Complex & Intelligent Systems最新文献_第2页

Enhancing implicit sentiment analysis via knowledge enhancement and context information

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-22 DOI: 10.1007/s40747-025-01840-w

Yanying Mao, Qun Liu, Yu Zhang

{"title":"Enhancing implicit sentiment analysis via knowledge enhancement and context information","authors":"Yanying Mao, Qun Liu, Yu Zhang","doi":"10.1007/s40747-025-01840-w","DOIUrl":"https://doi.org/10.1007/s40747-025-01840-w","url":null,"abstract":"Sentiment analysis (SA) is a vital research direction in natural language processing (NLP). Compared with the widely-concerned explicit sentiment analysis, implicit sentiment analysis (ISA) is more challenging and rarely studied due to the lack of sentiment words. However, existing implicit sentiment analysis methods are hard to identify implicit sentiment without the support of commonsense and contextual background. To address these limitations, we propose a knowledge-enhanced framework that integrates external knowledge graphs and contextual information for implicit sentiment analysis. We draw an analogy between the word in the target sentence and the knowledge graph entities and propose a retrieving and selecting method to automatically extract helpful knowledge graph entity embedding for implicit sentiment analysis. By introducing external knowledge from the knowledge graph, the proposed approach can extend semantic of implicit sentiment expressions. Then, a knowledge fusion module based on dynamic Coattention has been designed to integrate the extracted helpful knowledge with the context representation, effectively enriching the semantic representation of texts. The experiments on two implicit sentiment analysis datasets and two explicit sentiment analysis datasets prove that our model can achieve better performances in text sentiment analysis by fully utilizing external commonsense knowledge and context information.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"56 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reliability centred maintenance-oriented framework for modelling, evaluating, and optimising complex repairable flow networks

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-22 DOI: 10.1007/s40747-025-01787-y

Nicholas Kaliszewski, Romeo Marian, Javaan Chahl

{"title":"A reliability centred maintenance-oriented framework for modelling, evaluating, and optimising complex repairable flow networks","authors":"Nicholas Kaliszewski, Romeo Marian, Javaan Chahl","doi":"10.1007/s40747-025-01787-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01787-y","url":null,"abstract":"Few would argue that maximising the performance of the many flow networks (FNs) that operate for the benefit of our society and the economy is anything but essential. Through seeking to mitigate the risks posed by different asset failure modes, maintenance is critical to minimising disruptions and maximising resilience. Repairable flow network (RFN) optimisation and reliability centred maintenance (RCM) are both used to support asset related decisions in FNs but independently; meaning, attempts to maximise FN performance using RCM are likely to result in suboptimal outcomes. There is limited work bringing RFN optimisation and RCM together to support evaluating maintenance decisions in the context of holistic network level performance (measured in terms of profitability) and in a way that considers the complex structural and topological relationships that exist between process components, equipment components, and failure modes. Hence, this paper addresses this by developing the complex repairable flow network (CRFN) modelling framework with the goal of ensuring RFN optimisation integrates complex process and equipment (including failure modes) component topologies, such that it operates in alignment with the needs of RCM as part of maximising network flow in terms of gross profitability. This was done through the creation of a novel and transdisciplinary multi-layered network-based approach that integrates information from what were termed the facility, process, maintainable item, and failure mode levels. Furthermore, through running simulation experiments on an example CRFN, it is demonstrated that the CRFN modelling framework can be used to evaluate the impact different maintenance strategies have on maximising network flow in terms of gross profitability.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Co-evolutionary algorithm with a region-based diversity enhancement strategy 基于区域多样性增强策略的协同进化算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-22 DOI: 10.1007/s40747-025-01819-7

