Computers & Industrial Engineering最新文献

{"title":"Optimizing renewable energy integration using advanced mathematical modeling with storage and emission constraints for resilient and sustainable energy systems","authors":"Lalji Kumar,&nbsp;Kajal Sharma,&nbsp;Prerna Pathak,&nbsp;Uttam Kumar Khedlekar","doi":"10.1016/j.cie.2025.111379","DOIUrl":"10.1016/j.cie.2025.111379","url":null,"abstract":"<div><div>This study presents a stochastic optimization framework for the integration of renewable energy sources into modern power systems, aiming to address key challenges associated with variability in generation, demand uncertainty, and environmental constraints. The model simultaneously incorporates renewable and non-renewable generation, energy storage dynamics, and probabilistic resilience metrics to ensure system reliability, economic efficiency, and emission compliance. It employs a set of coupled energy balance equations, emission constraints, curtailment control strategies, and stochastic demand–supply interactions modeled via Beta and normal distributions. Applied to a rural Indian energy scenario, the model demonstrates a significant improvement over conventional configurations. Specifically, the optimized case with both storage and emission constraints yields a cost reduction of approximately 26%, a curtailment decrease of over 60%, and improved system resilience and reliability levels approaching 98%. These improvements validate the model’s effectiveness in mitigating renewable intermittency, reducing operational inefficiencies, and supporting decarbonization targets. Socially, the model contributes to enhancing energy access and stability in underserved regions by optimizing resource allocation under real-world uncertainties. Practically, the framework provides a scalable and adaptable tool for policymakers, planners, and utility operators seeking to develop low-carbon, cost-effective, and resilient energy infrastructures. By bridging theoretical modeling with practical system design, the proposed approach offers a valuable decision-support mechanism for transitioning to sustainable energy systems aligned with global climate commitments.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111379"},"PeriodicalIF":6.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671081","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":"Sustainable post-disaster humanitarian logistics network design: A real-case study with consecutive disaster risks","authors":"Mehmet Erdem","doi":"10.1016/j.cie.2025.111358","DOIUrl":"10.1016/j.cie.2025.111358","url":null,"abstract":"<div><div>Humanitarian logistics involves managing assets, skills, and knowledge to provide emergency relief to vulnerable societies against disasters and emergencies. After disasters, it is essential to mobilize logistics resources to assist victims and transport humanitarian supplies. An efficient and sustainable aid response plan can decrease social, economic, and environmental impacts. This paper dwells on sustainable post-disaster humanitarian logistics (SPD-HL) with environmentally friendly and conventional vehicles. The problem aims to establish the routes, schedules, and charges of the heterogeneous fleet, which designs the distribution of multi-relief items to vulnerable people in emergency assembly points and temporary shelter areas spread out in a geographically dispersed area. The objective of the SPD-HL is to minimize the total time that covers total travel time, charging duration, and total earliness and lateness. To solve this new complicated problem variation, we developed a sophisticated metaheuristic that integrates an adaptive large neighborhood search (ALNS) with a problem-specific construction and local search procedures. We conducted extensive experimental analyses on new real-world data instances to examine the performance of the heuristic. According to computational results, the proposed method effectively yields high-quality solutions compared to CPLEX results in small-size instances and improves solution quality in large-size instances. Additionally, we conduct scenario analyses with an energy and social cost-based combined objective function and the probability of road closure cases due to consecutive disasters. The results can aid decision-makers in designing an efficient, sustainable emergency logistics network in the aftermath of disasters in a reasonable time to lessen the loss of human life, costs, and environmental impact.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111358"},"PeriodicalIF":6.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671083","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":"Accessibility and congestion optimization in healthcare networks","authors":"Edgar Duarte-Forero ,&nbsp;Gustavo Alfredo Bula ,&nbsp;Edgar Alfonso-Lizarazo","doi":"10.1016/j.cie.2025.111282","DOIUrl":"10.1016/j.cie.2025.111282","url":null,"abstract":"<div><div>The design of healthcare networks involves decision-making processes related to facility location, capacity allocation, and user assignment to healthcare centers. These decisions must consider that healthcare networks are often both centralized and fragmented, yet they must support the clinical pathways required by users.</div><div>This problem has been widely addressed in the literature within the context of Facility Location Problems and Network Design Problems with Congestion. However, there are few instances where clinical pathways are explicitly incorporated into the modeling. This study proposes an analytical framework for decision-making in healthcare network design that incorporates the structure of clinical pathways with the aim of improving two key aspects: user accessibility and resource congestion.