monemi, Rahimeh Neamatian, Shahin Gelareh, Anass Nagih, and Dylan Jones. "Bi-objective load balancing multiple allocation hub location: a compromise programming approach." Annals of Operations Research 296, no. 1-2 (January 28, 2020): 363–406. http://dx.doi.org/10.1007/s10479-019-03421-w.
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AbstractIn this paper we address unbalanced spatial distribution of hub-level flows in an optimal hub-and-spoke network structure of median-type models. Our study is based on a rather general variant of the multiple allocation hub location problems with fixed setup costs for hub nodes and hub edges in both capacitated and uncapacitated variants wherein the number of hub nodes traversed along origin-destination pairs is not constrained to one or two as in the classical models.. From the perspective of an infrastructure owner, we want to make sure that there exists a choice of design for the hub-level sub-network (hubs and hub edges) that considers both objectives of minimizing cost of transportation and balancing spatial distribution of flow across the hub-level network. We propose a bi-objective (transportation cost and hub-level flow variance) mixed integer non-linear programming formulation and handle the bi-objective model via a compromise programming framework. We exploit the structure of the problem and propose a second-order conic reformulation of the model along with a very efficient matheuristics algorithm for larger size instances.
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Batubara, Putri, Elly Rosmaini, and Esther Nababan. "Kajian Masalah Transshipment Tidak Seimbang Menggunakan Metode Least Cost - Stepping Stone Dan Metode Least Cost - Modi." Talenta Conference Series: Science and Technology (ST) 1, no. 1 (October 17, 2018): 049–58. http://dx.doi.org/10.32734/st.v1i1.189.
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Penelitian ini merupakan kajian masalah transshipment tidak seimbang menggunakan metode Least Cost - Stepping Stone. Metode Least Cost - MODI juga digunakan untuk membandingkan uji optimalitas mana yang lebih baik dalam menyelesaikan masalah transshipment ini. Hasil dari penelitian menunjukkan bahwa metode Least Cost - SteppingStone dan metode Least Cost - MODI dapat menyelesaikan masalah transshipment tidak seimbang. Menurut uji perbandingan metode MODI lebih efisien dari pada metode Stepping Stone dalam menguji optimalitas suatu masalah transshipment karena metode MODI memerlukan lebih sedikit iterasi dibandingkan dengan metode Stepping Stone. Pada Metode MODI nilai indeks perbaikan dapat dicari tanpa harus mencari loop dari setiap sel kosong, yakni hanya membutuhkan satu loop yang didapat setelah menentukan sel dengan indeks perbaikan terbesar, sedangkan pada metode Stepping Stone nilai indeks perbaikan dicari dengan membuat loop untuk setiap sel kosong pada setiap iterasi. Selain itu Metode Least Cost menghasilkan biaya transportasi yang berbeda apabila posisi penempatan biaya diubah, sedangkan dengan metode Stepping Stone biaya transportasi akan tetap sama dan optimal apabila posisi penempatan biaya diubah.
This research is a study of unbalanced transshipment problems using the Least Cost - Stepping Stone method. The Least Cost - MODI method was also used to compare which optimality test was better in solving this transshipment problem. The results of the study showed that the Least Cost - Stepping Stone method and the Least Cost - MODI method could solve unbalanced transshipment problems. According to the comparison test, the MODI method was more efficient than the Stepping Stone method in testing the optimality of a transshipment problem because the MODI method required less iteration than the Stepping Stone method. In the MODI method, the repair index value could be searched without having to search for loops from each empty cell, which only requires one loop after determining the cell with the largest repair index. On the other hand, in the Stepping Stone method, the repair index value was searched by making a loop for each empty cell at each iteration. In addition, the Least Cost method produced different transportation costs if the placement position costs were changed. Meanwhile, the Stepping Stone method transportation costs would remain the same and optimal if the placement position costs were altered.
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Li, Qing, and Ziyou Gao. "Managing Rush Hour Congestion with Lane Reversal and Tradable Credits." Mathematical Problems in Engineering 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/132936.