Kangshun Li, RuoLin Ruan, Shumin Xie, Hui Wang

{"title":"Co-evolutionary algorithm with a region-based diversity enhancement strategy","authors":"Kangshun Li, RuoLin Ruan, Shumin Xie, Hui Wang","doi":"10.1007/s40747-025-01819-7","DOIUrl":"https://doi.org/10.1007/s40747-025-01819-7","url":null,"abstract":"When addressing constrained multi-objective optimization problems, the presence of complex constraints often results in a non-connected feasible region, segmenting the Pareto front into multiple discrete segments. This fragmentation can significantly limit population diversity. To tackle this issue, we have designed two mechanisms aimed at preserving population diversity and have developed a constrained multi-objective co-evolutionary algorithm (DESCA) based on the framework of a two-population co-evolutionary algorithm. The proposed algorithm consists of two populations: a main population dedicated to exploring the constrained Pareto front and an auxiliary population tasked with exploring the unconstrained Pareto front. To sustain the diversity within both populations, the algorithm dynamically adjusts the genetic operator based on the observed states of the populations. Moreover, when the main population encounters stagnation, a regional mating mechanism is employed between the main population and the auxiliary population, accompanied by a relaxation of the constraints on the main population. Conversely, when the auxiliary population experiences stagnation, a diversity-first individual selection strategy is implemented; this strategy utilizes a regional distribution index to assess individual diversity and mitigates population stagnation by enhancing diversity. The performance of DESCA has been evaluated across 33 benchmark problems and 6 real-world problems. Experimental results demonstrate that DESCA exhibits strong competitiveness compared to seven other typical state-of-the-art algorithms.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"94 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-21 DOI: 10.1007/s40747-025-01779-y

Bin Yang, Lei Ding, Jianqiang Li, Yong Li, Guangzhi Qu, Jingyi Wang, Qiang Wang, Bo Liu

{"title":"Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification","authors":"Bin Yang, Lei Ding, Jianqiang Li, Yong Li, Guangzhi Qu, Jingyi Wang, Qiang Wang, Bo Liu","doi":"10.1007/s40747-025-01779-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01779-y","url":null,"abstract":"Digital medical imaging, particularly pathology images, is essential for cancer diagnosis but faces challenges in direct model training due to its super-resolution nature. Although weakly supervised learning has reduced the need for manual annotations, many multiple instance learning (MIL) methods struggle to effectively capture crucial spatial relationships in histopathological images. Existing methods incorporating positional information often overlook nuanced spatial correlations or use positional encoding strategies that do not fully capture the unique spatial dynamics of pathology images. To address this issue, we propose a new framework named TMIL (Transformer-based Multiple Instance Learning Network with 2D positional encoding), which leverages multiple instance learning for weakly supervised classification of histopathological images. TMIL incorporates a 2D positional encoding module, based on the Transformer, to model positional information and explore correlations between instances. Furthermore, TMIL divides histopathological images into pseudo-bags and trains patch-level feature vectors with deep metric learning to enhance classification performance. Finally, the proposed approach is evaluated on a public colorectal adenoma dataset. The experimental results show that TMIL outperforms existing MIL methods, achieving an AUC of 97.28% and an ACC of 95.19%. These findings suggest that TMIL’s integration of deep metric learning and positional encoding offers a promising approach for improving the efficiency and accuracy of pathology image analysis in cancer diagnosis.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"56 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143666250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SLPOD: superclass learning on point cloud object detection

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-21 DOI: 10.1007/s40747-025-01781-4

Xiaokang Yang, Kai Zhang, Yangyue Feng, Beibei Su, Yiming Cai, Kaibo Zhang, Zhiheng Zhang

{"title":"SLPOD: superclass learning on point cloud object detection","authors":"Xiaokang Yang, Kai Zhang, Yangyue Feng, Beibei Su, Yiming Cai, Kaibo Zhang, Zhiheng Zhang","doi":"10.1007/s40747-025-01781-4","DOIUrl":"https://doi.org/10.1007/s40747-025-01781-4","url":null,"abstract":"In the realm of point cloud object detection, classification tasks emphasize extracting common features to enhance generalization, often at the expense of individual-specific features. This limitation becomes particularly evident when handling intricate datasets like KITTI. Traditional models struggle to adequately capture individual-specific features, resulting in a scattered distribution of samples within the feature space and compromising the precision of object bounding boxes. To tackle this challenge, we introduce SLPOD, a Superclass-based point cloud object detection algorithm. Employing a siamese network structure, SLPOD conducts unsupervised clustering of samples within the same category to enhance the extraction of individual-specific features, thereby improving detection accuracy when confronted with complex datasets. Additionally, our approach integrates strategies such as voxel and point cloud feature fusion, global feature acquisition, and dynamic adjustment of sampling rates based on point sparsity, further enhancing the network’s capability to extract features. Experimental results demonstrate that SLPOD outperforms baseline algorithms in mean Average Precision on both KITTI and Waymo datasets, exhibiting robustness across diverse scenarios.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-19 DOI: 10.1007/s40747-025-01786-z

