Matching Problem Research Articles

The Operational Mechanism of Agricultural Products Supply Chain Finance Based on the Mode-Capability-Customer Matching Approach

The key to successful agricultural product supply chain finance of commercial banks lies in coordinating the relationship among the “supply chain finance model”, “banking serviceability”, and “user satisfaction”. Previous research on agricultural products supply chain finance rarely incorporated the operational level of commercial banks. To fill this gap, this paper transforms the operational problem of the agricultural product supply chain finance of commercial banks into a matching problem between the agricultural product supply chain finance model and customers under the constraint of serviceability. First, a matching model of “mode-capability-customer” of agricultural products supply chain finance is constructed based on the theory of bilateral matching; second, a measurement index system of matching degree and satisfaction is constructed, where matching degree and satisfaction are calculated; finally, the optimal matching model is studied. This paper proposes a model that matches agricultural supply chain finance with customer satisfaction, considering bank serviceability constraints. It improves the implementation of agricultural product supply chain finance by commercial banks. The study uses surveys and evaluations to assess stakeholder satisfaction and preferences. An operational research model optimizes the matching process between supply chain finance models and recipients, ensuring suitability. The paper provides a theoretical basis for rural financial services and practical recommendations for commercial bank supply chain finance, with valuable real-world applications.

Application of Various Mechanisms on Students School Choice Problem

School choice mechanisms are used to determine how to assign students to the public schools. These mechanisms take into account the students preferences order and the schools priority order to calculate an assignment that meets the needs and preferences of both students and schools. This paper sketches the school choice problem and its real-life condition in some cities, discusses the theory background of school choice problem and its specialty as a one-side matching problem. Also, this paper introduces the Defer Acceptance (DA) mechanism, the Top Trading Cycle (TTC) mechanism, the Boston (BOS) mechanism and the Serial Dictatorship (SD) mechanism. The paper analyzes four different mechanisms desirable properties, including strategy-proofness, stability and Pareto efficiency. Meanwhile, the flaws of the mechanisms are introduced. At last, based on the analysis and the purpose of improving students welfare, the paper raises some suggestion for the government, schools and parents, ensuring the students a transparent, fair and equal environment of choosing their schools.

Energy landscapes of some matching-problem ensembles

The maximum-weight matching problem and the behavior of its energy landscape is numerically investigated. We apply a perturbation method adapted from the analysis of spin glasses. This method provides insight into the complexity of the energy landscape of different ensembles. Erdős–Rényi graphs and ring graphs with randomly added edges are considered, and two types of distributions for the random edge weights are used. Fast and scalable algorithms exist for maximum weight matching, allowing us to study large graphs with more than 105 nodes. Our results show that the structure of the energy landscape for standard ensembles of matching is simple, comparable to the energy landscape of a ferromagnet. Nonetheless, for some of the ensembles presented here, our results allow for the presence of complex energy landscapes in the spirit of a replica-symmetry breaking scenario.

A stakeholder decision and road-network-based deterministic model to simulate material flows in regional scale industrial symbioses

Industrial Symbiosis (IS) projects are today well known for their contribution to sustainable development. Designing IS projects implies modelling the quantity of material flows between economic components (ECs), in particular to evaluate the environmental and economic gains obtained through more material circularity and use efficiency. The Industrial Symbiosis concept is now being applied to activities distributed over territories much wider than industrial parks. For such setups, existing modelling methods are becoming strained by the number of economic components involved, their dissemination within heterogeneous landscapes and the resulting complexity of the exchanges. In this paper, we propose a generic, spatially explicit simulation model based on stakeholder decision rules to study regional scale flows quantification between ECs in n-n IS networks. Our approach introduces the use of the Gale-Shapley stable matching algorithm for addressing the many-to-many supply-demand matching problem. The method is deterministic and provides stable solutions that optimise the individual objectives of the ECs. A second introduction considers using transport cost along the road network among the decision rules when ECs preferences are ordered. This solves the issue of correctly estimating travel distance in heterogeneous landscapes where it can be significantly different to the distance as the crow flies. We tested the model with the implementation of two real and concrete case studies related to the bioeconomy of a tropical island: (i) collective storage of fodder and (ii) co-composting platforms. The outputs of the model make it possible to compare scenarios based on EC satisfaction relative to supply and demand as well as the total distance travelled along the road network. Finally, we discuss the limitations and genericity of the model to explore diverse industrial symbiosis projects.