</div><div>The proposed framework, referred to as Multi-Objective Healthcare Network Design, is based on the use of Open Queueing Networks to model user flow. Accessibility is evaluated using the Two-Step Floating Catchment Area metric, while congestion is assessed through resource utilization calculation.</div><div>The optimization problem is solved using the Adaptive Bisection AUGMECON algorithm, incorporating hypervolume and spread indicators to evaluate the quality of the Pareto front. Implementations in artificial networks and in a real healthcare network in a region of Colombia reveal opportunities for redesigning these systems, with a focus on improving patient flow in accordance with the clinical pathways defined in the network.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111282"},"PeriodicalIF":6.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679641","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":"Automatic guided vehicle scheduling based photovoltaic-energy storage-charging station low-carbon optimal sizing via a computational intelligence framework","authors":"Chao Yang ,&nbsp;Hao Xiao ,&nbsp;Xinqiang Chen ,&nbsp;Lijuan Luo ,&nbsp;Haixiao Li ,&nbsp;Salvatore Antonio Biancardo","doi":"10.1016/j.cie.2025.111401","DOIUrl":"10.1016/j.cie.2025.111401","url":null,"abstract":"<div><div>Automated container terminals (ACTs) utilizing Automatic Guided Vehicles (AGVs) require low-carbon charging infrastructure to support the global transition to carbon neutrality. Photovoltaic‑energy storage‑charging stations (PECSs) represent a novel charging infrastructure solution that integrates photovoltaic and energy storage to serve both AGVs and electric vehicles operated by terminal personnel. Against this background, this paper proposes a capacity sizing model for PECS tailored for ACTs, considering the AGV charging load modeling and lifecycle carbon emissions across multiple equipment types and lifecycle stages. Firstly, a multi-cycle AGV scheduling and charging model is developed incorporating internal-external vehicle segregation constraints for typical ACT layouts. Secondly, a lifecycle carbon emission model for PECS is constructed by clearly delineating the model boundary, which encompasses six lifecycle stages and incorporates four types of critical equipment. Thirdly, the carbon emission model is systematically integrated with economic objectives through interface variables, including equipment capacity and purchased electricity power. Therefore, a bi-objective optimal sizing model for PECSs is developed to maximize annual economic benefits while minimizing annual carbon emissions. The model is solved effectively using computational intelligence, specifically the Non-dominated Sorting Dung Beetle Optimizer, and validated through an experiment of a representative ACT. The experimental results demonstrate that the proposed model effectively optimizes PECS’s capacity to balance carbon emissions and economic objectives.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111401"},"PeriodicalIF":6.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679640","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":"Dynamic emergency routing problem for electric motorcycle fleets under uncertain conditions considering battery swapping","authors":"Pouria Tajasob,&nbsp;S. Mohammad J. Mirzapour Al-e-Hashem ,&nbsp;Saba Karimi,&nbsp;Saeed Mansour","doi":"10.1016/j.cie.2025.111406","DOIUrl":"10.1016/j.cie.2025.111406","url":null,"abstract":"<div><div>Most Emergency Medical Service (EMS) systems worldwide face the challenge of reducing response times from the occurrence of an incident to patient arrival. Responding to patient demands is highly time-sensitive, leading EMS providers to use motorized vehicles. Motorized ambulances carry battery-dependent medical devices such as automated external defibrillators (AEDs). In this context, employing electric motorcycles not only ensures rapid transportation but also facilitates continuous charging of onboard medical devices throughout their routes. In addition, EMS demands are unpredictable, and new requests may dynamically occur while vehicles are en route to provide services. Therefore, dynamic response capability becomes critically important. To address these issues, this study proposes a MILP dynamic emergency routing problem model for electric motorcycle fleets considering battery swapping stations. The proposed model integrates routing decisions for both patients and battery swapping stations to effectively manage response time. The model is capable of dynamically receiving new patient demands and updating optimal routes in real-time upon information changes. Furthermore, the model includes soft time windows, and patient service time is treated as uncertain. Small-scale instances are solved using exact methods, while larger-scale problems are addressed through a Variable Neighborhood Search (VNS) algorithm. Numerical experiments demonstrate that the proposed algorithm can obtain solutions with 98% accuracy, operating much faster than exact solutions. The results indicate that the proposed model significantly reduces EMS response time, enhances dynamic response capabilities, and effectively accounts for uncertainty, thereby improving the effectiveness and efficiency of emergency medical operations in realistic conditions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111406"},"PeriodicalIF":6.