Aiting Yao, Huang Chen, Weiqi Zhang, Chengzu Dong, Meiqu Lu, Junjun Mao, Xiao Liu, Xuejun Li

{"title":"Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach","authors":"Aiting Yao, Huang Chen, Weiqi Zhang, Chengzu Dong, Meiqu Lu, Junjun Mao, Xiao Liu, Xuejun Li","doi":"10.1007/s40747-025-01786-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01786-z","url":null,"abstract":"With the rapid advancement of intelligent technologies and network environments, the efficient and accurate handling of uncertain decision-making information has become an urgent challenge. Traditional methods often struggle to process complex and incomplete information, especially in cyber defense. To address this, we introduce discrete multi-dimensional Z-numbers (MZs) as a mathematical tool for modeling uncertainty and reliability in network defense decisions. This paper proposes a synthesis method for MZs, enabling the integration of multi-source information while considering both uncertainty and reliability. By leveraging a hidden probability model, we extend MZs into multi-dimensional Z+-numbers, enhancing their expressiveness in handling uncertainty. Furthermore, we define utility functions based on MZs and develop a multi-attribute group decision-making framework tailored for network defense. This approach offers a novel perspective for designing strategies against highly adaptive and covert cyberattacks. The proposed method is validated through a case study on the network security assessment of an intelligent logistics company. Results demonstrate significant improvements in the accuracy and efficiency of decision-making, highlighting the method’s advantages and broad potential in cyber defense. Beyond logistics, this integrated MZ-based decision framework provides an adaptable and intelligent tool for strengthening network security defenses.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"183 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traffic signal optimization control method based on attention mechanism updated weights double deep Q network

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-19 DOI: 10.1007/s40747-025-01841-9

Huizhen Zhang, Zhenwei Fang, Youqing Chen, Haotian Dai, Qi Jiang, Xinyan Zeng

{"title":"Traffic signal optimization control method based on attention mechanism updated weights double deep Q network","authors":"Huizhen Zhang, Zhenwei Fang, Youqing Chen, Haotian Dai, Qi Jiang, Xinyan Zeng","doi":"10.1007/s40747-025-01841-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01841-9","url":null,"abstract":"As a critical guidance facility for vehicle convergence and diversion in urban traffic networks, the control effect of traffic signals directly affects traffic efficiency and road congestion level. As a mature deep reinforcement learning algorithm, the double deep Q network has shown a significant optimization effect in intelligent traffic signal control research. In this paper, for the feature extraction defects of deep double Q network and the problem of underestimating the evaluation value of actions, we propose an Attention Mechanism Updated Weights Double Deep Q Network (AMUW–DDQN) based on the attention mechanism for the optimal control of traffic signals. The AMUW–DDQN method enhances the perceptual ability of the network by introducing the attention mechanism of Squeeze And Excitation Networks (SENet) to make the neural network pay attention to important state components automatically, and based on the idea that accurate representation of potentially optimal action values is better than the balanced representation of all the action values, it is considered that underestimated actions have a certain probability of being the optimal action and the loss function is weighted to optimize the action values. Simulation experiments were also conducted using the traffic flow data of the intersection of Fengze Street–Tian’an South Road, Fengze District, Quanzhou City, Fujian Province, China. The experimental results show that the method proposed in this paper has the most significant final convergence effect for the same number of iterations, and has better performance in the evaluation indexes such as vehicle queue length and vehicle delay time.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A generative model-based coevolutionary training framework for noise-tolerant softsensors in wastewater treatment processes

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-17 DOI: 10.1007/s40747-025-01845-5

