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        <title>Frontiers in Industrial Engineering | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/industrial-engineering</link>
        <description>RSS Feed for Frontiers in Industrial Engineering | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-02T05:03:31.916+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1670308</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1670308</link>
        <title><![CDATA[The effect of facility layout changes and 5S implementation on reworks using MCDM: a lean implementation case study]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tlotlo K. Ramasu</author><author>Mukondeleli G. Kanakana-Katumba</author><author>Tshifhiwa Nenzhelele</author><author>Khumbulani Mpofu</author>
        <description><![CDATA[IntroductionSmall and medium-sized enterprises (SMEs) in the manufacturing sector often struggle with inefficiencies, defective products, and weak employee–management collaboration, all of which weaken their competitiveness and long-term sustainability. This case study explores the implementation of a lean implementation program at H4A Pty Ltd, a composite manufacturing plant in response to persistent reworks, and delays which affect productivity. The study aims to assess how 5S, facility layout and workplace cooperation impact the achievement of key operational performance indicators. The lean approach adopted in the study aligns closely with Sustainable Development Goals 4 and 8, supporting SMEs in enhancing their sustainability, responsibility, and competitiveness through quality education thereby contributing to the advancement of a more resilient and sustainable economy.MethodsThe study focused on 5S methodology, facility layout changes using multi criteria decision making (MCDM) Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and improved monitoring to eliminate defective products. Implementation involved structured employee training, participatory decision-making workshops, and management support to ensure sustainability.ResultsResults demonstrated significant improvements in efficiency, including a marked reduction in defective products, improved workflow due to optimized facility layout, and stronger communication between management and staff. Defect rates were reduced to zero during the implementation phase, while key performance indicators (KPIs) for productivity showed consistent upward trends.DiscussionAlthough the study was limited to only one firm, the findings suggest that integrating 5S with MCDM-based facility layout provides a replicable strategy for manufacturing firms seeking to improve productivity, meet KPIs, and foster a cooperative workplace environments, while advancing sustainable development objectives.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2026.1803536</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2026.1803536</link>
        <title><![CDATA[Editorial: Learning-driven optimization for solving scheduling and logistics]]></title>
        <pubdate>2026-02-19T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Shi Cheng</author><author>Mitsuo Gen</author><author>Jie Gao</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2026.1769776</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2026.1769776</link>
        <title><![CDATA[A hybrid neural network integrating attention mechanism for time series and non-time series multi-factor electric vehicle energy consumption prediction]]></title>
        <pubdate>2026-02-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenqiang Zhang</author><author>Ruisheng Chai</author><author>Mingzhe Li</author><author>Yashuang Mu</author><author>Peng Li</author><author>Mitsuo Gen</author>
        <description><![CDATA[IntroductionIn recent years, electric vehicles (EVs) have garnered increasing consumer favor due to their low energy consumption and mechanical simplicity; however, the persistent limitation of short driving range has not been fundamentally resolved and continues to fuel drivers’ range anxiety. To enhance the accuracy of EV energy-consumption prediction, this paper categorizes influencing factors from multiple perspectives and proposes a hybrid neural-network prediction model that integrates temporal features and an attention mechanism.MethodsThe model first partitions the dataset into time-series and non-time-series subsets based on temporal correlation. A convolutional neural network (CNN) is then employed to extract and reconstruct features from the time-series data to reduce computational complexity, after which an attention-enhanced bidirectional gated recurrent unit (AtBiGRU) further captures sequential dependencies. The resulting fitted representations, together with the non-time-series variables, are fed into a deep neural network (DNN) for ensemble learning, yielding precise energy-consumption predictions. By processing sequential and non-sequential data separately, the method effectively improves computational efficiency and model expressiveness.ResultsExperimental results demonstrate that the proposed CNN–AtBiGRU–DNN hybrid model achieves higher prediction accuracy and faster convergence than baseline algorithms.ConclusionThe proposed model validates its effectiveness and advancement in EV energy-consumption prediction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1686126</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1686126</link>
        <title><![CDATA[Efficiently solving open capacitated location-routing problems through a discrete fireworks algorithm]]></title>
        <pubdate>2025-11-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huizhen Zhang</author><author>Xun Zhou</author><author>David Rios Insua</author>
