Two-Sided Matching Decision Making with Multi-Attribute Probabilistic Hesitant Fuzzy Sets

In previous research on two-sided matching(TSM)decision,agents’preferences were often given in the form of exact values of ordinal numbers and linguistic phrase term sets.Nowdays,the matching agent cannot perform the exact evaluation in the TSM situations due to the great fuzziness of human thought and the complexity of reality.Probability hesitant fuzzy sets,however,have grown in popularity due to their advantages in communicating complex information.Therefore,this paper develops a TSM decision-making approach with multi-attribute probability hesitant fuzzy sets and unknown attribute weight information.The agent attribute weight vector should be obtained by using the maximum deviation method and Hamming distance.The probabilistic hesitancy fuzzy information matrix of each agent is then arranged to determine the comprehensive evaluation of two matching agent sets.The agent satisfaction degree is calculated using the technique for order preference by similarity to ideal solution(TOPSIS).Additionally,the multi-object programming technique is used to establish a TSM method with the objective of maximizing the agent satisfaction of two-sided agents,and the matching schemes are then established by solving the built model.The study concludes by providing a real-world supply-demand scenario to illustrate the effectiveness of the proposed method.The proposed method is more flexible than prior research since it expresses evaluation information using probability hesitating fuzzy sets and can be used in scenarios when attribute weight information is unclear.

Two-Sided Stable Matching Decision-Making Method Considering Matching Intention under a Hesitant Fuzzy Environment

In this paper,a stable two-sided matching(TSM)method considering the matching intention of agents under a hesitant fuzzy environment is proposed.The method uses a hesitant fuzzy element(HFE)as its basis.First,the HFE preference matrix is transformed into the normalized HFE preference matrix.On this basis,the distance and the projection of the normalized HFEs on positive and negative ideal solutions are calculated.Then,the normalized HFEs are transformed into agent satisfactions.Considering the stable matching constraints,a multiobjective programming model with the objective of maximizing the satisfactions of two-sided agents is constructed.Based on the agent satisfaction matrix,the matching intention matrix of two-sided agents is built.According to the agent satisfaction matrix and matching intention matrix,the comprehensive satisfaction matrix is set up.Furthermore,the multiobjective programming model based on satisfactions is transformed into a multiobjective programming model based on comprehensive satisfactions.Using the G-S algorithm,the multiobjective programming model based on comprehensive satisfactions is solved,and then the best TSM scheme is obtained.Finally,a terminal distribution example is used to verify the feasibility and effectiveness of the proposed method.

Geological adaptability matching design of disc cutter using multicriteria decision making approaches

Geological adaptability matching design of a disc cutter is the prerequisite of cutter head design for tunnel boring machines(TBMs)and plays an important role in improving the tunneling efficiency of TBMs.The main purpose of the cutter matching design is to evaluate the cutter performance and select the appropriate cutter size.In this paper,a novel evaluation method based on multicriteria decision making(MCDM)techniques was developed to help TBM designers in the process of determining the cutter size.The analytic hierarchy process(AHP)and matter element analysis were applied to obtaining the weights of the cutter evaluation criteria,and the fuzzy comprehensive evaluation and technique for order performance by similarity to ideal solution(TOPSIS)approaches were employed to determine the ranking of the cutters.A case application was offered to illustrate and validate the proposed method.The results of the project case demonstrate that this method is reasonable and feasible for disc cutter size selection in cutter head design.

Fast inter-mode decision method based on best matching block for H.264 encoding

In H.264 encoder, all possible coding modes should be checked to choose the most appropriate mode for every macroblock, which adds a heavy computation burden to the encoder. In this paper, a fast inter-mode decision method is presented to reduce computation complexity of an H.264 encoder. By detecting the best matching block （BMB） before transform and quantization, some coding modes can be skipped and the corresponding encoding steps can be omitted for these BMBs. Meanwhile this method can also be used to detect all-zero blocks. The experimental results show that this method achieves consistently significant reduction of encoding time while keeping almost the same rate-distortion performance.

A Fuzzy Decision Based WSN Localization Algorithm for Wise Healthcare

Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.