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Within the morning and evening rush hour, the two-way road flows are always unbalanced in opposite directions. In order to make full advantage of the existing lanes, the two-way road lane has to be reallocated to play the best role in managing congestion. On the other hand, an effective tradable credit scheme can help to reduce the traffic demand and improve fairness for all travelers. So as to alleviate the commute congestion in urban transportation network, a discrete bilevel programming model is established in this paper. In the bilevel model, the government at the upper level reallocates lanes on the two-way road to minimize the total system cost. The traveler at the lower level chooses the optimal route on the basis of both travel time and credit charging for the lanes involved. A numerical experiment is conducted to examine the efficiency of the proposed method.
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Wu, Jixiao, and Yinghui Wang. "Distribution of the Emergency Supplies in the COVID-19 Pandemic: A Cloud Computing Based Approach." Mathematical Problems in Engineering 2021 (October 21, 2021): 1–18. http://dx.doi.org/10.1155/2021/5972747.
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The containment of the COVID-19 pandemic was significantly affected by the unbalanced distribution of emergency supplies, low coordinated transport efficiency, high costs, and the inability of nonprofit organizations to handle the emergency supplies efficiently. Based on the COVID-19 experience, in this paper, we build a cloud platform for emergency supplies distribution to reduce the asymmetry of emergency logistics information, reduce the costs, and improve the efficiency of emergency supplies distribution. Our proposed method uses a genetic algorithm with the monarch scheme to optimize the urban emergency supplies distribution. The numerical results and sensitivity analysis for a sample network indicate that using the proposed platform the integrated cost in different cities are reduced by 29.01%, 28.67%, and 22.73%, the required time in different cities are reduced by 22.98%, 26.59%, and 36.65%. The results suggest that the proposed method reduces the integrated cost and transportation time and finds the optimal distribution path.
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Elhenawy, Mohammed, Hesham A. Rakha, Youssef Bichiou, Mahmoud Masoud, Sebastien Glaser, Jack Pinnow, and Ahmed Stohy. "A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems." Sustainability 13, no. 23 (December 4, 2021): 13433. http://dx.doi.org/10.3390/su132313433.
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City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm.
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Iftitah, Nurul, Pardi Affandi, and Akhmad Yusuf. "PENYELESAIAN MODEL TRANSPORTASI MENGGUNAKAN METODE ASM." JURNAL MATEMATIKA MURNI DAN TERAPAN EPSILON 14, no. 1 (June 1, 2020): 40. http://dx.doi.org/10.20527/epsilon.v14i1.2395.
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(demand). the method that could be used for solving the transportation problem is to directly find the optimal solution. The direct method that used in this study id the ASM method for solving the balance transportation problem and revised ASM method for solving the unbalance transportation problem. This study aims to construct a transportation model using those methods and it solution. The method on this study is to identify the transportation model, construct the transportation model matrixes, construct an algorithm table using ASM method and to determine the optimal solution of the transportation problem. The obtained result from this study was the model ASM method could determine the optimum value without using initial feasible solution. On solving the unbalance transportation problem, there is an addition of dummy cell or column step. Then reducing the cost of cell and column and change the dummy cost with the biggest cost of reduced cell or column.
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Filip, Florin Gheorghe. ""The Disassembly Line: Balancing and Modeling" - Book Review." International Journal of Computers Communications & Control 6, no. 3 (September 1, 2011): 581. http://dx.doi.org/10.15837/ijccc.2011.3.2137.
