Pub Date : 2026-03-01Epub Date: 2026-01-10DOI: 10.1016/j.cie.2026.111811
John Maleyeff , Jingran Xu , Ruthairut Wootisarn
Simheuristics is a simulation optimization method that combines simulation with heuristic approaches to solve complex or combinatorically challenging problems. Its performance is considered effective when it converges on a good solution while minimizing the number of simulation runs. Repair part inventory policy is an increasingly important component of inventory management due to the proliferation of equipment and products that need frequent updating, overhaul, or repair. A repair inventory problem, where the repair can start only after all parts needed for the repair are available, is addressed using a two-phase simheuristics algorithm. The approach is unique because in phase 1 it employs a designed experiment to create a metamodel of simheuristics results which, in phase 2, becomes the initial solution presented to the simheuristics algorithm. Results show faster convergence compared to the use of a deterministic model that typically initializes a simheuristics algorithm.
{"title":"Simheuristics with metamodel initialization for determining repair system inventory policies","authors":"John Maleyeff , Jingran Xu , Ruthairut Wootisarn","doi":"10.1016/j.cie.2026.111811","DOIUrl":"10.1016/j.cie.2026.111811","url":null,"abstract":"<div><div>Simheuristics is a simulation optimization method that combines simulation with heuristic approaches to solve complex or combinatorically challenging problems. Its performance is considered effective when it converges on a good solution while minimizing the number of simulation runs. Repair part inventory policy is an increasingly important component of inventory management due to the proliferation of equipment and products that need frequent updating, overhaul, or repair. A repair inventory problem, where the repair can start only after all parts needed for the repair are available, is addressed using a two-phase simheuristics algorithm. The approach is unique because in phase 1 it employs a designed experiment to create a metamodel of simheuristics results which, in phase 2, becomes the initial solution presented to the simheuristics algorithm. Results show faster convergence compared to the use of a deterministic model that typically initializes a simheuristics algorithm.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111811"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977755","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}
Efficient preparation and smooth operation of rail freight trains are essential for improving rail freight services and customer satisfaction. This study examines how automation and digitalisation − specifically Digital Automatic Coupling (DAC) and Virtual Coupling (VC) − can enable seamless rail freight transport within marshalling yards and along railway lines. For the first time, a combined simulation- and optimisation-based modelling approach is proposed to assess the impact of these technologies.
A multi-agent simulation model of the Hallsberg marshalling yard was developed to analyse train handling and yard capacity. A 10-hour shunting operation was simulated under manual coupling and DAC technology, comparing standard train lengths and longer trains. The results indicate a substantial increase in processed trains when DAC was applied. Standard-length trains increased from 7 (manual) to 9 and 12 with DAC types 4 and 5, respectively, with similar gains observed for longer trains.
Trains from the simulation’s departure yard were subsequently integrated into an optimisation model to assess their scheduling on the main railway line. dispatchers face challenges in optimising freight train routing, VC was proposed as a capacity-enhancing measure. The optimisation results showed that, with conventional timetables, only 70 freight trains could be scheduled while prioritising passenger services, whereas VC enables up to 128 freight trains − − an 82.86% capacity increase.
Overall, these results demonstrate that integrating DAC and VC technologies can significantly enhance the efficiency and capacity of rail freight operations and systems, offering substantial benefits to stakeholders across the sector.