A Novel Symbolic Algorithm for Maximum Weighted Matching in Bipartite Graphs

The maximum weighted matching problem in bipartite graphs is one of the classic combinatorial optimization problems, and arises in many different applications. Ordered binary decision diagram (OBDD) or algebraic decision diagram (ADD) or variants thereof provides canonical forms to represent and manipulate Boolean functions and pseudo-Boolean functions efficiently. ADD and OBDD-based symbolic algorithms give improved results for large-scale combinatorial optimization problems by searching nodes and edges implicitly. We present novel symbolic ADD formulation and algorithm for maximum weighted matching in bipartite graphs. The symbolic algorithm implements the Hungarian algorithm in the context of ADD and OBDD formulation and manipulations. It begins by setting feasible labelings of nodes and then iterates through a sequence of phases. Each phase is divided into two stages. The first stage is building equality bipartite graphs, and the second one is finding maximum cardinality matching in equality bipartite graph. The second stage iterates through the following steps: greedily searching initial matching, building layered network, backward traversing node-disjoint augmenting paths, updating cardinality matching and building residual network. The symbolic algorithm does not require explicit enumeration of the nodes and edges, and therefore can handle many complex executions in each step. Simulation experiments indicate that symbolic algorithm is competitive with traditional algorithms.

A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers’needs or CSPs’capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making(MCDM)method,called Analytic Hierarchy Process(AHP),to acquire the information of consumers’preferences and service providers’capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria,and matching each set of tasks/services to the best CSPS.

The Symbolic OBDD Algorithm for Finding Optimal Semi-matching in Bipartite Graphs

The optimal semi-matching problem is one relaxing form of the maximum cardinality matching problems in bipartite graphs, and finds its applications in load balancing. Ordered binary decision diagram (OBDD) is a canonical form to represent and manipulate Boolean functions efficiently. OBDD-based symbolic algorithms appear to give improved results for large-scale combinatorial optimization problems by searching nodes and edges implicitly. We present novel symbolic OBDD formulation and algorithm for the optimal semi-matching problem in bipartite graphs. The symbolic algorithm is initialized by heuristic searching initial matching and then iterates through generating residual network, building layered network, backward traversing node-disjoint augmenting paths, and updating semi-matching. It does not require explicit enumeration of the nodes and edges, and therefore can handle many complex executions in each step. Our simulations show that symbolic algorithm has better performance, especially on dense and large graphs.

The Aviation Technology Two-Sided Matching with the Expected Time Based on the Probabilistic Linguistic Preference Relations

The two-sided matching has been widely applied to the decision-making problems in the field of management.With the limited working experience,the two-sided agents usually cannot provide the preference order directly for the opposite agent,but rather to provide the preference relations in the form of linguistic information.The preference relations based on probabilistic linguistic term sets(PLTSs)not only allowagents to provide the evaluation with multiple linguistic terms,but also present the different preference degrees for linguistic terms.Considering the diversities of the agents,they may provide their preference relations in the form of the probabilistic linguistic preference relation(PLPR)or the probabilistic linguistic multiplicative preference relation(PLMPR).For two-sided matching with the expected time,we first provide the concept of the time satisfaction degree(TSD).Then,we transform the preference relations in different forms into the unified preference relations(u-PRs).The consistency index to measure the consistency of u-PRs is introduced.Besides,the acceptable consistent u-PRs are constructed,and an algorithm is proposed to modify the unacceptable consistent u-PRs.Furthermore,we present the whole two-sided matching decisionmaking process with the acceptable consistent u-PRs.Finally,a case about aviation technology suppliers and demanders matching is presented to exhibit the rationality and practicality of the proposed method.Some analyses and discussions are provided to further demonstrate the feasibility and effectiveness of the proposed method.

Heuristic solution using decision tree model for enhanced XML schema matching of bridge structural calculation documents

Research on the quality of data in a structural calculation document(SCD)is lacking,although the SCD ofa bridge is used as an essential reference during the entire lifecycle of the facility.XML Schema matching enables qualitative improvement of the stored data.This study aimed to enhance the applicability of XML Schema matching,which improves the speed and quality of information stored in bridge SCDs.First,the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs.The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations.Second,the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree.The decision tree model was built using the content elements stored in the SCD,design companies,bridge types,and weight factors as input variables,and the matching accuracy as the target variable.The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.

基于三边匹配理论的应急救援分配决策

为解决大型地质灾害背景下应急救援初期救援人员、应急救援装备与受灾地点的分配问题,提高应急救援的效率和质量,首先,将应急救援三方主体之间的选择过程描述为三边匹配问题;然后,标准化处理匹配三方主体的多属性偏好评价信息,构造多属性综合偏好得分矩阵,并以系统稳定性和总偏好效用最大化为目标,建立应急救援初期背景下的三边匹配满意稳定模型;最后,通过算例验证模型的可行性和有效性。结果表明:该模型考虑各主体偏好对于资源分配的影响,能实现应急救援背景下救援人员、应急救援装备与受灾地点三方主体的合理匹配,有助于缩短应急救援部署时间,提高应急救援的效率和质量。