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696396","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 theory in light of lean manufacturing","authors":"Baruch Mor","doi":"10.1016/j.cie.2025.111308","DOIUrl":"10.1016/j.cie.2025.111308","url":null,"abstract":"<div><div>A central implicit axiom in classic deterministic scheduling theory is that the manufacturing resource, i.e., the machines, yields perfect and zero waste products throughout its life span. In this paper, we aim to challenge this hypothesis, as it is well known that there is always a non-zero percentage of defective products and raw material waste in real-life situations. Thus, we are trying to combine scheduling theory with the current and essential initiatives of Zero Waste Manufacturing (ZWM) and Zero Defect Manufacturing (ZDM), which aim to reduce waste and avoid failures and imperfections during production, respectively. Given this effort, we revisit and analyze several classic single-machine scheduling problems given ZWM. We assume the machine’s performance deteriorates and tends to increase waste or produce faulty products. Therefore, a Calibration and Preventive Maintenance Activity (CPMA) is essential to guarantee optimal performance in the planning horizon. To this end, we postulate that each product is penalized with a job-dependent waste cost. Consequently, we seek to minimize a scheduling measure subject to an upper bound on the permitted total waste cost or, alternatively, an upper bound on the defective products’ fixing (repairing) cost. First, we address the fixed time CPMA interval and then the floating time interval. The researched scheduling measures are the makespan and the total weighted completion time. As these problems are known to be ordinary NP-hard, even without the new constraints, we introduce pseudo-polynomial dynamic programming (DP) algorithms. Furthermore, we demonstrate the procedure of mapping the set of Pareto-optimal solutions, establishing that all the studied problems are ordinary NP-hard.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111308"},"PeriodicalIF":6.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679643","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":"A game theory approach for optimizing job shop scheduling problems with transportation in common shared human–robot environments","authors":"Kader Sanogo ,&nbsp;Abdelkader Mekhalef Benhafssa ,&nbsp;M’hammed Sahnoun","doi":"10.1016/j.cie.2025.111366","DOIUrl":"10.1016/j.cie.2025.111366","url":null,"abstract":"<div><div>The Job Shop Scheduling Problem with Transportation (JSSPT) is a critical challenge in modern industrial systems, particularly in environments where human operators and Autonomous Intelligent Vehicles (AIVs) interact. Traditional scheduling approaches often fail to address the dynamic and unpredictable nature of these shared human–robot environments. In response, this paper introduces a game theory-based scheduling algorithm that optimizes transportation tasks in Industry 5.0 settings, where human–robot collaboration is essential. By modeling AIVs as rational agents within a potential game framework, we reformulate JSSPT as a Multi-robot task allocation problem (MRTA), applying iterative best-response strategies to reach a Nash equilibrium that minimizes the overall makespan. Our approach uniquely integrates human movements into the scheduling process, enabling real-time adaptation to fluctuating production environments. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods, namely the VNS and entropy-based approaches, particularly in settings where human unpredictability significantly impacts performance. On average, the game-theory-based algorithm reduces the makespan by 7 s compared to the entropy-based algorithm and by 17 s compared to the VNS algorithm. Despite the restrictive assumptions regarding human movement, this study underscores the significance of dynamic scheduling approaches in highly variable settings, contributing to more resilient and efficient production systems in line with Industry 5.0’s vision.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111366"},"PeriodicalIF":6.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653061","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":"AI-based hybrid knowledge extraction method in complex engineering scenarios: A case study of drill-and-blast tunnelling excavation","authors":"Zinan Wang ,&nbsp;Baoguo Liu ,&nbsp;Xiaomeng Shi ,&nbsp;Zhiyun Deng ,&nbsp;Jinglai Sun","doi":"10.1016/j.cie.2025.111375","DOIUrl":"10.1016/j.cie.2025.111375","url":null,"abstract":"<div><div>The explosive rise of generative AI models is reshaping both the technological trajectory and developmental landscape of industrial intelligence. However, these large language models demonstrate significant limitations when processing specialized engineering knowledge due to intricate knowledge systems and domain expertise fragmented across various unstructured sources. An AI-based hybrid knowledge extraction method (AHKEM) is proposed to address these challenges in complex engineering scenarios. The method integrates AI techniques and large language models into a systematic framework: TF–IDF analysis is combined with word vector semantics for entity identification across extensive textual corpora, Bert-BiLSTM-CRF is employed for entity recognition, and a novel