{"title":"An approach for seamless rail freight: integration of virtual coupling and digital automatic coupling","authors":"Weiting Yang , Yuguang Wei , Evelin Krmac , Boban Djordjevic","doi":"10.1016/j.cie.2026.111810","DOIUrl":"10.1016/j.cie.2026.111810","url":null,"abstract":"<div><div>Efficient preparation and smooth operation of rail freight trains are essential for improving rail freight services and customer satisfaction. This study examines how automation and digitalisation − specifically Digital Automatic Coupling (DAC) and Virtual Coupling (VC) − can enable seamless rail freight transport within marshalling yards and along railway lines. For the first time, a combined simulation- and optimisation-based modelling approach is proposed to assess the impact of these technologies.</div><div>A multi-agent simulation model of the Hallsberg marshalling yard was developed to analyse train handling and yard capacity. A 10-hour shunting operation was simulated under manual coupling and DAC technology, comparing standard train lengths and longer trains. The results indicate a substantial increase in processed trains when DAC was applied. Standard-length trains increased from 7 (manual) to 9 and 12 with DAC types 4 and 5, respectively, with similar gains observed for longer trains.</div><div>Trains from the simulation’s departure yard were subsequently integrated into an optimisation model to assess their scheduling on the main railway line. dispatchers face challenges in optimising freight train routing, VC was proposed as a capacity-enhancing measure. The optimisation results showed that, with conventional timetables, only 70 freight trains could be scheduled while prioritising passenger services, whereas VC enables up to 128 freight trains − − an 82.86% capacity increase.</div><div>Overall, these results demonstrate that integrating DAC and VC technologies can significantly enhance the efficiency and capacity of rail freight operations and systems, offering substantial benefits to stakeholders across the sector.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111810"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977752","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}
Pub Date : 2026-03-01Epub Date: 2025-12-16DOI: 10.1016/j.cie.2025.111763
Héctor López-Ospina , Lucas Jose Fernandez-Davila , Carlos A. Gonzalez-Calderon , Diana P. Moreno-Palacio , Luz Florez-Calderon
This research develops a multi-objective and multiclass freight tour synthesis transportation model. The model integrates objectives of maximizing trip entropy while minimizing costs and time, including reducing CO2 emissions. The study identified various solutions along the Pareto frontier and evaluated the impact of other constraints on costs, emissions, and time using the epsilon-constraint method. The results show that entropy favors a balanced distribution of resources, while time prioritizes the use of higher-capacity diesel trucks. Minimizing emissions prioritizes electric trucks, highlighting the trade-off between sustainability and operational efficiency. The TOPSIS multicriteria method was used to rank or prioritize the solutions. This method depends on the weight assigned to each objective; thus, a sensitivity analysis of the weights was conducted. The solutions reflect the necessary trade-offs between costs, time, emissions, and system diversity. It is concluded that incorporating environmental and entropy objectives in fleet optimization improves sustainability, operational flexibility, and adaptability.
{"title":"Multi-class freight tour synthesis model incorporating environmental, entropy, cost, and travel time objectives","authors":"Héctor López-Ospina , Lucas Jose Fernandez-Davila , Carlos A. Gonzalez-Calderon , Diana P. Moreno-Palacio , Luz Florez-Calderon","doi":"10.1016/j.cie.2025.111763","DOIUrl":"10.1016/j.cie.2025.111763","url":null,"abstract":"<div><div>This research develops a multi-objective and multiclass freight tour synthesis transportation model. The model integrates objectives of maximizing trip entropy while minimizing costs and time, including reducing CO<sub>2</sub> emissions. The study identified various solutions along the Pareto frontier and evaluated the impact of other constraints on costs, emissions, and time using the epsilon-constraint method. The results show that entropy favors a balanced distribution of resources, while time prioritizes the use of higher-capacity diesel trucks. Minimizing emissions prioritizes electric trucks, highlighting the trade-off between sustainability and operational efficiency. The TOPSIS multicriteria method was used to rank or prioritize the solutions. This method depends on the weight assigned to each objective; thus, a sensitivity analysis of the weights was conducted. The solutions reflect the necessary trade-offs between costs, time, emissions, and system diversity. It is concluded that incorporating environmental and entropy objectives in fleet optimization improves sustainability, operational flexibility, and adaptability.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111763"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799641","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}