        <description><![CDATA[In the open capacitated location-routing problem (OCLRP), a fleet of distribution vehicles departs from selected depots to satisfy customers’ demands, but they do not need to return to their starting depots after serving all customers. Comparing the solutions of the OCLRP and its corresponding capacitated location-routing problem (CLRP) can provide valuable insights for companies considering whether to outsource their delivery activities. To effectively solve the OCLRP, this study proposes a novel discrete fireworks algorithm (DFWA) with two key innovations. (1) An adaptive search mechanism: embedding swap, insertion, and reverse operations into explosion/mutation breaks the limitations of traditional fireworks algorithms in discrete optimization by expanding the search space while enhancing local tuning, thus boosting global optimal solution discovery. (2) A diversified selection strategy: integrating fitness value and Hamming distance improves the “premature convergence from single fitness selection” defect in existing algorithms. It retains high-performance solutions while maintaining population diversity. Evaluated on OCLRP instances adapted from standard CLRP benchmarks, the DFWA shows strong competitiveness, consistently generating high-quality solutions within reasonable computation time. A real-world OCLRP case further verifies its practical applicability in complex industrial scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1620422</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1620422</link>
        <title><![CDATA[Hybrid heuristic approach for generalized police officer patrolling problem]]></title>
        <pubdate>2025-08-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fumito Kudo</author><author>Hiroaki Tohyama</author><author>Masaki Tomisawa</author>
        <description><![CDATA[In urban areas with many commercial facilities, patrolling by police officers or security guards is essential for crime prevention, in addition to the use of surveillance cameras. To address the challenge of planning effective patrol routes, Tohyama and Tomisawa introduced the Police Officer Patrolling Problem (POPP), an arc routing problem that allows for visual monitoring from intersections and is proven to be NP-complete. Building on this work, we propose the Generalized POPP (GPOPP), a more realistic bi-objective combinatorial optimization model. This model simultaneously minimizes the total patrol route length and maximizes the coverage of surveillance areas. The contributions of this paper are threefold: (1) we formulate the GPOPP by incorporating practical constraints, such as mandatory patrolling of high-security roads and visibility-based coverage from intersections; (2) we develop a novel hybrid heuristic method that combines a multi-objective evolutionary algorithm (MoEA-HSS) with an improved Jaya algorithm to solve the GPOPP effectively; and (3) we conduct comprehensive computational experiments using benchmark instances to evaluate the effectiveness and competitiveness of the proposed method. These contributions demonstrate the practicality and efficiency of our approach for addressing realistic urban patrolling problems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1611512</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1611512</link>
        <title><![CDATA[Multi-agent reinforcement learning for flexible shop scheduling problem: a survey]]></title>
        <pubdate>2025-07-31T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Weitao Xu</author><author>Jinghan Gu</author><author>Wenqiang Zhang</author><author>Mitsuo Gen</author><author>Hayato Ohwada</author>
        <description><![CDATA[This paper presents a systematic and comprehensive review of multi-agent reinforcement learning (MARL) methodologies and their applications in addressing the flexible shop scheduling problem (FSSP), a fundamental yet challenging optimization paradigm in contemporary manufacturing systems. While conventional optimization approaches exhibit limitations in handling the inherent multi-resource constraints, dynamics and stochastic characteristics of real-world FSSP scenarios, MARL has emerged as a promising alternative framework, particularly due to its capability to effectively manage complex, decentralized decision-making processes in dynamic environments. Through a rigorous analytical framework, this study synthesizes and evaluates the current state-of-the-art MARL implementations in FSSP contexts, encompassing critical aspects such as problem formulation paradigms, agent architectural designs, learning algorithm frameworks, and inter-agent coordination mechanisms. We conduct an in-depth examination of the fundamental challenges inherent in MARL applications to FSSP, including the optimization of state-action space representations, the design of effective reward mechanisms, and the resolution of scalability constraints. Furthermore, this review provides a comparative analysis of diverse MARL paradigms, including centralized training with decentralized execution, fully decentralized approaches, and hierarchical methodologies, critically evaluating their respective advantages and limitations within the FSSP domain. The study culminates in the identification of significant research gaps and promising future research directions, with particular emphasis on theoretical foundations and practical implementations. This comprehensive review serves as an authoritative reference for researchers and practitioners in the field, providing a robust theoretical foundation and practical insights for advancing the application of MARL in flexible shop scheduling and related manufacturing optimization domains. The findings presented herein contribute to the broader understanding of intelligent manufacturing systems and computational optimization in Industry 4.0 contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1605975</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1605975</link>
        <title><![CDATA[Cognitive ergonomics: Triangulation of physiological, subjective, and performance-based mental workload assessments]]></title>
        <pubdate>2025-06-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Emmie Fogelberg</author><author>Huizhong Cao</author><author>Peter Thorvald</author>