two-stage hierarchical clustering-GPT relationship mining method (HC-GPT RMM) is utilized for relationship extraction. The approach was demonstrated through a case study of drill-and-blast tunnelling excavation, a typical engineering scenario with complex data characteristics, using a corpus that comprised 13 specifications and standards, 4 professional books, and 343 academic papers, resulting in a knowledge graph containing 1,607 entities and 1,582 relationships that effectively supports various intelligent applications in construction practice. The advantages of AHKEM in handling complex domain knowledge are further validated through comparative experiments with joint extraction approaches. Both a practical framework for knowledge extraction in engineering domains is provided by this study and its application value is demonstrated through a specific construction scenario.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111375"},"PeriodicalIF":6.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671080","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 carbon emission allocation for liner shipping companies","authors":"Yidan Shangguan ,&nbsp;Xuecheng Tian ,&nbsp;Yong Jin ,&nbsp;Shuaian Wang","doi":"10.1016/j.cie.2025.111348","DOIUrl":"10.1016/j.cie.2025.111348","url":null,"abstract":"<div><div>Maritime shipping accounts for approximately 3% of all annual carbon dioxide (<span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span>) emissions worldwide. To address this, the EU Emissions Trading System (ETS) mandates that shipping companies pay for every ton of <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> they emit. In response to this regulation, liner shipping companies have passed the cost of carbon emissions onto their customers. These companies typically levy an emissions surcharge per twenty-foot equivalent unit (TEU) container and calculate the surcharge based on individual trades, which typically encompass multiple origin–destination (od) pairs within a region. However, this surcharge structure fails to account for variations in distance between od pairs within the same trade. To address this limitation, we propose a carbon emissions allocation policy using an optimization method to allocate emissions per TEU for each od pair. This method incorporates both the shortest sailing distance and the actual traveling distance of the od pair, combining these into a harmonic distance. The harmonic factor represents the chosen carbon emissions allocation policy. Considering that carbon emission allocation should be tailored to specific shipping routes, this study constructs an augmented shipping network of multiple routes. Based on this network, we develop two od-link-based models: one with transshipment and the other without transshipment. Both models aim to maximize the profits of liner shipping companies. The models are validated through experiments conducted using real-world and synthetic shipping networks. The results show that the model with transshipment yields higher profits than the model without. In the model without transshipment, the carbon emission allocation policy has little impact on the profits of shipping companies. However, in the model with transshipment, selecting an appropriate carbon emission allocation policy is critical.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111348"},"PeriodicalIF":6.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653057","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":"A neural network adaptation on neutrosophic triplets for robotic assembly line optimization in smart manufacturing","authors":"Amirhossein Nafei ,&nbsp;Zhi Li ,&nbsp;S. Pourmohammad Azizi","doi":"10.1016/j.cie.2025.111398","DOIUrl":"10.1016/j.cie.2025.111398","url":null,"abstract":"<div><div>The decision-making process in smart manufacturing often involves complex, multi-criteria scenarios characterized by uncertainty and conflicting objectives. Traditional decision-making approaches face inherent limitations in managing indeterminacy, ensuring robustness, and addressing computational complexity, which compromise their reliability in dynamic manufacturing environments. This study introduces an innovative framework that integrates the VIKOR method, neural networks, and Neutrosophic Triplets (NTs) to address these challenges. The proposed approach is specifically designed to optimize robotic assembly line configurations by balancing key objectives such as cost, operational efficiency, and sustainability. VIKOR’s compromise solution methodology is leveraged to evaluate trade-offs between group utility and individual regret, while Neutrosophic Triplets enhance the management of indeterminate information. Neural networks provide scalability and adaptability, enabling dynamic ranking refinement and reducing computational overhead. Additionally, a ranking strategy based on occurrence pattern analysis ensures robust and reliable decision-making outcomes. Validated through a case study on robotic assembly line optimization in a smart manufacturing environment, the framework demonstrates its effectiveness in improving productivity, adaptability, and sustainability. These results position the smart VIKOR method as a powerful and scalable solution for addressing the complexities of modern manufacturing systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111398"},"PeriodicalIF":6.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654251","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}