Pub Date : 2026-03-01Epub Date: 2025-12-24DOI: 10.1016/j.cie.2025.111785
Prabal Das , Nabendu Sen , Ali Akbar Shaikh
Growing environmental regulations and rising consumer awareness have made it crucial for manufacturers to design inventory systems that strike a balance between profitability and sustainability. This work develops a dynamic green production–inventory model with price- and warranty-sensitive dual-channel demand, preservation investment, product deterioration, warranty-driven remanufacturing, and carbon-emission constraints. The model is evaluated across three cases: (i) a baseline system with warranty-based returns; (ii) a blockchain-enabled cap-and-trade mechanism; and (iii) an AI-guided adaptive preservation strategy that responds to real-time demand and emission levels. The system is formulated using nonlinear differential equations and solved via the Artificial Ecosystem-Based Optimizer (AEO). Case 1 yields an average profit of INR 2997.31. relative to Case 1, Case 2 increases average profit by 180.72% and reduces emissions by 9.52% through carbon-credit trading. Case 3 achieves an average profit of INR 8416.59-＋180.80% vs. Case 1-while reducing emissions by 21.43%. Under matched computational budgets, mainstream metaheuristics (PSO/GA/DE) reach a similar neighborhood of solutions, while AEO exhibits stable convergence with minimal tuning, corroborating robustness. Sensitivity analysis highlights demand elasticity and preservation-investment parameters as dominant profit drivers, and carbon pricing as a key environmental lever. The framework offers a scalable and adaptable decision-support tool for integrating AI, blockchain, and green investment into circular supply-chain design.
{"title":"AI-enabled green production–inventory with dual channels, warranty returns, and blockchain carbon trading","authors":"Prabal Das , Nabendu Sen , Ali Akbar Shaikh","doi":"10.1016/j.cie.2025.111785","DOIUrl":"10.1016/j.cie.2025.111785","url":null,"abstract":"<div><div>Growing environmental regulations and rising consumer awareness have made it crucial for manufacturers to design inventory systems that strike a balance between profitability and sustainability. This work develops a dynamic green production–inventory model with price- and warranty-sensitive dual-channel demand, preservation investment, product deterioration, warranty-driven remanufacturing, and carbon-emission constraints. The model is evaluated across three cases: (i) a baseline system with warranty-based returns; (ii) a blockchain-enabled cap-and-trade mechanism; and (iii) an AI-guided adaptive preservation strategy that responds to real-time demand and emission levels. The system is formulated using nonlinear differential equations and solved via the Artificial Ecosystem-Based Optimizer (AEO). Case 1 yields an average profit of INR 2997.31. relative to Case 1, Case 2 increases <em>average</em> profit by <span><math><mo>≈</mo></math></span>180.72% and reduces emissions by <span><math><mo>≈</mo></math></span>9.52% through carbon-credit trading. Case 3 achieves an average profit of INR 8416.59-<strong>＋180.80% vs. Case 1</strong>-while reducing emissions by <span><math><mo>≈</mo></math></span>21.43%. Under matched computational budgets, mainstream metaheuristics (PSO/GA/DE) reach a similar neighborhood of solutions, while AEO exhibits stable convergence with minimal tuning, corroborating robustness. Sensitivity analysis highlights demand elasticity and preservation-investment parameters as dominant profit drivers, and carbon pricing as a key environmental lever. The framework offers a scalable and adaptable decision-support tool for integrating AI, blockchain, and green investment into circular supply-chain design.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111785"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884900","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}
Pub Date : 2026-03-01Epub Date: 2025-12-16DOI: 10.1016/j.cie.2025.111765
Sheng Jing , Wenhan Fu
Integrated circuit (IC) design is a crucial industry sitting at the upstream of the semiconductor supply chain by providing application-specific design services that enable manufacturers to make flexible and strategic decisions. For IC design service providers, research and development (R&D) capability is the core source of productivity, which are heavily dependent on the effectiveness of project management and the performance of manpower allocation.
In the current intensified market competition and rapid technological evolution, the efficient allocation of R&D resources, particularly manpower, has become essential for IC design industry to maintain sustainable growth. The ability to scientifically match multi-skilled teams with diverse project requirements, design complexities, and potential returns is a key factor in in strengthening the core competitiveness of IC design. However, existing studies have paid limited attention to data-driven approaches for portfolio planning and resource allocation..