        <description><![CDATA[IndroductionAs the manufacturing assembly industry advances, increased customizations and product variety results in operators’ executing more cognitively complex tasks. To bridge these cognitive challenges, the assessment of operators’ health and performance in relation to their tasks has become an increasingly important topic in the field of cognitive ergonomics.MethodsThis paper examines operators’ mental workload through an integrated approach by implementing measures covering different mental workload signals: physiological, performance-based, and subjective, while assembling a 3D-printed drone. In this study, four validated mental workload instruments were used and their correlation levels were evaluated: error rate, completion time, the Rating Scale Mental Effort (RSME), and Heart Rate Variability (HRV).ResultsThe results indicate that three out of four mental workload measures significantly correlate and can effectively be used to support the assessment of mental workload. More specifically, error rate, completion time, and RSME.DiscussionSince current literature has stressed the importance of developing a multidimensional mental workload assessment framework, this paper contributes with new findings applicable to the manufacturing assembly industry.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1523203</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1523203</link>
        <title><![CDATA[Hybrid genetic algorithm and Q-learning-based solution for the time-variant berth and quay crane allocation problem]]></title>
        <pubdate>2025-03-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chengji Liang</author><author>Dong Tang</author><author>Rui Zhao</author><author>Yu Wang</author>
        <description><![CDATA[IntroductionThis study addresses the joint scheduling optimization of continuous berths and quay cranes by proposing a time-variant quay crane allocation method.MethodsA coordinated optimization model is constructed that considers the temporal dimension of quay crane scheduling and equipment collision factors to reduce overall port operational costs. A hybrid intelligent algorithm integrating Q-learning is innovatively designed, using a genetic algorithm as the main framework while embedding a quay crane allocation module and dynamically selecting genetic operators through Q-learning to achieve adaptive optimization of the evolutionary mechanism.ResultsThe module with Q-learning optimization is compared to the module without Q-learning optimization, demonstrating that the Q-learning module can accelerate the convergence of the algorithm and has a better ability to find the optimal solution in large-scale cases, proving the effectiveness of the module.DiscussionThe results show that the proposed algorithm and CPLEX perform similarly in small-scale cases, while the solution speed and capability are better than the genetic algorithm in large-scale problems and superior to the CPLEX algorithm with time constraints in some cases, proving the effectiveness and superiority of the proposed algorithm.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2025.1540022</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2025.1540022</link>
        <title><![CDATA[Metaheuristics for multi-objective scheduling problems in industry 4.0 and 5.0: a state-of-the-arts survey]]></title>
        <pubdate>2025-01-27T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Wenqiang Zhang</author><author>Xuan Bao</author><author>Xinchang Hao</author><author>Mitsuo Gen</author>
        <description><![CDATA[The advent of Industry 4.0 and the emerging Industry 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels of complexity in production scheduling. This complexity is further amplified by the integration of cyber-physical systems, Internet of Things, Artificial Intelligence, and human-centric approaches, necessitating more sophisticated optimization methods. This paper aims to provide a more comprehensive perspective on the application of metaheuristic algorithms in shop scheduling problems within the context of Industry 4.0 and Industry 5.0. Through a systematic review of recent literature (2015–2024), we analyze and categorize various metaheuristic approaches, including Evolutionary Algorithms (EAs), swarm intelligence, and hybrid methods, that have been applied to address complex scheduling challenges in smart manufacturing environments. We specifically examine how these algorithms handle multiple competing objectives such as makespan minimization, energy efficiency, production costs, and human-machine collaboration, which are crucial in modern industrial settings. Our survey reveals several key findings: 1) hybrid metaheuristics demonstrate superior performance in handling multi-objective optimization compared to standalone algorithms; 2) bio-inspired algorithms show promising results in addressing complex scheduling and multi-objective manufacturing environments; 3) tri-objective and higher-order multi-objective optimization problems warrant further in-depth exploration; and 4) there is an emerging trend towards incorporating human factors and sustainability objectives in scheduling optimization, aligned with Industry 5.0 principles. Additionally, we identify research gaps and propose future research directions, particularly in areas such as real-time scheduling adaptation, human-centric optimization, and sustainability-aware scheduling algorithms. This comprehensive review provides insights for researchers and practitioners in the field of industrial scheduling, offering a structured understanding of current methodologies and future challenges in the evolution from Industry 4.0 to 5.0.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1426074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1426074</link>
        <title><![CDATA[Semantic-based systems engineering for digitalization of space mission design]]></title>
        <pubdate>2024-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elaheh Maleki</author><author>Alberto Gonzalez Fernandez</author><author>Nils Fischer</author><author>Quirien Wijnands</author><author>Nikolena Christofi</author>
