Computers & Industrial Engineering最新文献

{"title":"Consensus reaching model with self-confidence-based dynamic weights and personalized adjustment constraints for multi-attribute group decision making","authors":"Xiaoan Tang ,&nbsp;Meng Sun ,&nbsp;Qiang Zhang ,&nbsp;Witold Pedrycz ,&nbsp;Yinghua Shen","doi":"10.1016/j.cie.2025.111032","DOIUrl":"10.1016/j.cie.2025.111032","url":null,"abstract":"<div><div>It is quite a remarkable fact that experts always hope that the final decision outcome can preserve their original assessments in practical multi-attribute group decision making (MAGDM) activities to a high degree, which can be reflected from their self-confidence in their own assessments. Meanwhile, experts may have different attitude towards the assessment modification at the levels of attribute and alternative in consensus reaching process (CRP), which is commonly characterized by their personalized adjustment constraints on the acceptance of the assessment’ modification suggestions recommended by moderators. Motivated by the facts outlined above, this study proposes a consensus reaching method with self-confidence-based dynamic weights and personalized adjustment constraints for the MAGDM problem. First, a self-confidence-based dynamic weight management method is proposed to accelerate the CRP. Next, considering that experts may have different levels of sensitivities/tolerances for their assessment modifications at the various attribute and alternative levels, a dynamic weight and personalized adjustment constraint-driven consensus model is proposed with the objective of minimizing the distance between experts’ initial assessments and the adjusted collective assessment. Meanwhile, an associated maximum consensus model is implemented to examine whether a predetermined consensus threshold can be achieved with the given assessments. Then, a resolution approach with an interactive CRP that can reconcile the goals that experts should be allocated enough attention and their original assessments should be preserved as much as possible is developed. Finally, three illustrative examples and a comparative study are conducted to show the validity and advantages of the proposal. Overall, this study exhibits three facets of originality: the dynamic weight management method is developed, the personalized assessment-modification willingness is formed, and the interactive consensus reaching algorithm is created.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111032"},"PeriodicalIF":6.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697374","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":"Integrating environmental considerations and resilience in material sourcing for construction projects: A two-stage stochastic programming model","authors":"Zixuan Chen ,&nbsp;Ahmed W.A. Hammad ,&nbsp;Mana Alyami ,&nbsp;Assed N. Haddad","doi":"10.1016/j.cie.2025.111027","DOIUrl":"10.1016/j.cie.2025.111027","url":null,"abstract":"<div><div>This study presents a multi-objective two-stage stochastic model to optimise supplier selections, material ordering, inventory management, and material <em>trans</em>-shipments within a construction project portfolio. The model is designed to address challenges faced by general contractors when managing a project portfolio, particularly in controlling financial costs and minimising the environmental impact associated with material transportation and storage. Material back-up sourcing is integrated into the model as a strategy to mitigate supply chain disruptions. The model focuses on assisting the general contractor’s resilient material management decisions in response to supply disruptions influenced by multiple uncertain factors. To achieve this, disruption probabilities are estimated using a Bayesian network based on primary suppliers’ reliabilities and the macro market conditions. An analysis is undertaken to assess the impact of the Bayesian network-based scenario modelling approach on model results. Additionally, sensitivity analysis reveals that higher penalties for material shortages or storage fees have a minimal effect on total supply chain costs due to the contractor’s capacity to utilise back-up sourcing and <em>trans</em>-shipments. Similarly, while higher costs for back-up suppliers increase overall expenses, the total carbon dioxide equivalent emissions are reduced by approximately 20% due to decreasing deliveries and <em>trans</em>-shipment activities. This study uses a novel Bayesian network-based approach to model CSC disruption scenarios, and further contributes to construction supply chain resilience studies by integrating back-up sourcing decisions and project portfolio material <em>trans</em>-shipments within a two-stage stochastic programming framework.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111027"},"PeriodicalIF":6.