To address this gap, this study proposes an integrated optimization approach for skill-aware and collaborative IC design portfolios decision that integrates revenue evaluation, project selection and team assignment optimization to improve the effectiveness and resilience of R&D resource. A multi-objective evolutionary algorithm with feasibility-prioritized search, intelligent repair mechanism and dynamic penalty strategy is developed to derive near-optimal portfolio decisions effectively.
To validate the proposed approach, an empirical study is conducted in an IC design service company. The results show that the proposed approach has good feasibility and capable of improving the project undertaking efficiency and overall profitability, while optimizing project management process. The study provides a data-driven and adaptive decision-support framework to enhance R&D efficiency and organizational resilience in IC design management.
{"title":"Integrated optimization approach for skill-aware and collaborative IC design portfolios decision to enhance R&D project effectiveness and resilience","authors":"Sheng Jing , Wenhan Fu","doi":"10.1016/j.cie.2025.111765","DOIUrl":"10.1016/j.cie.2025.111765","url":null,"abstract":"<div><div>Integrated circuit (IC) design is a crucial industry sitting at the upstream of the semiconductor supply chain by providing application-specific design services that enable manufacturers to make flexible and strategic decisions. For IC design service providers, research and development (R&D) capability is the core source of productivity, which are heavily dependent on the effectiveness of project management and the performance of manpower allocation.</div><div>In the current intensified market competition and rapid technological evolution, the efficient allocation of R&D resources, particularly manpower, has become essential for IC design industry to maintain sustainable growth. The ability to scientifically match multi-skilled teams with diverse project requirements, design complexities, and potential returns is a key factor in in strengthening the core competitiveness of IC design. However, existing studies have paid limited attention to data-driven approaches for portfolio planning and resource allocation..</div><div>To address this gap, this study proposes an integrated optimization approach for skill-aware and collaborative IC design portfolios decision that integrates revenue evaluation, project selection and team assignment optimization to improve the effectiveness and resilience of R&D resource. A multi-objective evolutionary algorithm with feasibility-prioritized search, intelligent repair mechanism and dynamic penalty strategy is developed to derive near-optimal portfolio decisions effectively.</div><div>To validate the proposed approach, an empirical study is conducted in an IC design service company. The results show that the proposed approach has good feasibility and capable of improving the project undertaking efficiency and overall profitability, while optimizing project management process. The study provides a data-driven and adaptive decision-support framework to enhance R&D efficiency and organizational resilience in IC design management.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111765"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792123","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}
Pub Date : 2026-03-01Epub Date: 2025-12-25DOI: 10.1016/j.cie.2025.111784
Xiaoli Zhao, Dewei Li, Xinyu Bao
This study proposes a deep reinforcement learning-based optimization framework for integrated train scheduling and rolling stock circulation planning under dynamic passenger demand. The problem is formulated as a Markov decision process (MDP) with a hybrid action space that simultaneously captures continuous timetable decisions and discrete rolling stock allocations. The objective is to minimize passenger waiting time and operator costs while adhering to complex operational constraints. To address the challenge of simultaneously coordinating continuous and discrete decision variables in a high-dimensional operational context, we adopt a Hybrid Proximal Policy Optimization (HPPO) algorithm, incorporating separate actor networks for discrete and continuous actions, and employing constraint-handling techniques such as action masking and action space embedding. Furthermore, a potential-based reward shaping function is introduced to enhance learning efficiency by addressing issues of sparse and delayed rewards. The proposed approach is validated on the Beijing Metro Changping Line. Experimental results demonstrate that the HPPO algorithm effectively improves system efficiency and policy robustness.
