Computers & Industrial Engineering最新文献

{"title":"Robust design of recycling networks considering uncertain regulatory, economic, and technological conditions — The case of recovering polystyrene from building insulation","authors":"Julia Schleier,&nbsp;Grit Walther","doi":"10.1016/j.cie.2025.111254","DOIUrl":"10.1016/j.cie.2025.111254","url":null,"abstract":"<div><div>With increasing building renovations and demolitions, a growing accumulation of end-of-life external thermal insulation composite systems (ETICS) with expanded polystyrene (EPS) is expected in Germany. To enhance material circularity, novel recycling technologies such as pyrolysis and solvent-based recycling offer sustainable alternatives to waste incineration. However, their successful implementation depends on an efficient recycling infrastructure, designed with consideration for uncertainties in future EPS-ETICS waste volumes.</div><div>This study applies a scenario-based robust optimization model to investigate and support decision-making in EPS-ETICS waste management by minimizing economic risks and ensuring robust network design. The model integrates strategic decisions on technology selection, facility location, and processing capacity as well as tactical decisions regarding processing and material flows while accounting for uncertainty in waste volumes driven by regulatory, economic, and technological factors. Applying the model to a German case study, we demonstrate that robust network designs mitigate risks, ensuring cost-effective EPS recovery. Our findings provide insights for policymakers and investors, emphasizing the need for adaptable recycling infrastructure to support EU circular economy goals.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111254"},"PeriodicalIF":6.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Airport gate reassignment with transfer connections","authors":"Chuhang Yu,&nbsp;Meng Cheng","doi":"10.1016/j.cie.2025.111317","DOIUrl":"10.1016/j.cie.2025.111317","url":null,"abstract":"<div><div>Due to the rapid growth in air traffic demand and lags in infrastructure expansion, most of the hub airports are running close to capacity. As one of the most important and highly utilized infrastructures in airports, gate resources are generally insufficient. Restricted by this resource limitation, flight gate schedules are generally tight with little buffer time. A major consequence of tight flight schedules is that gate conflicts between arrivals and departures may frequently occur when faced with flight schedule disturbances during daily airport operations. When disruptions are encountered, flights generally need to be re-scheduled and, therefore, have to be reassigned to other available gates. The traditional gate reassignment process generally neglects the connection feasibility of passengers and crews. As a result, the transfer connections of both passengers and crews are very likely to be disrupted if such a reassignment plan is carried out. This disruption will result in serious inconvenience as well as huge costs to the airport, and therefore is of importance during the gate reassignment planning. In this paper, we first develop a gate reassignment model that explicitly considers transfer connection feasibility for both passengers and crews. Then, an iterative heuristic method is proposed to solve this model efficiently in order to meet real-time application requirements. This method was tested on realistic data from an international hub airport, and the computational results show that the proposed method yields a significant improvement (75.1% on average) over the benchmark approaches.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111317"},"PeriodicalIF":6.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle routing and scheduling problem with two deployment strategies to handle urgent orders in a vaccine supply chain","authors":"Yong Jae Kim,&nbsp;Hyun Ji Kim,&nbsp;Byung Soo Kim","doi":"10.1016/j.cie.2025.111346","DOIUrl":"10.1016/j.cie.2025.111346","url":null,"abstract":"<div><div>In this paper, we study a vehicle routing and scheduling problem considering two deployment strategies for handling urgent orders in a vaccine supply chain. In the addressed problem, customers regularly or urgently order various vaccine products delivered by homogeneous vehicles. During the delivery, we consider storage temperature, due date, and shelf life, which are characteristics of vaccine products. Furthermore, we propose two deployment strategies to handle urgent orders. We formulate a mixed integer linear programming model to minimize the total cost for the addressed problem. In the model, we must simultaneously determine the acceptance of urgent orders, the deployment strategies for the accepted urgent orders, and the routing and scheduling of each vehicle. We present a genetic algorithm and particle swarm optimization to efficiently and effectively solve large-size instances. To evaluate the performance of the proposed algorithms, we conduct numerical experiments for large-size instances. The genetic algorithm shows a smaller average relative percentage deviation value than that of the particle swarm optimization in a reasonable CPU time. Additionally, we present managerial insights for two proposed deployment strategies by conducting a sensitivity analysis.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111346"},"PeriodicalIF":6.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scheduling twin automated stacking cranes for job assignment at automated container terminals","authors":"Yu-Qi Yin ,&nbsp;Meisu Zhong ,&nbsp;Ying-En Ge","doi":"10.1016/j.cie.2025.111344","DOIUrl":"10.1016/j.cie.2025.111344","url":null,"abstract":"<div><div>This paper investigates a problem of scheduling twin automated stacking cranes (ASCs) for job assignment at an automated container terminal with a new yard layout, in which the handover areas at two-end of a yard block have the same function for transferring containers between ASCs and automated lifting vehicles. This problem is to assign container jobs to twin ASCs and sequence these assigned jobs, and it is formulated as a mixed-integer linear programming model to minimize the sum of the makespan and the total job tardiness (or delay time). For solving the medium- and large- sized cases, a new hybrid algorithm named <em>Dividing, Resequencing</em> and <em>Exchanging</em> (DRE) is designed and consists of the dividing procedure, the model for the extended assignment problem and the improved adaptive large neighborhood search algorithm. Numerical experiments demonstrate the efficiency and effectiveness of the proposed model and algorithm, and show the great influence of the ASC speed on the problem objective.