Computers & Industrial Engineering最新文献

{"title":"Last-mile delivery optimization: Leveraging electric vehicles and parcel lockers for prime customer service","authors":"Niloufar Mirzavand Boroujeni ,&nbsp;Nima Moradi ,&nbsp;Saeed Jamalzadeh ,&nbsp;Nasim Mirzavand Boroujeni","doi":"10.1016/j.cie.2025.110991","DOIUrl":"10.1016/j.cie.2025.110991","url":null,"abstract":"<div><div>In last-mile delivery, offering home delivery services is one of the most costly and time-consuming approaches for a delivery company. Parcel lockers (PLs) are an alternative to mitigate operational costs, enabling self-service parcel collection at post offices, transportation hubs, and campuses. Additionally, companies like Amazon often prioritize specific customers, such as ‘Amazon Prime Members,’ by offering them same-delivery options. This study explores last-mile delivery with home-attended service via electric vehicles (EVs) and self-collection at PLs while serving high-priority prime customers via EV or PL. The problem is termed the selective electric vehicle routing problem with PLs (SEVRP-PL), finding EV routes for prime customers and delivering parcels to designated PLs for customer pickup while minimizing EVs’ route, usage, and PLs’ opening costs and maximizing the collected prizes. A novel mixed-integer linear programming model is developed. Also, an efficient problem-tailored large neighborhood search heuristic with simulated annealing criterion (solution acceptance/rejection with a probability) is proposed to tackle large instances. The sensitivity analysis and performance comparison of the methods are presented. Significant reductions in route and usage costs for EVs are achieved through the proposed multi-modal delivery while prioritizing prime customers; savings of 24% in EV routing cost and 22.50% in EV usage cost on average for various instances. These results indicate that the addressed multi-modal delivery system effectively utilizes the resources, e.g., EVs and PLs, mainly when fast service for prime customers is desired.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110991"},"PeriodicalIF":6.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529738","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":"How to reduce range anxiety among electric vehicle consumers through government subsidies?","authors":"Zongxian Wang,&nbsp;Xinjiang Chen,&nbsp;Guannan He","doi":"10.1016/j.cie.2025.110986","DOIUrl":"10.1016/j.cie.2025.110986","url":null,"abstract":"<div><div>With the growing global focus on electric vehicles, governments worldwide have implemented various subsidy policies to promote their adoption and usage. These policies significantly influence competitive behaviors in the automotive market. This study develops three game-theoretic models to examine the competitive behaviors of internal combustion engine vehicle (ICEV) manufacturers and electric vehicle (EV) manufacturers under three scenarios: no subsidies, subsidies for consumers, and subsidies for manufacturers. Through both theoretical and numerical equilibrium analyses, the study investigates optimal pricing strategies and profitability across different scenarios. The results indicate that adjusting the per-mile cost difference between ICEVs and EVs plays a pivotal role in shaping their pricing strategies. Furthermore, governments can influence the dynamics of the automotive market by strategically adjusting fuel and electricity prices. Notably, when the ratio of consumer sensitivity to subsidy intensity and the subsidy coefficient for EV manufacturers is low, subsidies targeting manufacturers prove more effective in promoting EV adoption than those directed toward consumers. This study provides critical insights and actionable policy recommendations for designing effective subsidy programs to accelerate the transition to electric vehicles. It provides valuable guidance for policymakers and stakeholders in the automotive industry, contributing to the sustainable development and competitiveness of the sector.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110986"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562864","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":"Towards sustainable maritime distribution: Developing an optimal fleet distribution model","authors":"Yu-Chung Tsao ,&nbsp;I Gede Arei Banyupramesta","doi":"10.1016/j.cie.2025.110970","DOIUrl":"10.1016/j.cie.2025.110970","url":null,"abstract":"<div><div>Maritime transportation is a key component of global trade, but its contribution to greenhouse gas (GHG) emissions necessitates more sustainable fleet distribution strategies. To support compliance with International Maritime Organization (IMO) MARPOL Annex VI regulations, this study develops an optimal fleet distribution model aimed at minimizing the Carbon Intensity Indicator (CII) while ensuring operational feasibility. The model is formulated using Mixed-Integer Linear Programming (MILP) and evaluated through Variable Neighborhood Search (VNS) across multiple operational scenarios. A case study on LNG distribution in the Bali-Nusa Tenggara region is conducted to assess the model’s effectiveness. The results show that Scheme 1 and Scheme 4 consistently achieve the lowest CII values, making them the most suitable configurations under IMO compliance criteria. The comparison between VNS and MILP demonstrates that VNS can efficiently approximate MILP results while maintaining computational efficiency in optimizing fleet assignments. The scenario analysis further highlights that capacity constraints play a critical role in determining CII values, emphasizing the importance of selecting the appropriate fleet size and allocation strategy. The findings suggest that optimizing fleet selection and route distribution can significantly reduce carbon emissions while maintaining cost-effective operations. This study provides a structured and data-driven approach to sustainable maritime distribution, offering practical insights for industry stakeholders to achieve both environmental compliance and operational efficiency.