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760953","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":"Enhancing strategic investment in construction engineering projects: A novel graph attention network decision-support model","authors":"Fatemeh Mostofi ,&nbsp;Ümit Bahadır ,&nbsp;Onur Behzat Tokdemir ,&nbsp;Vedat Toğan ,&nbsp;Victor Yepes","doi":"10.1016/j.cie.2025.111033","DOIUrl":"10.1016/j.cie.2025.111033","url":null,"abstract":"<div><div>Selecting the right investment projects is a pivotal decision-making process that can steer a company’s financial and operational future. Existing methods often fall short in merging machine learning with network-based multi-criteria decision-making (MCDM) strategies. This research presents a first-time investment network framework fed into a graph attention network (GAT) to forecast the success of construction engineering projects by leveraging their interrelated data across various decision-making parameters. Expert judgment was initially employed to filter over 33,000 investment projects based on organizational goals, project risk, and business development ratings. The refined dataset was organized into three specialized MCDM investment-decision networks: regional-based, country-level, and funding-mode-based. These networks were subsequently fed into GAT models to classify investment values. The regional-based network achieved over 99 % accuracy, the country-level and funding-mode-based networks delivered over 98 % accuracy. These insights demonstrate that while all three models maintain high accuracy, the slight variances in their performance reflect the importance of tailoring decision-support tools to specific geographical contexts. The understanding of different network structures can provide strategic decision-making insight for large-scale infrastructure investments, where even minor misclassifications can lead to substantial financial consequences.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111033"},"PeriodicalIF":6.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631735","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 unsupervised subdomain adaptation of cross-domain remaining useful life prediction for sensor-equipped equipments","authors":"Jianhai Yan ,&nbsp;Zhi-Sheng Ye ,&nbsp;Shuguang He ,&nbsp;Zhen He","doi":"10.1016/j.cie.2025.110967","DOIUrl":"10.1016/j.cie.2025.110967","url":null,"abstract":"<div><div>Most existing remaining useful life (RUL) prediction models assume a similarity between the data distributions in the source and target domains, but this assumption is often challenging in practical applications. Although traditional transfer learning methods could relax this assumption by minimizing the distributional differences between domains, they often ignore the fine-grained features exhibited by equipment at different health state and the inherent properties of the domains. Additionally, most models ignore the importance of the location information of the sensors equipped on the equipment, and the real-world problem of the high cost of fully labeling the data. Addressing these challenges, we introduce a novel model of unsupervised subdomain adaptation and feature disentanglement for equipment RUL prediction. The model contains four components: a feature extractor, a feature similarity component, a domain adaptation component, and a multi-task module. Specifically, the feature extractor designs a dynamic fusion network to divide the domain’s private and shared features. The feature similarity component employs a cross-attention mechanism to constrain the extracted features. The domain adaptation enables the proposed model to extract deeper subdomain-shared features. The multi-task module simultaneously divides subdomains and performs RUL prediction. The proposed model is validated using multiple tasks and compared with existing works.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110967"},"PeriodicalIF":6.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631830","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":"Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model","authors":"Jaewoong Choi ,&nbsp;Janghyeok Yoon ,&nbsp;Changyong Lee","doi":"10.1016/j.cie.2025.111034","DOIUrl":"10.1016/j.cie.2025.111034","url":null,"abstract":"<div><div>Machine learning (ML) approaches for predicting future citations of patents using textual information are valuable for early screening of potential breakthrough technologies. However, their practicality is often limited by the opaque nature of ML models and hierarchical structures of patent documents. This study proposes a patent-specific hierarchical attention network (PatenHAN) designed to enhance interpretability at the technological factor level using patent claim information. Central to this approach are (1) a patent-specific pre-trained language model (PLM) that effectively captures the semantics of patent claims, (2) a hierarchical network architecture that reflects the structure of patent claims representing the technological factors and scope of the invention, and (3) a claim-wise self-attention mechanism that enables the interpretation of individual claims’ influence on predictions by revealing pivotal claims with high attention scores. A case study of 35,376 pharmaceutical patents demonstrates the effectiveness of the proposed approach in early screening for potential breakthrough technologies, achieving enhanced interpretability and notable performance, including an accuracy of 91.7 % and a Matthews correlation coefficient of 0.293. Additional analyses using various PLMs and claim types confirm the robust performance of PatenHAN and provide practical insights into its implementation. The proposed approach is expected to serve as a useful complementary tool for the early screening of potential breakthrough technologies, facilitating expert-machine collaborations.