Singles monotonicity and stability in one-to-one matching problems

We propose a new axiom called “own-side singles monotonicity” in one-to-one matching problems between men and women. Suppose that there is an agent who is not matched in a problem. Suppose for simplicity it is a woman. Now in a new problem, we improve (or leave unchanged) her ranking for each man. Own-side singles monotonicity requires that each woman should not be made better off (except for her). If we focus on improving the ranking of an unmatched woman, then the men-optimal stable solution satisfies this property. By contrast, (if the gender of an unmatched agent is not specified), no single-valued solution satisfies own-side singles monotonicity and stability. However, there is a multi-valued solution, the stable solution, that does. We show that the stable solution is the unique solution satisfying weak unanimity, null agent invariance, own-side singles monotonicity, and consistency, where consistency can be replaced by Maskin invariance.

Bit duplication technique to generate hard quadratic unconstrained binary optimization problems with adjustable sizes

SummaryQuadratic unconstrained binary optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. The main contribution of this article is to propose the bit duplication technique that can specify the number of duplicated bits, so that it can generate hard QUBO problem with adjustable sizes. The idea is to duplicate specified number of bits and then to give constraints so that the corresponding two bits take the same binary values. By this technique, any QUBO problem with bits is converted to a hard QUBO problem with bits . We use random QUBO problems, N‐Queen problems, traveling salesman problem and maximum weight matching problems for experiments. The performance of QUBO solvers including Gurobi optimizer, Fixstars Amplify AE, OpenJij with SA, D‐Wave samplers with SA, D‐Wave hybrid and ABS2 QUBO solver are evaluated for solving these QUBO problems. The experimental results show that only a small scale of duplicated bits can make QUBO problems harder. Hence, the bit duplication technique is a potent method to generate hard QUBO problems and generated QUBO problems can be used as benchmark problems for evaluating the search performance of QUBO solvers.

Performance Improvement of a Direct Carbon Fuel Cell through an Irreversible Vacuum Thermionic Generator

A novel hybrid system consisting of a direct carbon fuel cell (DCFC), a thermionic generator (TIG), and a regenerator is developed to recover the exhaust heat from the fuel cell. Expressions for the power output and efficiency of subsystems and the hybrid system are derived. Based on the energy balance equation, the area matching problem between the DCFC and the TIG is discussed and solved. By considering the main irreversibilities, the influences of the DCFC’s current density and the TIG’s voltage on the performance of the hybrid system are revealed. The maximum power output density and the corresponding efficiency of the hybrid system are, respectively, equal to 379 W/m2 and 36%. To enhance the maximum power density of the single DCFC, the optimal regions of the main parameters are determined.

Collaborative Diffusion Based on Value Measurement in Social–Physical Networks

In the study of information diffusion in social-physical networks, existing works are usually based on information entropy. These works measure and represent the information attribute characteristics independently for social networks and physical networks, resulting in ineffective interactions and waste of resources. Therefore, to solve the key problem of the mismatch between interaction demands and communication resources, the framework of collaborative diffusion based on value measurement is proposed in social-physical networks, including social-physical interaction, cognitive difference and mutual trust degree of nodes. Based on parameterizing the relative strength of these influences by confidence and collaborative conservation factors, the collaborative diffusion model based on value measurement is established. Extensive simulations verify the influence of value measurement on the collaboration diffusion process, presented by the evolutions of value entropy, sentiment fragmentation and diffusion range. In addition, the influence of collaboration on information dissemination is confirmed by the comparison of the change of value entropy and diffusion range. These results can help decision-makers better balance the matching problem between interaction demands and available resources, which is beneficial to realize customized information diffusion.

G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics

BackgroundLiquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.ResultsTo comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC–MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034ConclusionsIn this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC–MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Fast estimation of the look-elsewhere effect using Gaussian random fields

We discuss the use of Gaussian random fields to estimate the look-elsewhere effect correction. We show that Gaussian random fields can be used to model the null-hypothesis significance maps from a large set of statistical problems commonly encountered in physics, such as template matching and likelihood ratio tests. Some specific examples are searches for dark matter using pixel arrays, searches for astronomical transients, and searches for fast-radio bursts. Gaussian random fields can be sampled efficiently in the frequency domain, and the excursion probability can be fitted with these samples to extend any estimation of the look-elsewhere effect to lower p values. In addition, in cases where the Gaussian random field is stationary and the parameter space is Euclidean, the look-elsewhere effect correction can be computed analytically. We demonstrate these methods using two example template matching problems. Finally, we apply these methods to estimate the trial factor of a 43\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4^3$$\\end{document} accelerometer array for the detection of dark matter tracks in the Windchime project. When a global significance of 3σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\\sigma $$\\end{document} is required, the estimated trial factor for such an accelerometer array is 1014\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{14}$$\\end{document} for a one-second search, and 1022\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{22}$$\\end{document} for a 1-year search.