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111344"},"PeriodicalIF":6.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and implementation of a self-driving car using deep reinforcement learning: A comprehensive study","authors":"Rachid Djerbi,&nbsp;Anis Rouane,&nbsp;Zineb Taleb,&nbsp;Safia Saradouni","doi":"10.1016/j.cie.2025.111319","DOIUrl":"10.1016/j.cie.2025.111319","url":null,"abstract":"<div><div>This paper presents a groundbreaking and comprehensive study on the design, implementation, and evaluation of a self-driving car utilizing deep reinforcement learning, showcasing significant advancements in autonomous vehicle technology. Our robust framework integrates three innovative AI models for essential functionalities: road detection, traffic sign recognition, and obstacle avoidance. The system architecture, structured around a three layers “DDD” (Data, Detection, Decision) approach, involves meticulous data preprocessing for traffic signs and road data, followed by specialized Deep Learning models for each detection task, including a CNN for traffic signs, a CNN for road detection, and the pre-trained MobileNet-SSD for obstacle detection. A reinforcement learning agent in the Decision Layer processes these outputs for real-time control (steering, acceleration, braking) through a continuous learning process with environmental feedback. The research encompasses both extensive simulation in Unity, leveraging the ML-Agents toolkit for agent training across diverse environments, and crucial real-world deployment. Our reward/punishment system in the simulation environment, based on collisions with road markers and obstacles, refined the agent’s decision-making. The trained AI models were successfully exported and deployed onto a physical prototype, controlled by a Raspberry Pi and equipped with a camera and ultrasonic sensors. Real-world testing affirmed the robust performance of the physical model in detecting roads, recognizing traffic signs, and effectively avoiding obstacles. Quantitative results demonstrate compelling performance, including over 90% accuracy in obstacle detection and a 15% improvement in navigation efficiency compared to traditional algorithms under controlled simulation conditions. Model evaluation metrics show a 98% accuracy, 12% loss, and a prediction rate exceeding 77%. This study not only contributes a comprehensive framework for autonomous vehicle development but also highlights the transformative potential of deep reinforcement learning for creating intelligent and adaptable autonomous systems in both virtual and real-world scenarios, paving the way for safer and more efficient transportation technologies.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111319"},"PeriodicalIF":6.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transient and steady-state analysis of multi-product serial lines with geometric machines","authors":"Xiaohan Wang ,&nbsp;Yaping Dai ,&nbsp;Bin Xin ,&nbsp;Zhiyang Jia ,&nbsp;Jiewu Leng","doi":"10.1016/j.cie.2025.111320","DOIUrl":"10.1016/j.cie.2025.111320","url":null,"abstract":"<div><div>The multi-product production line has become an important component of modern manufacturing. This study investigates a multi-product serial line with limited buffer capacity and machines obeying the geometric reliability model, analyzing both transient and steady-state production performance. We first provide an analytical analysis method for one- and two-machine lines. Then, for multi-machine lines, a computationally efficient approximation method is proposed based on an equivalent parameter calculation procedure that satisfies the production equivalence conditions. Numerical experiments validate the high accuracy of the proposed approximation method. Furthermore, the properties of the multi-product production line are investigated. Finally, a case study is presented to demonstrate the applicability of the proposed model and the analyzing approach.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111320"},"PeriodicalIF":6.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable accident prediction at highway-rail grade crossings: a deep learning approach","authors":"Xiang Yin,&nbsp;Jiangang Jin,&nbsp;Zhipeng Zhang","doi":"10.1016/j.cie.2025.111337","DOIUrl":"10.1016/j.cie.2025.111337","url":null,"abstract":"<div><div>Accidents at highway-rail grade crossings (HRGCs) pose significant risks to life and property, leading to substantial losses each year in the United States. Accurate and interpretable accident prediction provides a viable solution for improving the safety of HRGCs. Although encouraging processes have been achieved, existing studies either exhibit insufficient predictive performance or lack inherent interpretability, hindering efforts to further enhance the safety of HRGCs. To fill this gap, a well-designed deep learning model for accurate and interpretable accident prediction at HRGCs is proposed in this study. First, a word embedding approach is employed to generate vector representations of the category characteristics of HRGCs, effectively capturing the semantic information inherent in these characteristics. Second, the attention mechanism is used to separately aggregate the category characteristics and numerical characteristics, which can dynamically identify the key contributing characteristics of the accidents at HRGCs. The HRGCs data from Louisiana, Texas, and Washington were employed for a comparative analysis with the baseline model, demonstrating and validating the superiority and practicality of the proposed deep learning model. Finally, an interpretive analysis of the prediction process and prediction results of the proposed deep learning model is conducted. Eventually, this study explores the causative factors of accidents at HRGCs in a data-driven manner, providing valuable insights for further improving the safety performance of HRGCs.