{"title":"A deep reinforcement learning approach for integrated optimization of train scheduling and rolling stock circulation planning","authors":"Xiaoli Zhao, Dewei Li, Xinyu Bao","doi":"10.1016/j.cie.2025.111784","DOIUrl":"10.1016/j.cie.2025.111784","url":null,"abstract":"<div><div>This study proposes a deep reinforcement learning-based optimization framework for integrated train scheduling and rolling stock circulation planning under dynamic passenger demand. The problem is formulated as a Markov decision process (MDP) with a hybrid action space that simultaneously captures continuous timetable decisions and discrete rolling stock allocations. The objective is to minimize passenger waiting time and operator costs while adhering to complex operational constraints. To address the challenge of simultaneously coordinating continuous and discrete decision variables in a high-dimensional operational context, we adopt a Hybrid Proximal Policy Optimization (HPPO) algorithm, incorporating separate actor networks for discrete and continuous actions, and employing constraint-handling techniques such as action masking and action space embedding. Furthermore, a potential-based reward shaping function is introduced to enhance learning efficiency by addressing issues of sparse and delayed rewards. The proposed approach is validated on the Beijing Metro Changping Line. Experimental results demonstrate that the HPPO algorithm effectively improves system efficiency and policy robustness.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111784"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885432","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}
Pub Date : 2026-03-01Epub Date: 2025-12-26DOI: 10.1016/j.cie.2025.111787
Xi Zhang , Amitava Mukherjee , Chenglong Li , Shurong Tong
The retrospective analysis of multivariate and high-dimensional processes in Phase I has garnered increasing attention in the field of industrial quality control. In such complex Phase I settings, where prior information about the underlying process distribution is often scarce, nonparametric methods are particularly valuable. However, research on nonparametric multivariate Phase I analysis remains relatively limited, with most existing studies concentrating on monitoring only a single feature of the underlying distribution. Principal Component Analysis (PCA), a fundamental technique for dimensionality reduction and feature extraction, has been widely adopted in nonparametric Phase II monitoring; however, its potential for Phase I analysis is not yet fully exploited. To address this gap, this paper introduces several novel Phase I schemes that integrate PCA with effective univariate Phase I procedures through different integration strategies. These schemes enable simultaneous monitoring of both location and scale parameters for any unknown multivariate distribution. Extensive Monte Carlo simulation studies demonstrate that the proposed schemes exhibit robust in-control (IC) performance. The results also reveal that some of the proposed schemes outperform others in anomaly detection, particularly in scenarios where out-of-control observations are attributed to shifts in a small subset of variables, as measured by overall performance metrics. The proposed schemes are beneficial for establishing a reference sample and developing an IC model for subsequent Phase II monitoring. Two case studies using real-world data are presented to illustrate the implementation and interpretation of the proposed schemes.
{"title":"Nonparametric Phase I analysis of multivariate data using PCA for industrial quality control","authors":"Xi Zhang , Amitava Mukherjee , Chenglong Li , Shurong Tong","doi":"10.1016/j.cie.2025.111787","DOIUrl":"10.1016/j.cie.2025.111787","url":null,"abstract":"<div><div>The retrospective analysis of multivariate and high-dimensional processes in Phase I has garnered increasing attention in the field of industrial quality control. In such complex Phase I settings, where prior information about the underlying process distribution is often scarce, nonparametric methods are particularly valuable. However, research on nonparametric multivariate Phase I analysis remains relatively limited, with most existing studies concentrating on monitoring only a single feature of the underlying distribution. Principal Component Analysis (PCA), a fundamental technique for dimensionality reduction and feature extraction, has been widely adopted in nonparametric Phase II monitoring; however, its potential for Phase I analysis is not yet fully exploited. To address this gap, this paper introduces several novel Phase I schemes that integrate PCA with effective univariate Phase I procedures through different integration strategies. These schemes enable simultaneous monitoring of both location and scale parameters for any unknown multivariate distribution. Extensive Monte Carlo simulation studies demonstrate that the proposed schemes exhibit robust in-control (IC) performance. The results also reveal that some of the proposed schemes outperform others in anomaly detection, particularly in scenarios where out-of-control observations are attributed to shifts in a small subset of variables, as measured by overall performance metrics. The proposed schemes are beneficial for establishing a reference sample and developing an IC model for subsequent Phase II monitoring. Two case studies using real-world data are presented to illustrate the implementation and interpretation of the proposed schemes.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111787"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885436","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}