Yu Peng, Erchao Li

{"title":"A generative model-based coevolutionary training framework for noise-tolerant softsensors in wastewater treatment processes","authors":"Yu Peng, Erchao Li","doi":"10.1007/s40747-025-01845-5","DOIUrl":"https://doi.org/10.1007/s40747-025-01845-5","url":null,"abstract":"Data-driven softsensors have gained widespread application in process monitoring and quality prediction, offering advantages over traditional measurement techniques by mitigating their limitations and costs. However, the effectiveness of softsensor models is often hindered by noise in data acquisition, posing significant challenges for model training. To tackle this issue, this study introduces a coevolutionary training framework based on generative models to mitigate the impact of noise corruption. The framework employs a denoising variational autoencoder to extract global and local features from auxiliary data, enhancing population distribution and constructing a deep nonlinear representation to counter noise effects. Additionally, a dual population coding method inspired by evolutionary computation is proposed, enabling the coevolution of network parameters and structure. The proposed multiobjective evolutionary network optimization with denoising strategy (MENO-D) demonstrated exceptional performance in various experiments. On a water quality prediction dataset, the MENO-D-trained softsensor model achieved the lowest prediction error under 10% and 20% noise interference. Further, on the WWTP benchmark dataset across three weather conditions, MENO-D-trained softsensor model exhibited competitive accuracy and robustness.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-17 DOI: 10.1007/s40747-025-01828-6

Xiaoting Dong, Guangxi Wan, Peng Zeng

{"title":"A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints","authors":"Xiaoting Dong, Guangxi Wan, Peng Zeng","doi":"10.1007/s40747-025-01828-6","DOIUrl":"https://doi.org/10.1007/s40747-025-01828-6","url":null,"abstract":"Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize job sequencing and machine selection while ignoring the impact of AGV transportation, resulting in suboptimal scheduling solutions and even difficulties in implementation. To address this issue, this paper formulates a cooperative scheduling model by introducing the AGV scheduling problem into the classical FJS scheduling problem, abbreviated as the FJS-AGV problem, with the objective of minimizing the makespan. With respect to the FJS-AGV problem, a heuristic-assisted deep Q-network (HA-DQN) algorithm is proposed, which leverages heuristic rules to enable the decision agent to perform multiple actions at each decision point, which includes determining the responses to the following questions: Which operation should be processed next? On which machine? By which AGV? This decision mechanism enables the agent to make more informed decisions, leading to improved performance and resource allocation in the FJS-AGV system. The practicability of the proposed FJS-AGV model and the efficiency of the HA-DQN algorithm in solving the FJS-AGV problem are verified through various international benchmarks. Specifically, when solving instances in a large benchmark, the HA-DQN algorithm yields a significant 12.63% reduction in makespan compared with that when traditional heuristics are employed.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"37 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-03-17 DOI: 10.1007/s40747-025-01826-8

Yanling Chen, Jingyi Wei, Tao Luo, Jie Zhou

{"title":"Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems","authors":"Yanling Chen, Jingyi Wei, Tao Luo, Jie Zhou","doi":"10.1007/s40747-025-01826-8","DOIUrl":"https://doi.org/10.1007/s40747-025-01826-8","url":null,"abstract":"Vehicle routing is a critical issue in the logistics and distribution industry. In practical applications, optimizing vehicle capacity allocation can significantly improve route optimization performance and service coverage. However, solving this problem remains challenging due to the complex constraints involved. Therefore, to address this real-world challenge, a novel intelligent optimization method, multi-objective capacity adjustment ant colony optimization algorithm (MCAACO), is proposed, which integrates advanced multi-objective optimization strategies, including capacity adjustment operators and crossover operators. Combined with pheromone updating and Pareto front-end optimization, the method effectively resolves the conflict between vehicle capacity constraints and multi-objective optimization. To further enhance the algorithm’s performance, dynamic pheromone updating mechanisms and elite individual retention strategies are proposed. Additionally, an adaptive parameter adjustment strategy is designed to balance global search and local exploitation capabilities. Through a series of experiments, it is demonstrated that compared to multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II), and multi-objective sparrow search algorithm (MOSSA), the proposed MCAACO significantly reduces travel paths by an average of 3.05% and increases vehicle service coverage by an average of 3.2%, while satisfying vehicle capacity constraints. Experimental indicators demonstrate that the breakthrough algorithm significantly addresses the issues of high costs and low efficiency prevalent in the practical logistics distribution industry.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"69 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0