        <description><![CDATA[The engineering of space systems is a collaborative, iterative process that integrates various domain-specific viewpoints to represent the final system. To ensure consistency across these viewpoints, the European Space Agency (ESA) employs Model-Based System Engineering (MBSE) and Semantic-Based System Engineering (SBSE) methodologies together to improve digital continuity and interoperability across collaborative space system developments. One significant application of semantic engineering in SE is the ESA MBSE Methodology. The ESA MBSE Methodology provides a standardized approach aligned with the European Cooperation for Space Standardization (ECSS), promotes interoperability across MBSE methodologies and tools, and overcomes integration challenges. ESA MBSE Methodology is the input for the Overall Semantic Modeling for Space System Engineering (OSMoSE) which leverages interoperability in the space community. Case studies, such as the EagleEye Earth Observation mission, demonstrate practical applications, highlighting how semantic models enhance efficiency in complex space systems. This paper discusses the importance of semantics and data management in SE and presents a practical solution derived from the ESA MBSE Methodology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1342734</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1342734</link>
        <title><![CDATA[Overlaps between industrial informatics and control, data acquisition and management in Big Science]]></title>
        <pubdate>2024-08-07T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Gabriele Manduchi</author>
        <description><![CDATA[Big Science applications require very large infrastructures and often involve different countries in order to achieve important scientific results or to find solutions to the major problems of mankind, such as finding a clean and endless source of energy. Big Science applications represent not only a scientific challenge, but also large engineering applications involving a wide range of technologies shared with other industrial applications. As a consequence there is a significant overlap in technologies and approaches between Big Science and Industry. In this paper, the overlap between Big Science and industrial applications will be presented in more detail under the control perspective, that is, by highlighting the common aspects between industrial informatics and the control, data acquisition and data management in large scientific applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1407367</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1407367</link>
        <title><![CDATA[Diffusion of AI value-driven services in the German manufacturing industries—an empirical examination of value-driven service references classified by the business Model Canvas]]></title>
        <pubdate>2024-07-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Patrick Berger</author><author>Joerg von Garrel</author>
        <description><![CDATA[This study investigates the diffusion of AI-based service applications within the business models of German manufacturing industries, surveying 162 decision-makers. The integration of AI into business model is assessed through the Business Model Canvas (BMC) framework, evaluating its value in terms of effectiveness as well as efficiency. Rather than focusing on specific use cases, the study delves into the intended usage of value-driven AI services references to enhance effectiveness and efficiency across various elements of the business models. Through this research, eleven service values have been identified. Each service vale corresponds to a distinct element of the BMC. Decision-makers were surveyed using a Confirmation/Disconfirmation (C/D) paradigm to measure the disparities between their current and target performance levels. Consequently, this study provides valuable insights from the perspective of decision makers regarding the current and desired state of AI integration in the German manufacturing industry, taking into account AI usage or no AI usage at the time of data collection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1426631</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1426631</link>
        <title><![CDATA[Strategic implementation of ED&I: unveiling the multifaceted impact on innovation, governance, and ethical conduct in engineering organizations]]></title>
        <pubdate>2024-07-19T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Yagmur Atescan-Yuksek</author><author>John Patsavellas</author><author>Konstantinos Salonitis</author>
        <description><![CDATA[In contemporary organizational landscapes, Equity, Diversity, and Inclusion (ED&I) stand as pivotal pillars for fostering innovation, resilience, and sustainable growth. This article explores the critical importance of ED&I within engineering organizations, focusing on the strategies for understanding ED&I dynamics, implementing inclusive environments, and extending ED&I principles through the industrial value chain. It investigates the complexities of individual identities, the significance of intersectionality, and the strategic advantage of diversity for organizational performance. By exploring comprehensive governance of ED&I initiatives, the role of leadership in fostering diversity, and the impact of ED&I on organizational sustainability and innovation, this study provides a holistic view of the challenges and opportunities in creating inclusive workplaces.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1409748</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1409748</link>
        <title><![CDATA[A critical juncture: promoting responsible innovation in the self-driving automobile sector while improving human factors]]></title>
        <pubdate>2024-07-09T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Kayli Battel</author><author>David Pearl</author>