Pub Date : 2026-03-01Epub Date: 2025-12-26DOI: 10.1016/j.cie.2025.111792
Siqian Cheng, Jiankun Hu, Youfang Huang
The COVID-19 pandemic has underscored significant vulnerabilities in traditional road-based urban logistics systems under stringent lockdown conditions, prompting the exploration of alternative logistics solutions. This study proposes a novel Metro-based Underground Logistics System (M−ULS) to effectively manage emergency logistics during pandemics by leveraging existing subway infrastructure. We developed a multi-distribution center location optimization model integrating critical factors such as traffic flow, delivery time, service coverage, and cost efficiency. An immune genetic algorithm was adopted to solve this multi-objective model efficiently. Comparative analyses with conventional road logistics demonstrated that the M−ULS substantially improved distribution speed and reliability, achieving up to 90% efficiency in service delivery within high-risk zones. A practical application using a Shanghai case study further confirmed the model’s benefits, highlighting reduced delays and enhanced urban supply chain resilience. The proposed subway-based logistics system offers a systematic and innovative approach for urban planners and public health authorities to enhance emergency preparedness, representing a meaningful advancement in the field of urban logistics and epidemic response strategies.
{"title":"Pandemic resilience through subway-based multi-center logistics: an immune genetic approach","authors":"Siqian Cheng, Jiankun Hu, Youfang Huang","doi":"10.1016/j.cie.2025.111792","DOIUrl":"10.1016/j.cie.2025.111792","url":null,"abstract":"<div><div>The COVID-19 pandemic has underscored significant vulnerabilities in traditional road-based urban logistics systems under stringent lockdown conditions, prompting the exploration of alternative logistics solutions. This study proposes a novel Metro-based Underground Logistics System (M−ULS) to effectively manage emergency logistics during pandemics by leveraging existing subway infrastructure. We developed a multi-distribution center location optimization model integrating critical factors such as traffic flow, delivery time, service coverage, and cost efficiency. An immune genetic algorithm was adopted to solve this multi-objective model efficiently. Comparative analyses with conventional road logistics demonstrated that the M−ULS substantially improved distribution speed and reliability, achieving up to 90% efficiency in service delivery within high-risk zones. A practical application using a Shanghai case study further confirmed the model’s benefits, highlighting reduced delays and enhanced urban supply chain resilience. The proposed subway-based logistics system offers a systematic and innovative approach for urban planners and public health authorities to enhance emergency preparedness, representing a meaningful advancement in the field of urban logistics and epidemic response strategies.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111792"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927272","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}
Pub Date : 2026-03-01Epub Date: 2026-01-06DOI: 10.1016/j.cie.2026.111804
Sibel Çevik Bektaş , Yeşim Aysel Baysal Aslanhan , İsmail Hakkı Altaş
An effective day-ahead planning strategy is pivotal for ensuring the economic, secure, and balanced operation of modern electricity grids. To address this challenge, various metaheuristic methods have been proposed for multi-objective day-ahead energy management, yet many suffer from scalability and convergence issues under realistic operating constraints. This study presents an efficient multi-objective optimization framework for day-ahead hourly optimal energy scheduling (DAHOES) in renewable-integrated distribution systems. The proposed framework employs the Fast Non-Dominated Sorting Multi-Objective Symbiotic Organism Search (FNSMOSOS) algorithm to minimize both active power losses and total operating costs. Following the optimization process, a fuzzy decision-making method is utilized to select a balanced solution from the generated Pareto front, ensuring that the final operation plan aligns with practical performance criteria. To reflect actual distribution system behavior, a modified five-bus distribution network comprising photovoltaic (PV) units, wind energy systems (WES), energy storage systems (ESS), and grid supply is modelled. In addition, realistic hourly demand profiles, renewable generation forecasts, and grid price signals are incorporated to ensure both theoretical optimality and practical feasibility. The proposed algorithm is compared with several other methods, and simulation results show that FNSMOSOS outperforms NSMOCS by 24.1% in HV and surpasses MOGWO, MOWOA, and MONNA by 56%, 117%, and 790%, respectively, demonstrating superior Pareto convergence and diversity. Overall, the results confirm that the proposed framework offers a scalable and effective decision-support tool for distribution system operators facing multi-criteria scheduling challenges in complex and uncertain power systems.