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111034"},"PeriodicalIF":6.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682655","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":"Constraint first, shrinking next: A hybrid photovoltaic generation forecasting framework based on ensemble learning and multi-strategy improved optimizer","authors":"Jionghao Zhu ,&nbsp;Jie Liu ,&nbsp;Xiaoying Tang","doi":"10.1016/j.cie.2025.111022","DOIUrl":"10.1016/j.cie.2025.111022","url":null,"abstract":"<div><div>Accurate photovoltaic (PV) power generation forecasting is critical for effective smart grid management. However, PV power is highly dependent on weather variables, resulting in significant volatility and nonlinearity in generation patterns, which makes traditional forecasting methods often struggle to guarantee both accuracy and robustness at the same time. Although Pareto-front-based ensemble learning approaches partially mitigate these issues, the excessive generation of suboptimal solutions increases decision-making challenges for grid operators. To this end, this paper customizes a novel PV ensemble learning forecasting system based on Bi-Directional Guarantee (BIDG) and Pareto Front Shrinking (PFS). Specifically, the original Hippopotamus Optimizer (HO) is enhanced into a Multi-Objective Hippopotamus Optimizer (MOHO) using a crowding-distance-based multi-objective framework. To further enhance the algorithm’s performance, low-discrepancy sequence initialization is adopted to improve the optimizer’s exploration ability, and a perturbation operator is introduced to enhance convergence performance. The BIDG mechanism based on the improved MOHO is used as the weighting strategy for ensemble learning, which imposes dual constraints on the accuracy and robustness of the forecast and penalizes suboptimal solutions to improve the quality of the Pareto front. To further improve the quality of the Pareto front to simplify decision-making, we combine the Shrinking Method to Quantify Competition (SMQC) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to shrink the Pareto front. Numerical validation is conducted using seasonal local datasets from a PV plant in Australia’s Northern Territory. The experimental results show that the average absolute percentage error of the hybrid framework on the four datasets are <em>MAE_Spring</em> = 0.3518, <em>MAE_Summer</em> = 0.5689, <em>MAE_Autumn</em> = 0.8585, <em>MAE_Winter</em> = 1.2175, which are significantly lower than other models, achieving higher forecasting accuracy and demonstrating its practical applicability for real-world smart grid operations.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111022"},"PeriodicalIF":6.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642036","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 nonparametric degradation modeling method based on generalized stochastic process with B-spline function and Kolmogorov hypothesis test considering distribution uncertainty","authors":"Zhongze He ,&nbsp;Shaoping Wang ,&nbsp;Di Liu","doi":"10.1016/j.cie.2025.111036","DOIUrl":"10.1016/j.cie.2025.111036","url":null,"abstract":"<div><div>The increasing reliability requirements of modern industrial products necessitate precise degradation modeling. Stochastic process-based methods are widely employed due to their robust uncertainty quantification capabilities but often rely on the assumption of a predefined degradation distribution, which may not hold in complex scenarios. This study presents a novel degradation modeling framework that integrates nonparametric estimation and stochastic processes into a unified approach. Unlike conventional methods combined nonparametric estimation and stochastic processes by separately modeling, the proposed method employs a nonparametric model to characterize generalized independent increment processes. Utilizing the flexibility of B-spline functions, the method effectively captures the uncertainties of degradation distributions while mitigating errors associated with improper distribution assumptions. The B-spline construction is further formulated as a numerical optimization problem supported by the Kolmogorov hypothesis test, enabling the direct determination of confidence levels for the constructed model. A Monte Carlo-based framework is employed for reliability assessment and lifetime prediction. Validation using simulations and real-world data demonstrates that the proposed method achieves superior accuracy in capturing degradation dynamics and significantly outperforms traditional methods.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111036"},"PeriodicalIF":6.