Cost-based Data Prefetching and Scheduling in Big Data Platforms over Tiered Storage Systems

The use of storage tiering is becoming popular in data-intensive compute clusters due to the recent advancements in storage technologies. The Hadoop Distributed File System, for example, now supports storing data in memory, SSDs, and HDDs, while OctopusFS and hatS offer fine-grained storage tiering solutions. However, current big data platforms (such as Hadoop and Spark) are not exploiting the presence of storage tiers and the opportunities they present for performance optimizations. Specifically, schedulers and prefetchers will make decisions only based on data locality information and completely ignore the fact that local data are now stored on a variety of storage media with different performance characteristics. This article presents Trident, a scheduling and prefetching framework that is designed to make task assignment, resource scheduling, and prefetching decisions based on both locality and storage tier information. Trident formulates task scheduling as a minimum cost maximum matching problem in a bipartite graph and utilizes two novel pruning algorithms for bounding the size of the graph, while still guaranteeing optimality. In addition, Trident extends YARN’s resource request model and proposes a new storage-tier-aware resource scheduling algorithm. Finally, Trident includes a cost-based data prefetching approach that coordinates with the schedulers for optimizing prefetching operations. Trident is implemented in both Spark and Hadoop and evaluated extensively using a realistic workload derived from Facebook traces as well as an industry-validated benchmark, demonstrating significant benefits in terms of application performance and cluster efficiency.

An improved acoustic transponder system via LCCLC impedance matching network and equalization control strategy

AbstractThe underwater acoustic transducer (UAT) generates a large amount of reactive power during the operation, greatly limiting the output capability of the power amplifier. It is necessary to match the UAT and improve its load characteristics. The conventional design methods are not powerful enough to solve nonlinear or parameter‐sensitive problems, lacking enough convergence ability for the network design problem. This article proposes an optimization design method for LCCLC impedance matching network based on the improved chaos optimization algorithm and the corresponding equalization control strategy. First, the impedance model of the UAT is proposed, and then the matching principle of the LCCLC network is analyzed. Based on these, the optimization design for LCCLC network is described in detail. Then, to solve the problem of the calculation of the command signal, a control method based on the FIR filter principle is proposed. Finally, the proposed methods are verified by an experimental platform. The results show that the proposed design method and its corresponding control strategy successfully solve the matching and emission problems.

Match of the cross-period capacity on the third-party shared manufacturing platform with demand delay

In this paper, we aim at the cross-period matching problem on a third-party shared manufacturing platform (TSMP) that provides matching services for capacity demanders (CDs) and capacity suppliers (CSs). Considering the impact of delay in meeting demand, we formulate a bi-level multi-objective optimization model (BMOM) with the objective functions of maximizing the manufacturers’ matching rate and the TSMP’s commission revenue, in which the lower-level model optimizes the matching strategy for the current period, and the upper-level model matches the cross-period demand (i.e., the demand with delay). Then, we propose a method that combines the ε-constraint and a three-stage solution algorithm to solve the BMOM, and the performance of the proposed method is verified by comparing it with the optimization solver CPLEX in numerical experiments. The results of numerical experiments show that the method proposed in the paper has advantages in computational effectiveness and efficiency and can solve the current-period and cross-period joint matching problem of the TSMP, which provides a reference method for TSMPs to optimize the matching strategies.

Population-wise labeling of sulcal graphs using multi-graph matching.

Population-wise matching of the cortical folds is necessary to compute statistics, a required step for e.g. identifying biomarkers of neurological or psychiatric disorders. The difficulty arises from the massive inter-individual variations in the morphology and spatial organization of the folds. The task is challenging both methodologically and conceptually. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of the multi-graph matching framework in this context. We then present a procedure for generating populations of artificial sulcal graphs, which allows us to benchmark several state-of-the-art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques in obtaining a population-wise consistent labeling of cortical folds at the sulcal basin level.

Improved Chimp Optimization Algorithm for Matching Combinations of Machine Tool Supply and Demand in Cloud Manufacturing

Cloud manufacturing is a current trend in traditional manufacturing enterprises. In this environment, manufacturing resources and manufacturing capabilities are allocated to corresponding services through appropriate scheduling, while research on the production shop floor focuses on realizing a basic cloud manufacturing model. However, the complexity and diversity of tasks in the shop floor supply and demand matching environment can lead to difficulties in finding the optimal solution within a reasonable time period. To address this problem, a basic model for dynamic scheduling and allocation of workshop production resources in a cloud-oriented environment is established, and an improved Chimp optimization algorithm is proposed. To ensure the accuracy of the solution, two key improvements to the ChOA are proposed to solve the problem of efficient and accurate matching combinations of tasks and resources in the cloud manufacturing environment. The experimental results verify the effectiveness and feasibility of the improved ChOA (SDChOA) using a comparative study with various algorithms and show that it can solve the workshop supply and demand matching combination problem and obtain the optimal solution quickly.

