复杂系统建模与仿真(英文)最新文献

{"title":"Spare Part Replenishment Strategy for Electronic Product Based on Model Predictive Control","authors":"Xingchang Fu;Chu-ge Wu;Bo Fu;Yuanqing Xia","doi":"10.23919/CSMS.2024.0027","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0027","url":null,"abstract":"After-sale service plays an essential role in the electronics retail industry, where providers must supply the required repair parts to consumers during the product warranty period. The rapid evolution of electronic products prevents part suppliers from maintaining continuous production, making it impossible to supply spare parts consistently during the warranty periods and requiring the providers to purchase all necessary spare parts on Last Time Buy (LTB). The uncertainty of customer demand in spare parts brings out difficulties to maintain optimal spare parts inventory. In this paper, we address the challenge of forecasting spare parts demand and optimizing the purchase volumes of spare parts during the regular monthly replenishment period and LTB. First, the problem is well defined and formulated based on the dynamic economic lotsize model. Second, a transfer function model is constructed between historical demand values and product sales, aiming to identify the length of warranty period and forecast the spare part demands. In addition, the linear Model Predictive Control (MPC) scheme is adopted to optimize the purchase volumes of spare part considering the inaccuracy in the demand forecasts. A real-world case considering different categories of spare parts consumption is studied. The results demonstrate that our proposed algorithm outperforms other algorithms in terms of forecasting accuracy and the inventory cost.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RAAN: A Gaussian Prior Domain Adaptive Network for Rolling Bearing Fault Diagnosis Under Variable Working Conditions","authors":"Kang Liu;Yang Yu;Yuanjiang Li;Tao Lang;Ruochen Liu","doi":"10.23919/CSMS.2024.0026","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0026","url":null,"abstract":"In the field of fault diagnosis for rolling bearings under variable working conditions, significant progress has been made using methods based on unsupervised domain adaptation (UDA). However, most existing UDA methods primarily achieve identification by directly aligning the distributions of the source and target domains, often overlooking the relevance of samples between different domains, which may result in incomplete extraction of deep features and alignment of feature distributions. Therefore, this study proposes a novel domain adaptation network based on Gaussian prior distributions, aiming at solving the challenges of cross working conditions bearing fault diagnosis. The method consists of a feature mining module and an adversarial domain adaptation module. The former effectively extracts deep features by stacking multiple residual networks (Resnet), while the latter employs an indirect latent alignment strategy, using Gaussian prior distributions in the latent feature space to indirectly align the feature distributions of the source and target domains, achieving more precise feature alignment. In addition, an adaptive factor is introduced to dynamically assess the method's transfer and discriminative capabilities. Experimental data from two bearing systems validate that the method can effectively transfer source domain knowledge to the target domain, confirming its effectiveness.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 1","pages":"16-33"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Total Contents","authors":"","doi":"10.23919/CSMS.2024.10821010","DOIUrl":"https://doi.org/10.23919/CSMS.2024.10821010","url":null,"abstract":"","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"I-II"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10821010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Optimization of Guidance Guardrail Layout Based on Multi-Objective Evolutionary Algorithm","authors":"Wei-Li Liu;Yixin Chen;Xiang Li;Jinghui Zhong;Rongjun Chen;Hu Jin","doi":"10.23919/CSMS.2024.0020","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0020","url":null,"abstract":"Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places. However, existing methods of the guardrail layout mainly rely on manual design or mathematical models, which are not flexible or effective enough for crowd control in large public places. To address this limitation, this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm. The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout. In addition to optimizing pedestrian passage efficiency and safety, the paper also considers the situation of pedestrian counterflow, making the guardrail layout more practical. Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion, as well as its significant improvements in passage efficiency and prevention of pedestrian counterflow.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"353-367"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics","authors":"Xiao Jing;Xin Pei;Pengpeng Xu;Yun Yue;Chunyang Han","doi":"10.23919/CSMS.2024.0016","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0016","url":null,"abstract":"Freeway logistics plays a pivotal role in economic development. Although the rapid development in big data and artificial intelligence motivates long-haul freeway logistics towards informatization and intellectualization, the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators. The present study thereby proposed intelligent algorithms for truck dispatching for freeway logistics. Specifically, our contributions include the establishment of mathematical models for full-truckload (FTL) and less-than-truckload (LTL) transportation modes, respectively, and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning. Simulation experiments based on the real-world freeway logistics data collected in Guiyang, China show that our algorithms improved operational profitability substantially with a 76% and 30% revenue increase for FTL and LTL modes, respectively, compared with single-stage optimization. These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"368-386"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph Pointer Network Based Hierarchical Curriculum Reinforcement Learning Method Solving Shuttle Tankers Scheduling Problem","authors":"Xiaoyong Gao;Yixu Yang;Diao Peng;Shanghe Li;Chaodong Tan;Feifei Li;Tao Chen","doi":"10.23919/CSMS.2024.0017","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0017","url":null,"abstract":"Shuttle tankers scheduling is an important task in offshore oil and gas transportation process, which involves operating time window fulfillment, optimal transportation planning, and proper inventory management. However, conventional approaches like Mixed Integer Linear Programming (MILP) or meta heuristic algorithms often fail in long running time. In this paper, a Graph Pointer Network (GPN) based Hierarchical Curriculum Reinforcement Learning (HCRL) method is proposed to solve Shuttle Tankers Scheduling Problem (STSP). The model is trained to divide STSP into voyage and operation stages and generate routing and inventory management decisions sequentially. An asynchronous training strategy is developed to address the coupling between stages. Comparison experiments demonstrate that the proposed HCRL method achieves 12% shorter tour lengths on average compared to heuristic algorithms. Additional experiments validate its generalizability to unseen instances and scalability to larger instances.