        <description><![CDATA[Navigating the future of autonomous vehicles (AVs) brings promise and peril. This paper zeroes in on Tesla’s innovative yet sometimes controversial approach to AVs, spotlighting the intersection of human cognition, vehicle automation, and safety. Amid the excitement of rapid tech advancements, we highlight the risks of over-reliance and potential misperceptions fueled by marketing overreach. Introducing the “Quick Car Scorecard,” we offer a solution to empower consumers in deciphering AV usability, bridging tech specs with real-world needs. As AVs steer our future, it is crucial to prioritize human life and responsible innovation. The journey to automation demands not just speed, but utmost caution and clarity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1353531</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1353531</link>
        <title><![CDATA[Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments]]></title>
        <pubdate>2024-05-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Daniel Christopher Merten</author><author>Annick Lesne</author><author>Yilmaz Uygun</author><author>Marc-Thorsten Hütt</author>
        <description><![CDATA[Introduction: Production systems are bound to operate in stochastic conditions. Prominent sources of performance-reducing uncertainty are constituted by machine failures, decision errors, and fluctuating supplies. This article offers a novel approach to uncertainty through modelling and simulation of nonlinear production systems. In particular, the authors consider production systems where the output is drastically reduced when a resource of fluctuating input values reaches an upper threshold.Methods: The article introduces minimal models of such hreshold-impeded stochastic production (TISP) systems and the system performance (i.e., the output) is analyzed as a function of system parameters (e.g., the type of nonlinearity) and noise input features (e.g., the distribution width or time correlations). Applications to steel manufacturing via continuous casting and power generation through wind turbines are discussed in detail.Results and Discussion: The simulation experiments illustrate that especially the extent of the input fluctuations affects the output performance which is why the authors recommend that TISP system operators counterbalance such fluctuations if possible.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1337174</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1337174</link>
        <title><![CDATA[Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey]]></title>
        <pubdate>2024-02-28T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Wenqiang Zhang</author><author>Guanwei Xiao</author><author>Mitsuo Gen</author><author>Huili Geng</author><author>Xiaomeng Wang</author><author>Miaolei Deng</author><author>Guohui Zhang</author>
        <description><![CDATA[Multi-objective scheduling problems in workshops are commonly encountered challenges in the increasingly competitive market economy. These scheduling problems require a trade-off among multiple objectives such as time, energy consumption, and product quality. The importance of each optimization objective typically varies in different time periods or contexts, necessitating decision-makers to devise optimal scheduling plans accordingly. In actual production, decision-makers confront intricate multi-objective scheduling problems that demand balancing clients’ requirements and corporate interests while concurrently striving to reduce production cycles and costs. In solving various problems, multi-objective evolutionary algorithms have attracted the attention of researchers and gradually become one of the mainstream methods to solve these problems. In recent years, research combining multi-objective evolutionary algorithms with machine learning technology has shown great potential, opening up new prospects for improving the performance of multi-objective evolutionary methods. This article comprehensively reviews the latest application progress of machine learning in multi-objective evolutionary algorithms for scheduling problems. We review various machine learning techniques employed for enhancing multi-objective evolutionary algorithms, particularly focusing on different types of reinforcement learning methods. Different categories of scheduling problems addressed using these methods were also discussed, including flow-shop scheduling issues, job-shop scheduling challenges, and more. Finally, we highlighted the challenges faced by the field and outlined future research directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2023.1266651</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2023.1266651</link>
        <title><![CDATA[We need a theoretical framework for the modernization of industrial legacy systems]]></title>
        <pubdate>2023-11-23T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Paulo Garcia</author><author>Warisa Sritriratanarak</author>
        <description><![CDATA[Industrial informatics brings computational intelligence to industry, powering the “software-ization” of manufacturing processes. However, when faced with the myriad of legacy systems that cannot be fully replaced cost-effectively, practitioners must retrofit computational intelligence into legacy systems. This modernization of legacy industrial systems is deceptively challenging: poor retrofitting can cause more harm than good, hindering overall metrics. We argue for a theoretical framework for modernizing legacy industrial systems. We illustrate the challenge within the context of the real-time performance of industrial cyber-physical systems by depicting a formalization of the problem and illustrating its impact through Monte Carlo methods. We show how knowledge of extant system internals constrains possible optimizations. We conclude by highlighting several research directions, including some recommendations, that must be pursued to establish a common theoretical underpinning that can inform practitioners.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2023.1267244</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2023.1267244</link>