Popular critical matchings in the many-to-many setting

We consider the many-to-many bipartite matching problem in the presence of two-sided preferences and two-sided lower quotas. The input to our problem is a bipartite graph G=(A∪B,E), where each vertex in A∪B specifies a strict preference ordering over its neighbours. Each vertex has an upper quota denoting the maximum number of vertices that can be assigned to it. In addition, each vertex has a lower quota denoting the minimum number of vertices that need to be assigned to it. In the many-to-many setting with two-sided lower quotas, informally, a critical matching is a matching which fulfils vertex lower quotas to the maximum possible extent. This is a natural generalization of the definition of critical matching in the one-to-one setting considered by Kavitha (FSTTCS 2021). In this work, our goal is to find a popular matching in the set of critical matchings. A matching is popular in a given set of matchings if it remains undefeated in a head-to-head election with any matching in that set. Here, vertices cast votes between pairs of matchings. We show that there always exists a matching that is popular in the set of critical matchings. We present an efficient algorithm to compute such a matching of the largest size. We prove the popularity of our matching using a dual certificate.

Optimal transport methods for combinatorial optimization over two random point sets

We investigate the minimum cost of a wide class of combinatorial optimization problems over random bipartite geometric graphs in Rd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {R}^d$$\\end{document} where the edge cost between two points is given by a pth power of their Euclidean distance. This includes e.g. the travelling salesperson problem and the bounded degree minimum spanning tree. We establish in particular almost sure convergence, as n grows, of a suitable renormalization of the random minimum cost, if the points are uniformly distributed and d≥3,1≤p<d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d \\ge 3, 1\\le p<d$$\\end{document}. Previous results were limited to the range p<d/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<d/2$$\\end{document}. Our proofs are based on subadditivity methods and build upon new bounds for random instances of the Euclidean bipartite matching problem, obtained through its optimal transport relaxation and functional analytic techniques.

Scene Matching Techniques Using Satellite Imagery Data

The problem of scene matching is a challenging problem in the field of image processing and pattern recognition. Therefore, it is modeled and its influencing factors are analyzed. According to sources, influence factors can be catalogued into three types: 1- changes of scenes 2- changes of image conditions 3- changes of sensors. For each factor, its mechanism is discussed. Given a pictorial description of a region of a scene, it is desired to determine which region in another scene is similar. The most efficient algorithms for scene matching are discussed. Those are the sequential hierarchical scenes matching algorithms for grey-scale and binary images and the two-stage template-matching algorithm. Experimental results are presented for matching satellite images of AI-Minea (EGYPT) and Montana (USA) using those approaches. The results prove efficiency and success in reaching the best match location with minimum required computations. A comment on the results is presented as well as a comparison between the applied methods.

Medicine-Shelf matching strategy based on Bayesian convolutional neural network with fuzzy analytic hierarchy process

In pharmaceutical warehousing operations, a scientific and reasonable medicine and shelf matching strategy can improve medicine shelving efficiency and manual work efficiency. However, the traditional matching strategy has the problems of static matching and low matching efficiency, so this paper proposes the matching algorithm for drugs and shelves based on the fuzzy analytic hierarchy process and Bayesian convolutional neural network (FAHP-BCNN). First, we propose a drug-shelf matching degree model and discuss the influence of the dynamic matching process on the matching results by studying two parts: the attribute matching degree and the dynamic matching influence degree. Secondly, this paper quantitatively assessed the importance of the two matches by means of the fuzzy analytic hierarchy process. Finally, we mapped the massive matching information onto Bayesian convolutional neural network nodes, constructed a dynamic matching network model, and obtained competitive matching strategies and results. The experimental results show that the FAHP-BCNN algorithm exhibits better matching results in all three scenarios with different quantity ratios of drugs and shelf matching. Compared with the fixed cargo matching strategy of the ABC classification method, the FAHP-BCNN algorithm shows excellent performance in both the manual walking distance index and fatigue index. In summary, the drug-shelf matching algorithm based on FAHP-BCNN is effective and can provide theoretical support for pharmaceutical warehousing enterprises to perform drug-shelf matching.

Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition

Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem. To model such problem, we propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM), in which Graph Matching mechanism is introduced owing to its good performance of excavating the instance and label relationship. The framework explicitly establishes category correlations and instance-label correspondences by modeling the relation among content-aware (instance) and semantic-aware (label) category representations, to facilitate multi-label image understanding and reduce the dependency of large amounts of training samples for each category. Specifically, we first construct an instance spatial graph and a label semantic graph respectively and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Empirical results conducted on generic multi-label image recognition demonstrate the superiority of our proposed method. Moreover, the proposed method also shows advantages in multi-label recognition with partial labels and multi-label few-shot learning, as well as outperforms current state-of-the-art methods with a clear margin. Our code is available at https://github.com/yananwu0510/ML-SGM.