{"title":"Efficient day-ahead energy scheduling in distribution systems via multi-objective symbiotic organism search","authors":"Sibel Çevik Bektaş , Yeşim Aysel Baysal Aslanhan , İsmail Hakkı Altaş","doi":"10.1016/j.cie.2026.111804","DOIUrl":"10.1016/j.cie.2026.111804","url":null,"abstract":"<div><div>An effective day-ahead planning strategy is pivotal for ensuring the economic, secure, and balanced operation of modern electricity grids. To address this challenge, various metaheuristic methods have been proposed for multi-objective day-ahead energy management, yet many suffer from scalability and convergence issues under realistic operating constraints. This study presents an efficient multi-objective optimization framework for day-ahead hourly optimal energy scheduling (DAHOES) in renewable-integrated distribution systems. The proposed framework employs the Fast Non-Dominated Sorting Multi-Objective Symbiotic Organism Search (FNSMOSOS) algorithm to minimize both active power losses and total operating costs. Following the optimization process, a fuzzy decision-making method is utilized to select a balanced solution from the generated Pareto front, ensuring that the final operation plan aligns with practical performance criteria. To reflect actual distribution system behavior, a modified five-bus distribution network comprising photovoltaic (PV) units, wind energy systems (WES), energy storage systems (ESS), and grid supply is modelled. In addition, realistic hourly demand profiles, renewable generation forecasts, and grid price signals are incorporated to ensure both theoretical optimality and practical feasibility. The proposed algorithm is compared with several other methods, and simulation results show that FNSMOSOS outperforms NSMOCS by 24.1% in HV and surpasses MOGWO, MOWOA, and MONNA by 56%, 117%, and 790%, respectively, demonstrating superior Pareto convergence and diversity. Overall, the results confirm that the proposed framework offers a scalable and effective decision-support tool for distribution system operators facing multi-criteria scheduling challenges in complex and uncertain power systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"213 ","pages":"Article 111804"},"PeriodicalIF":6.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927271","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}
Pub Date : 2026-03-01Epub Date: 2026-01-02DOI: 10.1016/j.cie.2026.111803
Mohammad Javad Eslami , Mohsen Varmazyar
Ambulances, one of the essential resources in the emergency medical service (EMS), are crucial in transporting patients to hospitals and saving lives. This research addresses the ambulance service scheduling problem (ASSP) for daily planning decisions by minimizing total weighted tardiness. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be NP-hard, two population-based genetic algorithm (GA) and particle swarm optimization (PSO), and two solution-based, simulated annealing (SA) and tabu search (TS) meta-heuristics are proposed to solve this problem. In addition, the Lagrangian relaxation (LR) and Benders decomposition methods are employed to find effective lower bounds. Random test problems with small, medium, and large sizes are generated and solved by the proposed algorithms to evaluate their performance. Numerical results show that the LR and Benders decomposition can find efficient lower bounds with approximately 4 % and 6 % gap rates, respectively. Furthermore, ANOVA and Tukey’s HSD tests indicate that the GA, PSO, and SA algorithms perform better in small-, medium-, and large-size problems, respectively. It is noticeable that the best-obtained meta-heuristic solutions have a gap rate of approximately 6.21 %, with the best-obtained lower bounds. Moreover, due to the ASSP problem’s stochastic nature, we develop a two-stage stochastic programming model by considering each mission’s weight and time under uncertainty. Additionally, considering enough scenarios, which in our research is 40, the optimal value can be closely approximated. The outputs of this research are employed for a real-world case study as well. Finally, some managerial and practical insights are discussed based on the results.
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