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<p>This book is about the disassembly of end-of-life products with particular emphasis on methods and techniques for solving the Disassembly Line Balancing Problem.</p><p>Disassembly is viewed as "the systematic separation and extracting valuable entities for possible future re-usage". In fact, disassembly is a distinct phase of the product lifecycle. It follows the "before life" phases (such as design and economical evaluation), "useful period" phases (such as manufacturing, distribution, usage and maintenance) and "end of life" phases (such as collecting, sorting). Disassembly might represent the essential first phase of the future activities, such as re-use and re-manufacturing and recycle. Due to the ever higher public awareness, the more and more strict regulations concerning environment quality preservation and increasing economic effectiveness and attractiveness for industry, the activities of recovering valuable parts and subassemblies have become a desirable alternative to the old fashioned disposal processes of end-of-life products.</p><p>The authors state, in the preface of the book, that the "disassembly line seems to be the most efficient way to disassemble a product". Consequently, the primary concern of the book is "the complete disassembly of [end-of-life] products on a paced disassembly line for component/material recovery purposes". The authors aim at investigating "the qualitative and quantitative aspects of multi-criteria solution sequences using the various combinatorial optimization techniques" (page 16) to solve the Disassembly Line Balancing Problem (DLPB). The DLBP consists in finding a disassembly feasible solution sequence which preserves precedence constraints and aims at attaining several objectives, such as minimizing the number of work stations and total idle time, ensuring similar idle time at each work station, while attempting to remove hazardous parts and materials and extracting highly demanded product components at the earliest moments possible and minimizing the number of direction changes required for disassembly (removing parts with similar part removal directions together), (page 102).</p><p>The book is composed of 29 chapters grouped into three parts entitled "Disassembly Background", "Disassembly-Line Balancing" and "Further Disassembly-Line Considerations" which address general aspects concerning disassembly processes, variations of methods and techniques to solve the DLBP, and other problems related to the disassembly line, respectively. <br /> <br /> Part I comprises six chapters which are meant to set the stage for the subsequent chapters. Various information concerning disassembly processes, assembly lines, disassembly lines, other related researches, graphical representations and computational complexity of combinatorial problems are provided.<br /> <br /> Part II is made up of 20 chapters and addresses the statement and analysis of the DLBP and several specific variations of methods and techniques which were adapted for solving the problem, tested on four application cases exprimental instances and compared. The objectives of this part of the book are: stating the mathematical model of DLBP, establishing the difficulty of the problem by using the complexity theory and determining the data sets and evaluation criteria to be used in analyzing the problem and solving techniques which are selected (page 99). <br /> <br /> It is demonstrated (in chapter 9) that the DLBP is a complex NP complete problem in the strong sense and necessitates specialized solution techniques. Accordingly, authors plea for combinatorial optimization approaches and select several algorithms to solve the problem. The techniques to be utilized to solve the DLBP are introduced in chapter 10 and their usage and performances in solving the problem are presented in chapters 12 through 19. There are seven techniques which are adapted, tested and compared. The exhaustive search is used to provide the optimal solution. Two metaheuristic approaches (genetic algorithms and ant colony optimization) are next studied. Two purely deterministic searches (the greedy algorithm and the "hunter-killer" search) and two 2-phase hybrid methods are adapted and tested.</p><p>The four experimental instances (the eight-part personal computer, the enhanced 10-part DLBP case, the 25-part cellular instance, and the size independent "a priori" benchmark with a known optimal solution) are described in chapter 11. Chapter 20 contains a detailed comparison of the six heuristic and metaheuristic techniques as applied to the DLBP with respect to several performance measures. Several complementary research results are reviewed in chapter 21 together with future research directions.<br /> <br /> Disassembly processes interact with other "before life", "useful", and "after life" periods of product usage and recovery. As a result, to make the picture complete, Part III addresses other areas of disassembly research such as product planning, line and facility design, operations scheduling and sequencing, inventory, "just-in-time", revenue and unbalanced lines (chapter 22 through 29).<br /> <br /> The authors of the book form a team who may be viewed as a fine and synergic combination of two complementary experiences and backgrounds from academia and industry. Seamus McGovern, an Electronics Engineer at the Volpe National Transportation System Center, holds a commission in the US Navy as an aerospace duty engineer as well as a part-time industrial engineering faculty appointment at the Northeastern University. Surendra M. Gupta is a professor of Mechanical and Industrial Engineering and a director of the Laboratory for Responsible Manufacturing at the Northeastern University. He has authored/co-authored over 400 technical papers and is a pioneer in the domain of the book.<br /> <br /> This book represents a very valuable work in a rather young research domain, which may be viewed as opened by the pioneering paper of Güngör and Gupta entitled "Disassembly Line in product Recovery (International Journal of Production Recovery, 40 (11), 2002). The volume mainly reflects the original studies of authors and their colleagues. It also makes an exhaustive and systematic review of the results which are reported in the domain scientific literature and are due to other scientists. The organization of the document is well thought and the presentation style is rigorous and clear. Subsequently, though information content is very dense and diverse, the book is accessible and its study is scientifically rewarding. Special remarks can be made to the uniform and coherent notation which is used throughout the book and to graphical illustrations. A final remark of appreciation is to be made to the excellent quality of editing and printing of the book due to the staff of McGraw-Hill Companies.<br /> <br /> In conclusion, the book is a timely work which contains relevant, inspiring and challenging information. Therefore, this reviewer warmly recommends it to the readers of academia and industry as well who are interested in modern manufacturing issues and combinatorial optimization methods and software.</p><p> </p>
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Ahmad, Qazi Shoeb. "A New Approach for Finding the Initial Solution of the Unbalanced Transportation Problem." Asian Journal of Business and Management 8, no. 4 (October 30, 2020). http://dx.doi.org/10.24203/ajbm.v8i4.6304.