面向多用户资源均衡分配的MEC计算卸载策略

移动边缘计算在资源受限地区的物联网部署上面临能源、算力、容量等资源限制的挑战,为保障网络QoS,提出一种任务分组和资源分配联合优化的两阶段计算卸载策略(TSCOS)。以卸载决策和资源分配为约束,设计联合优化卸载算法来降低时延、提高资源利用率。第一阶段基于任务偏好的待卸载计算任务分组模型,与候选服务器进行预匹配,提高计算卸载的精准度以降低匹配时间开销;第二阶段运用Gale Shapley算法快速计算多对多博弈最优匹配解集,实现边缘网络负载均衡。与距离分组模型相比,任务偏好的分组模型可以提高卸载精准度以及计算资源的利用率。将TSCOS策略与随机游走策略(RS)、贪心策略(GE)以及动态资源调度策略(FFS+IPFS)对比,实验结果表明,TSCOS在服务器负载均衡方面相比,方差缩小达到2~4倍,任务成功率提高5%~15%;在相同的能耗下,能效比提高5%~10%。任务平均接收率和卸载任务处理成功率达92%和96%。

基于GF-5卫星的西藏珠勒—芒拉地区矿物蚀变信息提取及找矿前景分析

【研究目的】近年来,遥感在地质调查和矿产勘查领域取得了广泛的应用,基于多光谱遥感数据的蚀变矿物填图为地质找矿工作提供了重要技术支撑,然而基于国产高光谱遥感数据在此领域的研究却为数不多。高分五号(GF-5)较小的波谱间隔提供了相比于多光谱更为丰富的目标地物波谱信息,为矿物的精细识别提供了良好的数据源。本文主要基于GF-5开展西藏革吉南地区的矿物蚀变信息提取,同时结合Landsat-8、ASTER多光谱数据提取结果叠加对比,综合野外调查验证,进一步深化遥感在地质矿产资源调查领域的应用。【研究方法】基于多光谱数据建立了不同类别蚀变矿物的光谱指数模型,在GF-5数据蚀变信息提取方面,摒弃了传统的光谱角匹配等方法,提出了基于决策树分类辅助混合调谐匹配滤波技术进行矿化蚀变信息的提取方法,最后综合区域构造、蚀变信息提取结果等要素,圈定成矿有利区,并开展野外调查验证。【研究结果】基于Landsat-8、ASTER两种多光谱数据对铁染、羟基类(Mg-OH、Al-OH)、碳酸盐类矿物信息进行了增强与提取;基于GF-5数据识别出了方解石、钠云母、普通白云母、多硅白云母、明矾石、高岭石、地开石、绿帘石8种蚀变矿物。【结论】结合不同数据源的提取与叠加结果,证实了本文提出的矿化蚀变信息提取方法的可行性。根据野外验证情况综合揭示了该地区发育高硫型浅成低温热液蚀变矿物组合,具有斑岩-浅成低温热液矿床的成矿潜力。本文认为高光谱与多光谱数据相结合有助于后续蚀变分带的分析与更精确的成矿预测,从而更好地服务于矿产勘查工程等领域。

基于马尔可夫决策的钢铁产成品订单分配模型研究

物流订单分配是钢铁产成品销售过程中的重要环节,对销售过程的整体体验和全流程的良性循环起着至关重要的作用。在实际生产过程中,传统的人工分单模式已难以适应长时间维度的发展要求。为了充分考虑一个时间周期内销售的物流成本最低以及承运司机的收益较高,同时确保企业的长期收益,本文用多目标约束,建立马尔可夫决策模型,引入KM算法执行二分图匹配,基于司机收益最大化和企业成本最小的多目标优化,最大化钢铁商品总交易量为长期的最终目标,结合价值函数和多属性的多目标优化函数,形成车辆和货物的完整匹配决策。以钢铁企业的真实业务数据为例,对数据进行预处理后筛选合适特征进行模型训练并验证算法的正确性和可用性。结果表明,该模型相较于传统的订单分配方法,可以更好地解决钢铁企业在订单分配场景下的需求。

面向异构传输需求的多无人机通信协同抗干扰决策方法

干扰条件下的多无人机通信中,用户根据业务有不同的通信需求,而各个信道质量也不同,对抗干扰通信决策提出了业务需求与资源匹配的新要求。针对外部对抗性干扰场景下,多个用户如何选择适合的信道和功率,在干扰躲避、内部冲突控制以及功率优化的同时实现有限资源的高效利用这一问题进行研究,提出了一种面向传输需求匹配的抗干扰决策方法。首先,利用部分重叠信道的特性进行信道资源复用;然后,运用多用户协同强化学习训练用户信道选择策略,并使用斯坦伯格博弈对功率进行优化,实现对用户异构传输需求进行资源匹配。所提方法能实现干扰条件下的多用户异构传输需求的信道和功率联合优化,提升资源利用合理性,实现传输效果的提升。