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"339-352"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Q-Learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time","authors":"Ruixue Zhang;Hui Yu;Adam Slowik;Kaizhou Gao","doi":"10.23919/CSMS.2024.0021","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0021","url":null,"abstract":"Since the increasing demand for surgeries in hospitals, the surgery scheduling problems have attracted extensive attention. This study focuses on solving a surgery scheduling problem with setup time. First, a mathematical model is created to minimize the maximum completion time (makespan) of all surgeries and patient waiting time, simultaneously. The time by the fatigue effect is included in the surgery time, which is caused by doctors' long working time. Second, four mate-heuristics are optimized to address the relevant problems. Three novel strategies are designed to improve the quality of the initial solutions. To improve the convergence of the algorithms, seven local search operators are proposed based on the characteristics of the surgery scheduling problems. Third, Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration. Finally, by comparing the experimental results of 30 instances, the Q-learning based local search strategy's effectiveness is verified. Among all the compared algorithms, the improved artificial bee colony (ABC) with Q-learning based local search has the best competitiveness.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"321-338"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advance in Significant Wave Height Prediction: A Comprehensive Survey","authors":"Jinyuan Mo;Xianghan Wang;Shengjun Huang;Rui Wang","doi":"10.23919/CSMS.2024.0019","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0019","url":null,"abstract":"The significant wave height prediction holds critical value for marine energy development, coastal infrastructure planning, and ensuring safety in maritime operations. The precision of such predictions carries substantial theoretical and practical weight. This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction, serving as a methodical guidepost for academicians. The study introduces an all-encompassing predictive framework for significant wave height, which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs. The framework is structured into four principal layers, i.e., feature selection, basic prediction, data decomposition, and parameter optimization. The ensuing sections meticulously dissect the methodologies within these strata, elucidating their core concepts, distinctive features, merits, and constraints, and their applicability to significant wave height prediction. To wrap up, the study delves into fresh research inquiries and avenues pertinent to the discipline, thereby broadening the comprehension of significant wave height prediction. In essence, this scholarly article imparts critical knowledge beneficial to the realm of marine technology.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"402-439"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820943","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Integrated Optimization Method of Batch Planning and Scheduling for Steelmaking-Continuous Casting-Hot Rolling Production Under Uncertain Environment Based on Lagrangian Relaxation Framework","authors":"Liangliang Sun;Xirang Hao;Yunpeng Li;Jinghan Xue;Natalia M. Matsveichuk;Yuri N. Sotskov;Qichun Zhang","doi":"10.23919/CSMS.2024.0018","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0018","url":null,"abstract":"The integrated process of steelmaking, continuous casting, and hot rolling (SM-CC-HR) covers the entire process from refining liquid steel to manufacturing semi-finished steel products. Its batch planning and scheduling are connected to the production contract plan at the upper level and the production process control at the lower level, which is the key to achieving efficient and full process steel manufacturing. Batch planning determines the location of the slabs to be produced in the converter, ladle, continuous casting machine, and hot rolling mill by combining production orders, process standards, and production conditions. Production scheduling, guided by batch planning, combines production performance indicators, process constraints, etc., to determine the equipment selection and start-stop times of specific production units at each process. The synergy of the two aims to optimize production profitability, energy consumption, efficiency, etc., through rational decisions, ensuring the efficiency and flexibility of the steel production process. However, due to the traditional “divide and conquer” management mode and the influence of many uncertain factors, it is difficult to ensure the flexible balance between the demand and capacity, as well as a reasonable matching of logistics and resources among the production processes that operate independently. Considering the uncertain environment and the integration of SM-CC-HR, this paper summarizes the research status of previous scholars from three aspects: mathematical modeling, model optimization, and algorithm optimization based on the Lagrangian framework. It discusses the research status of batch planning and scheduling methods for SM-CC-HR production based on the Lagrangian relaxation framework, analyzes the problems existing in current research, and points out the main research directions and important research contents in the future, in order to promote the research and application of batch planning and scheduling problems for SM-CC-HR under uncertain environments.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"387-401"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Dynamic Q-Learning Algorithm to Solve the Lot-Streaming Flowshop Scheduling Problem with Equal-Size Sublots","authors":"Ping Wang;Renato De Leone;Hongyan Sang","doi":"10.23919/CSMS.2024.0010","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0010","url":null,"abstract":"The lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan. In response, an improved dynamic O-learning (IDQL) algorithm is proposed, utilizing makespan as feedback. To prevent blind search, a dynamic search strategy is introduced. Additionally, the Nawaz-Enscore-Ham (NEH) algorithm is employed to diversify solution sets, enhancing local optimality. Addressing the limitations of the dynamic \u0000<tex>$varepsilon$</tex>\u0000-greedy strategy, the Glover operator complements local search efforts. Simulation experiments, comparing the IDQL algorithm with other intelligent algorithms, validate its effectiveness. The performance of the IDQL algorithm surpasses that of its counterparts, as evidenced by the experimental analysis. Overall, the proposed approach offers a promising solution to the complex ELFSP, showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"223-235"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}