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A transportation problem deals with two different problems balanced transportation problem or unbalanced transportation problem. This paper points out how Goyal’s modification of Vogel’s approximation method for the unbalanced transportation problem can be improved by subtracting or adding suitable constants to the cost matrix, rows and columns of the cost matrix. In this paper, a new method is proposed for solving unbalanced transportation problem which gives optimal or very near to optimal solution.
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S, Soundararajan, and Suresh Kumar M. "Solving unbalanced intuitionistic fuzzy transportation problem." Journal of Computational Mathematica 4, no. 1 (June 30, 2020). http://dx.doi.org/10.26524/cm61.
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In this paper, we find the optimal solution for an unbalanced intuitionistic fuzzy transportation problem by using monalisha’s approximation method. The main aim of this method is to avoid large number of iterations. To illustrate this method a numerical example Triangular intuitionistic fuzzy number, unbalanced intuitionistic fuzzy transportation problem, accuracy function.is given.
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Muthukumar, S., R. Srinivasan, and V. Vijayan. "An optimal solution of unbalanced octagonal fuzzy transportation problem." Materials Today: Proceedings, July 2020. http://dx.doi.org/10.1016/j.matpr.2020.06.375.
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"An Improved Algorithm for Optimal Solution of Unbalanced Transportation Problems." Mathematical Theory and Modeling, December 2020. http://dx.doi.org/10.7176/mtm/10-8-02.
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Rathi 1, K., and S. Muruganantham 2. "Absolute point algorithm for solving unbalanced fuzzy transportation problem." Transportation Management 1, no. 2 (July 13, 2018). http://dx.doi.org/10.24294/tm.v1i2.573.
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In real time situations, the total availability of goods or product may be more or less than the actual market demand and the unbalanced transportation situation arise more commonly. Such unbalanced Transportation Problems (TP) are solved by introducing dummy source or destination which do not exist in reality. The optimal allocation involves cells from such dummy source or destination and the allocated number of quantities are held back at one or more origins. The paper aims to propose an algorithm based on Absolute Points to solve unbalanced TP under fuzzy environment. The proposed algorithm is advantageous than the existing algorithms in such a way that it provides the added information of transporting the excess availability from dummy supply point to appropriate destination to meet future demands at minimum cost. Finally, by virtue of the proposed algorithm an example is done to illustrate the practicality and the effectiveness of the proposed algorithm.
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Jamali, Sanaullah, Muhammad Mujtaba Shaikh, and Abdul Sattar Soomro. "Overview of Optimality of New Direct Optimal Methods for the Transportation Problems." Asian Research Journal of Mathematics, November 13, 2019, 1–10. http://dx.doi.org/10.9734/arjom/2019/v15i430160.
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In this paper, we investigate the claimed optimality of a new method – Revised Distribution (RDI) Method – for finding optimal solution of balanced and unbalanced transportation models directly and compare the RDI method with other such methods. A large set of problems have been tested by RDI and other methods, and the results were compared with the Modified distribution (MODI) method – an optimal method. We found that the mostly the results of RDI are not optimal. For reference to prove our observations, we have added three example transportation problems here in this work and compared their results with MODI method to show that the RDI method like the direct exponential approach (DEA) method is not optimal method; but it is just an initial basic feasible solution (IBFS) for transportation problems.
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Kalhoro, Huzoor Bux. "A PIONEERING AND COMPREHENSIVE DATABASE OF BALANCED AND UNBALANCED TRANSPORTATION PROBLEMS FOR READY PERFORMANCE EVALUATION OF EXISTING AND NEW METHODS." JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES 15, no. 11 (November 27, 2020). http://dx.doi.org/10.26782/jmcms.2020.11.00013.