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110970"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509546","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":"Learning and knowledge-guided evolutionary algorithm for the large-scale buffer allocation problem in production lines","authors":"Sixiao Gao ,&nbsp;Fan Zhang ,&nbsp;Shuo Shi","doi":"10.1016/j.cie.2025.111002","DOIUrl":"10.1016/j.cie.2025.111002","url":null,"abstract":"<div><div>The large-scale buffer allocation problem (LBAP) in production lines represents a significant optimization challenge, centered on the efficient allocation of limited temporary storage areas. Prior research has predominantly addressed the LBAP through dynamic programming, search algorithms, and metaheuristics. However, these methodologies are often problem-specific and inefficient when applied to large-scale scenarios. Consequently, there is a pressing need to investigate innovative algorithms beyond existing approaches. This paper presents a novel learning and knowledge-guided evolutionary algorithm designed for the LBAP in production lines. The proposed algorithm develops an adaptive genetic algorithm and a variable neighborhood search algorithm, incorporating a simulated annealing-based strategy. An online Q-learning algorithm is employed to dynamically select the more effective of the two preceding algorithms for solution updates, while the simulated annealing-based strategy regulates the acceptance of these updated solutions. Furthermore, The proposed algorithm dynamically adjusts crossover, mutation, and shaking rates to adapt to the neighborhood structure. It also leverages conflict knowledge obtained from prior update experiences to inform the search process, thereby enhancing solution quality and computational efficiency. Numerical results indicate that the proposed algorithm surpasses state-of-the-art methods in addressing the LBAP. Additionally, empirical ablation studies demonstrate that the knowledge-guided approach efficiently explores promising solution regions by eliminating low-value solutions, while the learning-guided approach effectively generates improved solutions by selecting optimal strategies. This proposed algorithm significantly advances dynamic production resource allocation in large-scale systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111002"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552265","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":"Multi-objective Human-robot collaborative batch scheduling in distributed hybrid flowshop via automatic design of local search-reconstruction-feedback algorithm","authors":"Peng He ,&nbsp;Xuchu Jiang ,&nbsp;Qi Wang ,&nbsp;Biao Zhang","doi":"10.1016/j.cie.2025.110983","DOIUrl":"10.1016/j.cie.2025.110983","url":null,"abstract":"<div><div>The emergence of distributed production models has spurred extensive research on distributed hybrid flowshop scheduling. Despite advancements in resource allocation for flowshops, most studies overlook human-robot collaboration, which remains essential for complex manufacturing processes in real-world production. Additionally, the rise of multi-variety, small-batch production has driven widespread adoption of batch scheduling. Therefore, this paper introduces a multi-objective distributed hybrid flowshop batch scheduling problem with human-robot collaboration (DHFBSP_HC), aiming to minimize makespan and total energy consumption simultaneously. To address this issue, we propose a local search-reconstruction-feedback (LSRF) algorithm, which consists of four core components: population initialization, local search, reconstruction, and feedback mechanism. Additionally, the algorithm incorporates three configurable strategies, including fitness evaluation approaches, initialization approaches, and objective normalization approaches. These configurable strategies are regarded as categorical parameters, whereas the other parameters are referred to as numerical parameters. To select categorical and numerical parameters that can optimize the results of multi-objective DHFBSP_HC, we introduce the automated algorithm design and use I/F-Race to optimize parameter settings. Through comparisons with several state-of-the-art algorithms, we demonstrate the effectiveness and superiority of the LSRF algorithm in solving the multi-objective DHFBSP_HC.