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620987","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":"TAOD-Net: Automated detection and analysis of thyroid-associated orbitopathy in facial imagery","authors":"YongKyung Oh ,&nbsp;Giheon Koh ,&nbsp;Jiin Kwak ,&nbsp;Kyubo Shin ,&nbsp;Gi-Soo Kim ,&nbsp;Min Joung Lee ,&nbsp;Hokyung Choung ,&nbsp;Namju Kim ,&nbsp;Jae Hoon Moon ,&nbsp;Sungil Kim","doi":"10.1016/j.cie.2025.111024","DOIUrl":"10.1016/j.cie.2025.111024","url":null,"abstract":"<div><div>Thyroid-Associated Orbitopathy (TAO), a common autoimmune thyroid disease, significantly impacts patients’ quality of life. The conventional method for assessing TAO disease activity relies on the Clinical Activity Score (CAS), which is evaluated by skilled experts. However, the high cost of securing expert evaluators and inconsistencies in their assessments highlight the need for an expert-level, data-driven CAS assessment system. In response, we introduce TAOD-Net (Thyroid-Associated Orbitopathy Detection Network), an advanced data-driven system designed to identify five key CAS components related to inflammatory signs. Leveraging patient facial images as input, our system incorporates a novel learning strategy for multi-label classification and utilizes domain knowledge for optimized image cropping. The performance of TAOD-Net was rigorously validated using 2040 digital facial images collected from 1020 TAO patients at the Department of Ophthalmology, Seoul National University Bundang Hospital. Our results demonstrate that TAOD-Net surpasses existing models in diagnosing TAO disease activity, underscoring its potential to exceed current standards.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111024"},"PeriodicalIF":6.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631736","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":"Automated argumentation-based social trust negotiation in collaborative networks","authors":"Javier Andrade-Garda ,&nbsp;Víctor Carneiro-Díaz ,&nbsp;Daniel Lage-Etchart ,&nbsp;Sonia Suárez-Garaboa","doi":"10.1016/j.cie.2025.111026","DOIUrl":"10.1016/j.cie.2025.111026","url":null,"abstract":"<div><div>Collaboration is an inherent property in Collaborative Networks (CNs), that are increasingly becoming the mainstay of manufacturing and production firms. Collaboration success largely depends on trust. Therefore, trust should be managed in an explicit manner when selecting partners to collaborate with in a CN. Traditionally, the partner selection approaches that explicitly consider trust as a selection criterion manage it as a single concept. However, trust is a multidimensional concept, and each dimension has unique influences on business relationships. The focus of the paper is precisely on the social dimension of trust, which has proven to be of special relevance in CNs. The goal is to allow partners to create alliances where this trust dimension is guaranteed, thus maximizing the chances of successful collaboration. To achieve this, an innovative automated argumentation-based negotiation framework is proposed. In this framework, partners will be modelled as agents in a Multi-Agent System, where automated argumentation techniques are widely used, and they will use different types of arguments to negotiate their social trust requirements. A prototype developed in NetLogo was used to test the proposed framework using the Spanish network on environmental DMAs (Red Española de DMAs Ambientales, REDMAAS), a Spanish thematic research network funded by the Spanish Ministry of Science and Innovation (RED2018-102594-T).</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111026"},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Manufacturer’s encroachment strategies when facing the retail platform with AI-driven pricing","authors":"Wenhui Zhou,&nbsp;Yu Ding,&nbsp;Yanhong Gan,&nbsp;Wenting Ma","doi":"10.1016/j.cie.2025.111003","DOIUrl":"10.1016/j.cie.2025.111003","url":null,"abstract":"<div><div>The rapid development of artificial intelligence technology is facilitating the adoption of AI-driven personalized pricing. Retail platforms with a higher level of technology and a closer connection with consumers have taken the lead in adopting AI-driven pricing to improve pricing flexibility. In this paper, we explore the manufacturer’s encroachment strategies in the context of the retail platform’s adoption of AI-driven pricing by constructing a game-theoretic model, as well as clarifying the impact of the magnitude of AI capability on the profits of supply chain participants. We construct three channel structures, namely, a single-channel supply chain model without manufacturer encroachment, a dual-channel supply chain model with manufacturer uniform pricing encroachment, and a dual-channel supply chain model with manufacturer AI-driven pricing encroachment. The results show that manufacturer encroachment with uniform pricing does not lead to an increase in profit. However, the manufacturer can encroach with AI-driven pricing to gain benefits when the AI capability is high, the total cost of adding a direct channel and acquiring AI technology is low, and consumers’ valuation is high. We also find that increased AI capability benefits the manufacturer in any scenario, but not for the retail platform. In the single-channel scenario, enhanced AI capability can improve the retail platform’s profit if consumers’ valuation is relatively low. However, in the dual-channel scenarios where the manufacturer encroaches, enhanced AI capability is completely detrimental to the retail platform. This also brings a management insight to retail platforms that high AI capability may not necessarily boost profitability. Finally, we also conduct a case study to substantiate our claims.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111003"},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620986","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}