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In this paper, we present a comprehensive database of the data tables of some important transportation problems from literature, and experience with the proposition of new initial basic feasible (IBF) solution methods for the transportation problems. The paper contains a comprehensive database of 140 transportation problems, of which 103 are balanced, 25 are unbalanced and 12 are from research papers. The detailed description of the varying-nature test problems is described, and the optimal solutions of the 140 problems have been obtained by using the TORA software with the modified distribution (MODI) method. The algorithms of three methods: North-West-Corner (NWCM), Least cost (LCM) and Vogel’s approximation (VAM) have been used for IBF solutions. The final optimal results are also quoted for the ready reference of researchers and practitioners. The database of problems and their optimal solutions will be a great aid to researchers and practitioners working with the existing and new methods for solving transportation problems. A pioneering investigation of the performance evaluation of NWCM, LCM and VAM has also been conducted as a benchmark for the similar assessment of other existing and forthcoming IBF and /or optimal solution methods for the transportation problems.
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Prokudin Georgii, Chupaylenko Оleksii, Prokudin Оleksii, and Khobotnia Tetiana. "MANAGEMENT DECISION SUPPORT SYSTEM FREIGHT TRANSPORTATION ON TRANSPORT NETWORKS." European Journal of Intelligent Transportation Systems, no. 1(3) (January 30, 2021). http://dx.doi.org/10.31435/rsglobal_ejits/30032021/7352.
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The existing methods of reducing open transport problems to a balanced form, namely: the method of fictitious supplier / consumer, the difference method and the method of coefficients. The transport problem is a special case of the general problem of linear programming, so it is also possible to apply the most well-known method of solving problems of this class - the simplex method, pre-grafting the transport problem to the form of linear programming problem and taking into account its specificity. Experimental studies on the optimization of unbalanced freight traffic, which were obtained using the developed decision support system, allow us to conclude that the simplex method has shown high efficiency in finding optimal freight plans for both balanced and unbalanced transport problems. Analysis of the results of the use of all methods on many practical examples allows us to conclude that none of them has an absolute advantage over others. This fact is explained by the fact that each of the methods in some cases of its application showed a better result compared to the results of others.
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"Newly Proposed Matrix Reduction Technique Under Mean Ranking Method for Solving Trapezoidal Fuzzy Transportation Problems Under Fuzzy Environment." Advances in Machine Learning & Artificial Intelligence 3, no. 1 (March 20, 2022). http://dx.doi.org/10.33140/amlai.03.01.03.
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In this paper, improved matrix Reduction Method is proposed for the solution of fuzzy transportation problem in which all inputs are taken as fuzzy numbers. Since ranking fuzzy number is important tool in decision making, Fuzzy trapezoidal number is converting in to crisp set by using Mean techniques and solved by proposed method for fuzzy transportation problem. We give suitable numerical example for unbalanced and compare the optimal value with other techniques. The Result shows that the optimum profit of transportation problem using proposed technique under robust ranking method is better than the other method. Novelty: The numerical illustration demonstrates that the new projected method for managing the transportation problems on fuzzy algorithms.
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Sudirga, Rudy Santosa. "DUA HASIL OPTIMAL DALAM PENYELESAIAN PERSOALAN TRANSPORTASI DENGAN ASSIGNMENT METHOD, VAM AND MODI, NORTHWEST CORNER AND STEPPING-STONE." Business Management Journal 8, no. 1 (July 25, 2017). http://dx.doi.org/10.30813/bmj.v8i1.617.