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110983"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552857","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":"Co-Evolutionary NSGA-III with deep reinforcement learning for multi-objective distributed flexible job shop scheduling","authors":"Yingjie Hou ,&nbsp;Xiaojuan Liao ,&nbsp;Guangzhu Chen ,&nbsp;Yi Chen","doi":"10.1016/j.cie.2025.110990","DOIUrl":"10.1016/j.cie.2025.110990","url":null,"abstract":"<div><div>The multi-objective distributed flexible job shop scheduling problem (MO-DFJSP) is important to balance manufacturing efficiency and environmental impacts. This work aims to address the MO-DFJSP, simultaneously minimizing makespan, total tardiness, and carbon emission. Previous research has highlighted the effectiveness of integrating Reinforcement Learning (RL) methods with evolutionary algorithms (EAs). However, existing works often execute EAs and RL independently, with RL influencing only specific parameters. This constrains the algorithms’ overall optimization capabilities. To fully exploit the advantages of RL, this paper presents a co-evolutionary non-dominated sorting genetic algorithm-III (NSGA-III) integrated with deep reinforcement learning (CEGA-DRL). In CEGA-DRL, we incorporate an innovative gene operator into the NSGA framework, enabling the RL agent to directly derive excellent gene combinations from a chromosome and feed them back into NSGA-III. This accelerates the learning process of NSGA-III. In addition, we present a dual experience-pool elite backtracking strategy (DEEBS) to offer NSGA-III’s high-quality solution as experiences for the RL agent. This, in turn, improves the learning efficiency of the RL agent. The performance of CEGA-DRL is evaluated on the self-constructed MO-DFJSP benchmarks with various transit time, energy consumption, and workshop configurations. Experimental results demonstrate that, in comparison to the state-of-the-art intelligent optimization algorithms, CEGA-DRL achieves superior results across all the scheduling objectives and exhibits significant advantages in solution convergence and distribution.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110990"},"PeriodicalIF":6.7,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509521","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 intelligent data-driven adaptive health state assessment approach for rolling bearings under single and multiple working conditions","authors":"Wenqin Zhao ,&nbsp;Yaqiong Lv ,&nbsp;Ka Man Lee ,&nbsp;Weidong Li","doi":"10.1016/j.cie.2025.110988","DOIUrl":"10.1016/j.cie.2025.110988","url":null,"abstract":"<div><div>The health state assessment of critical rotating components (e.g., rolling bearings) is vital to supporting lifecycle health management in complex systems. Traditional approaches that are pivotal for such assessments usually require prior knowledge and manual intervention. Additionally, the challenge amplifies when considering assessments across varying operating conditions. Nowadays, intelligent data-driven approaches have been becoming increasingly prevalent. This paper presents a novel data-driven approach to realize adaptive health assessment in complex and multiple operational conditions. The approach consists of several functions. First, automatic health state segmentation employed deep feature extraction by variational auto-encoder (VAE), feature filtering, and unsupervised clustering. Furthermore, to enable adaptive feature extraction under multiple operating conditions, an improved classification variational auto-encoder with domain confusion (CVAEDC) model was designed to adapt cross-domain feature representation. Finally, a long short-term memory neural network with reinforcement learning to optimize the network hyperparameters (RL-LSTM) was developed for health assessment under single and multiple operating conditions. The proposed approach was validated using the 2012 IEEE Challenge dataset and the XJTU-SY dataset. In the former dataset, the assessment accuracies in the given operational condition and target operating condition reached over 92.4% and 85.9%. In the latter dataset, the accuracies reached 95.8% and 87.6%. Experimental results on the two datasets demonstrated the effectiveness and robustness of the approach, notably reflected in the root mean square (RMS) curve comparisons. In summary, the approach provides an effective solution to support practical health assessments for critical rotating components in varying operational environments.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110988"},"PeriodicalIF":6.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512388","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 unequal area facility layout design under stochastic material flow, re-arrangement cost, and change period","authors":"Mohammad Firouz ,&nbsp;Afshin Oroojlooy-Jadid ,&nbsp;Ardavan Asef-Vaziri","doi":"10.1016/j.cie.2025.110971","DOIUrl":"10.1016/j.cie.2025.110971","url":null,"abstract":"<div><div>In this paper, we present a novel approach to stochastic dynamic unequal area facility layout design while considering uncertainty in material flow, rearrangement costs, and change periods. Our method accounts for dynamic changes by adding new workcenters at stochastic time periods, material flows, and rearrangement costs. By leveraging slicing tree representations, a hybrid genetic algorithm, and a simulation-based evaluation framework, our approach effectively balances material handling efficiency and rearrangement costs, ensuring resilient layouts under stochastic scenarios. Our approach explicitly incorporates probabilistic modeling of material flows, dynamic cost functions, and stochastic event-driven layout adjustments in a unified framework. Computational experiments validate the effectiveness of our method, demonstrating significant improvements in cost optimization and adaptability. Our study reveals that deterministic solutions may not remain optimal under stochastic conditions, emphasizing the need for a range of material flow scenarios in the deterministic model. The results highlight the practical implications of our approach, enabling industries to reduce disruptions and operational costs while maintaining efficiency.