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111337"},"PeriodicalIF":6.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient method for solving large-scale open shop scheduling problem based on Horovod-GPU and improved graph attention network","authors":"Lanjun Wan ,&nbsp;Haoxin Zhao ,&nbsp;Xueyan Cui ,&nbsp;Long Fu ,&nbsp;Wei Ni ,&nbsp;Changyun Li","doi":"10.1016/j.cie.2025.111306","DOIUrl":"10.1016/j.cie.2025.111306","url":null,"abstract":"<div><div>The open shop scheduling problem (OSSP) involves complex processing constraints and a large number of job-machine combinations, which leads to an exponential increase in the solution space. For large-scale OSSP in real-world industrial productions, traditional methods struggle to provide satisfactory optimization results within a limited time. Therefore, an efficient method for solving large-scale OSSP through improved graph attention network based on link prediction (IGAT-LP) and Horovod-GPU is proposed. Firstly, an open shop scheduling (OSS) model based on IGAT-LP is designed to make full use of the feature information of operation nodes in OSSP. The model employs the graph attention network (GAT) structure to capture dependencies between tasks, learns global information through a multi-head attention mechanism, and predicts the optimal matching order between operations and machines. Secondly, a distributed parallelization method for the OSS model based on IGAT-LP is proposed. The distributed training capability of Horovod-GPU platform is fully utilized to expand the model training across multiple GPU nodes, significantly improving training efficiency. Finally, extensive experiments are conducted to analyze the effectiveness of the proposed method. The experimental results verify the superiority of the proposed method for solving large-scale OSSP instances. Moreover, the method significantly enhances the training performance of the OSS model based on IGAT-LP.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111306"},"PeriodicalIF":6.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of autonomous driving technology in ride-hailing service platform: based on multi-party evolutionary game analysis","authors":"Deru Xie ,&nbsp;Huiqin Zhang ,&nbsp;Yuxiang Zhang ,&nbsp;Jiaman Yu","doi":"10.1016/j.cie.2025.111339","DOIUrl":"10.1016/j.cie.2025.111339","url":null,"abstract":"<div><div>The rapid development of autonomous driving technology is reshaping the business landscape of ride-hailing service platforms, and there is a gap in dynamic research on the multi-party collaborative promotion of autonomous driving technology. Therefore, the purpose of this study is to explore the strategic interactions among the government, autonomous driving technology providers, and ride-hailing service platforms in the promotion and application of autonomous driving technology; reveal the strategic dependencies of the three parties by constructing a multi-party dynamic evolution game model; and draw the following conclusions from the sensitivity analysis of the relevant parameters through numerical simulation: it is found that the initial willingness of the technology provider and the service provider is the determinant of the stability of the autonomous driving car technology promotion strategy; the market mechanism is the main driving force of technology promotion, and the government subsidy plays an auxiliary incentive role; the socio-economic benefits and the technology commission ratio are the key elements to achieve the stability strategy, and the optimization of the commission ratio can reduce the dependence of the two parties on the government subsidy; the government subsidy and the penalty mechanism can effectively accelerate the process of technology promotion, in which the government’s initial willingness to subsidize and the subsidy ratio have a significant positive effect on the willingness of ride-hailing service platforms to adopt the technology. The results of this study provide decision-making references for policymakers and stakeholders to adapt to the market changes and transformation brought by technology impacts and strategic suggestions for enterprises to grasp the window period of technology promotion.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111339"},"PeriodicalIF":6.7,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing integrated train rescheduling strategies for diverse disruption scenarios using reinforcement learning","authors":"Haodong Yin ,&nbsp;Lina Liu ,&nbsp;Ximing Chang ,&nbsp;Hao Fu ,&nbsp;Jianjun Wu","doi":"10.1016/j.cie.2025.111329","DOIUrl":"10.1016/j.cie.2025.111329","url":null,"abstract":"<div><div>Urban rail transit systems are crucial for efficient urban transportation, but unexpected disruptions pose significant challenges to maintaining optimal train schedules. Previous studies on train timetable rescheduling (TTR) have been limited by single disruption scenario and lack of comprehensive strategies, often relying on single or dual rescheduling tactics. This paper develops an innovative approach using reinforcement learning to optimize TTR under diverse disruption scenarios. Unlike previous studies, our approach incorporates a broader range of rescheduling strategies, including normal operations, train holding, short turning, reverse running, delayed departure from depots, and their combined strategies, enhancing the model’s flexibility and adaptability. To tackle the complexity of the proposed model, we further propose a novel reward mechanism with primary and secondary reward functions in reinforcement learning. The model’s effectiveness and adaptability are validated in various disruption scenarios using real-world cases on Beijing Metro Line 19. Experimental results demonstrate that, in a 30-minute disruption scenario, the integrated rescheduling strategy reduces total train delays by 46.6 % and computation time by 13.7 % compared to a single rescheduling strategy.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111329"},"PeriodicalIF":6.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}