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<p>The famous method to determine and solve transportation problem is the transportation model and the assignment model. We see how to develop an initial solution to the transportation problem with VAM (Vogel’s Approximation Method) and MODI (Modified Distribution) and Northwest Corner rule and the Stepping-Stone method. VAM is not quite as simple as the Northwest Corner approach, but it facilitates a very good initial solution, as a matter of fact, one that is often the optimal solution. The Assignment method, which is simple and faster to solve the transportation problem by reducing the numbers (cost) in the table/tableau until a series of zeros is found, or zero opportunity costs, which means that we will reach the optimal cost allocations. Once we have reached the optimal cost allocations, we then allocate each sources or supply according to some points of demand (destinations). Assignment Method is a specialized form of optimization linear programming model that attempts to assign limited capacity to various demand points in a way that minimizes costs. The special cases of transportation problem included degeneracy (a condition that occurs when the number of occupied squares in any solution is less than the number of rows plus the number of columns minus 1 in a transportation table), unbalanced problems, and multiple optimal solutions. At this opportunity, we would like to demonstrate the multiple optimal solutions. We will see how the Assignment method may be viewed as a special case of solving the transportation problem.</p><p> </p><p>Keywords : Assignment method, VAM and MODI, Northwest Corner and Stepping-Stone.</p>
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Sudirga, Rudy Santosa. "TIGA JAWABAN PENYELESAIAN OPTIMAL DALAM PROBLEM TRANSPORTASI DENGAN VAM AND MODI METHOD." Business Management Journal 9, no. 2 (August 2, 2017). http://dx.doi.org/10.30813/bmj.v9i2.748.
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<p>The famous method to determine and solve transportation problem is the transportation model VAM and MODI method, the Northwest-Corner and Stepping-Stone method, and the Assignment method. We see how to develop an initial aolution to the transportation problem with VAM (Vogel’s Approximation Method) and MODI (Modified Distribution). VAM is not quite as simple as the Northwest Corner approach. But it facilitates a very good initial solution, as a matter of fact, one that is often the optimal solution.</p><p>VAM method trackles the problem of finding a good initial solution by taking into account the cost the costs associated with each route alternative. This is something that Northwest Corner Rules does not do. To apply VAM, we first compute for each row and column the penalty faced if we should ship over the second-best route instead of the least-cost route. After the initial of VAM solution has been found, you should evaluate it with either the Stepping-Stone method or the MODI method. The MODI (Modified Distribution) method allows us to compute improvement indices quickly for each unused square without drawing all of the closed paths. Because of this, it can often provide considerable time savings over the Stepping-Stone method for solving transportation problems. If there is a negative index indicating an imporovemet can be made, then only one Stepping-Stone path must be found. This is used as it was before to determine what changes should be made to obtain the improved solution.</p><p>In the Northwest-corner rule, the largest possible allocation is made to the cell in the upper left-hand corner of the tableau, followed by allocations to adjacent feasible celss. While the Stepping-stone method is an interactive technique for moving from an initial feasible solution to an optimal feasible solution, and continues will until the optimal solution is reached. The Stepping-stone path method is used to calculate improvement indices for the empty cells. Improved solutions are developed using a Stepping-stone path.</p><p>The assignment method, which is simple and faster to solve the transportation problem by reducing the numbers (cost) in the table/tableau until a series of zeros is found, or zero opportunity costs, which mean that we will reach the optimal cost allocations. Once we have reached the optimal cost allocations, we the allocate each sources or supply according to some point of demand (destinations). Assignment Method is a specialized form of optimization linear programming model that attempts to assign limited capacity to various demand points in a way that minimizes costs.</p><p>The special cases of transportation problem included degeneracy (a condition that occurs when the number of occupied squares in any solution is less than the number of rows plus the number of columns minus 1 in a transportation table), unbalanced problems, and multiple optimal solutions. We will see how the VAM and MODI method may be viewed as a special case of solving the multiple optimal solutions of the transportation problem.</p><p>Keywords : transportation, VAM and MODI</p>
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"Voltage Unbalance Reduction of a Local Transformer Area with Domestic Asymmetrical Inverter and Optimal Control Design." International Journal of Thermal and Environmental Engineering 12, no. 2 (2015). http://dx.doi.org/10.5383/ijtee.12.02.009.
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The three-phase unbalance problem can introduce additional losses in distribution networks due to both negative and zero sequence components, which leads to inefficient power consumption and increased CO2 emission of low voltage transformer area, moreover it causes safety possible malfunction of energy transportation networks. The aim of this paper is to introduce a new voltage unbalance indicator and a control algorithm that compensates source voltage unbalances. To examine the available measures of voltage asymmetry and make a comparison from the transient behaviours point of view. Analytical and simulation, results are presented to illustrate the effectiveness of the new techniques.
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"Impact of Transportation Problem’s Unbalance on Difference between Initial and Optimal Solution." Tehnicki vjesnik - Technical Gazette 26, no. 3 (June 2019). http://dx.doi.org/10.17559/tv-20170927100632.
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