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110971"},"PeriodicalIF":6.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474815","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 opinion evolution-based consensus-reaching model for large-scale group decision-making: Incorporating implicit trust and individual influence","authors":"Pei Wang ,&nbsp;Jing Zhang ,&nbsp;Youwu Lin ,&nbsp;Shuai Huang ,&nbsp;Xuanhua Xu","doi":"10.1016/j.cie.2025.110974","DOIUrl":"10.1016/j.cie.2025.110974","url":null,"abstract":"<div><div>Large-scale group decision-making (LGDM) often involves complex challenges, such as effectively clustering decision-makers, modeling asymmetric trust relationships, and balancing the influence of leaders and members to achieve consensus. This study proposes a novel opinion evolution-based consensus-reaching model to address these issues. A convex clustering method is developed, combining the strengths of K-means and hierarchical clustering to enable adaptive subgroup formation and automatic determination of the optimal number of clusters. A new asymmetric implicit trust measure is developed by combining partnership dynamics with the Pearson Correlation Coefficient, providing a realistic representation of trust relationships. Furthermore, the model identifies leaders and members within each subgroup, quantifies their mutual influence through dynamic weights, and incorporates these dynamics into an improved Friedkin–Johnsen framework to allow for iterative preference adjustments and alignment toward consensus. The feasibility and validity of the proposed method are demonstrated through a case study and sensitivity analysis, highlighting its adaptability and effectiveness. Simulation experiments further validate the model, showing superior performance compared to existing methods.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110974"},"PeriodicalIF":6.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487677","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":"Trade-offs in ready-mixed concrete truck scheduling considering stochastic congestion: A novel multi-objective model driven by strength Pareto evolutionary algorithm","authors":"Wenshun Wang ,&nbsp;Yuguo Zhang ,&nbsp;Lingyun Mi ,&nbsp;Qinglu Guo ,&nbsp;Lijie Qiao ,&nbsp;Li Wang ,&nbsp;Min Tao ,&nbsp;Jingqun Ma","doi":"10.1016/j.cie.2025.111000","DOIUrl":"10.1016/j.cie.2025.111000","url":null,"abstract":"<div><div>Ready-mixed concrete (RMC) is extensively used in the construction industry due to its high quality and efficiency. However, as market competition intensifies, RMC companies are under increasing pressure to improve their competitiveness while maintaining strong customer relationships. Efficiently dispatching trucks and optimizing RMC delivery to meet both customer demands and company needs has become a significant challenge for RMC companies’ development. To this end, this study first conducts a demand analysis of RMC truck scheduling from both supply and demand perspectives and identifies five key scheduling objectives: operational cost, load rate, load balancing, time windows, and continuity of concrete pouring. Secondly, objective dimensionality reduction is achieved by constructing composite load and penalty functions and integrating a congestion time function to simulate traffic congestion scenarios. On this basis, a tri-objective optimization model that balances total cost, composite load, and customer satisfaction is constructed, which reduces the complexity of model computation without compromising the integrity of objective constraints. Then, the study innovatively designs decision variables for truck numbers and departure intervals and employs the strength Pareto evolutionary algorithm based on reference direction (SPEA/R) to solve the model. Additionally, the mutation operation was improved by introducing traffic congestion variables and dynamically adjusting departure intervals to mitigate the impact of congestion on scheduling. This facilitates the refinement of RMC truck scheduling in terms of both truck configuration and dispatch planning under traffic congestion conditions. Finally, a Chinese RMC company case study was conducted to validate the effectiveness of the proposed model. Based on the findings, a two-dimensional RMC truck scheduling strategy matrix was developed, offering diverse guidance and recommendations for RMC factories to formulate truck scheduling plans that meet varying demands.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111000"},"PeriodicalIF":6.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509544","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}