基于应急预案的煤矿应急救援辅助决策系统设计

针对煤矿应急救援辅助决策系统中应急预案应用不足、应用效率低及系统生成的救援方案可执行性欠佳等问题,提出了一种基于应急预案的煤矿应急救援辅助决策系统设计方法。该方法采用基于大语言模型的信息抽取技术,从应急预案中提炼出关键任务要素,如任务名称、触发条件、执行部门和任务内容等,形成元任务,并构建根据事故类型和级别对元任务进行分类存储的元任务库;发生煤矿安全事故时,运用基于SBERT模型的语义匹配技术,根据现场收集的信息进行事故分类分级,并从元任务库中筛选出与当前应急需求相符合的元任务集;为提高任务的可执行性,将元任务与实时采集的现场数据结合,通过指令模板构建具体的行动指令,并利用任务规划技术对指令的优先级进行优化和调整,生成切实可行的现场救援方案。基于应急预案的煤矿应急救援辅助决策系统充分利用了应急预案的规范化内容,形成了与现场信息紧密结合、资源优化的救援方案,进一步提高了救援决策的准确性、科学性和智能化水平。

土地价格对企业对外直接投资的影响——基于遗传匹配的微观证据

土地价格高企已成为影响企业对外直接投资的间接区位因素。本文基于我国上市公司对外直接投资数据与国家信息中心地块交易数据,运用遗传匹配算法,实证检验土地价格对企业对外直接投资的影响。研究结果表明,土地价格上涨将促进对外直接投资规模增加。一方面,地价上涨影响企业跨期购地决策,使企业在当期选择对外直接投资作为替代策略;另一方面,土地具有生产要素和投资品的双重属性,地价上涨能够提高企业全要素生产率和抵押额度,进而促进企业对外直接投资。行业、城市和地块层面的异质性均会影响地价对企业对外直接投资的促进效果。在排除内生性、避税行为影响以及更换核心被解释变量后,本文的实证结果仍然稳健。进一步分析发现,国内异地购地和租赁土地不能完全消除高地价带来的成本和竞争压力,企业仍然具有国际化经营的动机。本文结论为企业合理配置要素,促进国内产业结构升级,构建新发展格局提供有益参考。

一种基于多智能强化学习的车货匹配算法

文中针对网络货运企业“多对多”车货匹配问题,基于匹配决策过程,将车货匹配问题建模为一个多智能体马尔科夫决策过程,并利用一种基于多层感知器的代理网络、全连接层的混合网络和超参数网络的多智能体强化学习算法,来模拟并优化多智能体马尔科夫决策过程.基于真实网络货运平台车货匹配数据和不同规模的运输路网环境进行数值实验.结果表明:所提出的算法在解决大规模车货匹配问题具有较好性能.

水下重力匹配导航适配性评价方法比较研究

重力匹配导航精度受重力适配区选取的影响。针对如何有效筛选最优适配区,采用信息熵的单特征准则与多属性决策的多特征准则评价重力底图的适配性。选择重力标准差、相关系数、粗糙度、坡度、重力特征丰度与累加梯度均值6种参数,利用单/多特征两种准则筛选同一海域适配/非适配区,通过地形轮廓匹配(terrain contour matching, TERCOM)算法在所筛选区域进行重力匹配导航验证,对比分析两者的匹配导航精度。结果表明,单特征准则在适配/非适配区的导航误匹配率分别为30%和20%;多特征准则在适配区无明显误匹配,误差在百米级,在非适配区出现多次误匹配。分析表明,多特征分析准则与单特征分析准则相比,可更准确有效地评价重力匹配区的适配性。

基于BP神经网络算法的判罚对足球比赛成绩影响的预测模型研究

本文基于某省近3届运动会足球比赛的判罚数据,采用BP神经网络算法构建预测模型,分析判罚对足球比赛成绩影响的关联效应。对数据进行预处理和特征提取,并输入到神经网络中进行训练,构建出较为理想的预测模型。结果表明:(1)在BP网络选用的隐含层节点数为6时,其训练集的预测相对均方差较小,预测未来比赛结果的准确率达到了90%以上,且召回率和F1分数等评价指标均表现良好。(2)判罚情况与足球比赛成绩呈显著正相关,是影响足球比赛成绩的重要因素。本文为判罚对足球比赛成绩影响的预测提供了新思路,同时为判罚对其他体育比赛成绩的分析和预测奠定了理论基础。