        <title><![CDATA[Workforce planning in project-driven companies: a high-level guideline]]></title>
        <pubdate>2023-11-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>G. J. L. Micheli</author><author>A. Martino</author><author>F. Porta</author><author>A. Cravello</author><author>M. Panaro</author><author>A. Calabrese</author>
        <description><![CDATA[Workforce Planning (WFP) has become a crucial part of the governance of project-driven companies and has been deemed fundamental to drive critical decisions on resource management. To manage manpower planning, companies independently developed internal procedures according to their sector, size, and skills. Despite the efforts to create a reliable workforce planning process, a lack of knowledge, standardization and sharing might lead to misalignment and to heterogeneous approaches among different organizations. This study aims at investigating the current knowledge of the WFP, pointing at the detection of its key factors in terms of process steps, application context, methods, input data, actors, tools and reports’ frequency. Additionally, it attempts to define WFP high-level guidelines which can be generally valid for project-driven organizations. The research seeks to meet these goals by combining the results of the academic literature review on the WFP with the findings of the empirical study in which the representatives of ten project-based enterprises participated. The paper describes the key principles of WFP and its main process’ sections, offering high-level guidelines in terms of recommended process steps, actors involved, operative models, data input, report’s frequency, and tools. The presented features, generated by the literature review and the empirical study, are meant to be generally applicable to project-driven companies and to support the practitioners initiating this process in their organization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2023.1258241</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2023.1258241</link>
        <title><![CDATA[The impact of Enabling Collaborative Situations in AR-assisted consignment tasks]]></title>
        <pubdate>2023-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nathan Compan</author><author>Jérémy Matias</author><author>Matthieu Lutz</author><author>Géraldine Rix-Lièvre</author><author>Daniel Brissaud</author><author>Clément Belletier</author><author>Fabien Coutarel</author>
        <description><![CDATA[The introduction of emerging technologies is often an opportunity to redesign the workstation. The Enabling Collaborative Situation (ECS) is a theoretical proposal that aims to deploy the operator’s activity in a collaborative situation with an emerging technology. This approach often seems to be neglected by designers. It is a practical tool for industrialists to guide the design of new work situations as well as for the evaluation of existing situations. An experiment was designed to reproduce a consignment operation. The initial situation corresponded to a classic situation with a paper-based operating procedure. The second situation was assisted by an augmented reality (AR) device and corresponded to either a good intensity of ECS or a low intensity of ECS (which is classically observed in a factory). This study has succeeded in creating an ECS, and it is well perceived as such by the subjects, but the different improvements are not perceived as important enough to make the overall experience better (satisfaction, comprehension, accessibility, performance, etc.). As it was a first attempt, the transformations of the situation were limited. This low intensity of change may explain some of the results of the experiment, but this first attempt also shows the originality and the interest of this work.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2023.1223989</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2023.1223989</link>
        <title><![CDATA[Conceptual application of digital twins to meet ESG targets in the mining industry]]></title>
        <pubdate>2023-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rachel Cranford</author>
        <description><![CDATA[Environmental, social, and governance (ESG) focus continues to gain traction in the mining industry through publicly made policies, promises, and commitments. In 2022, both ESG and technological investments were identified in the top trends by Deloitte and in the top risks and opportunities by Ernst and Young. As the first step in the value chain, the mining industry sets the foundation for most industries in meeting their ESG targets. Beyond providing sustainable materials, the mining industry is required to produce the critical minerals needed for the creation of sustainable technologies. With an ongoing debate on how ESG factors should be measured and inconsistent reporting between mining companies, there remains a gap in consistent and auditable progress in ESG reporting. This study evaluates the application of a digital twin technology to bridge the gap in ESG reporting. By examining the use of digital twin technology through thirty case studies and theoretical applications across industries that share commonalities with mining, this study analyzes the opportunity to apply the technology to the mining industry. The research found that digital twin technology can be applied across all mining project phases and can provide added value to improve multiple ESG factors and measure them. Though the research identifies that there are benefits from the application of digital twin technology to all project phases, and across all three ESG dimensions, there remains challenges to implementation. Successful implementation of digital twin technology will require the right people with the right capabilities. Though suggested that the mining industry should let other industries stabilize the digital twin market due to their history and substantial investment in data systems, it is arguable through literature, case studies and leading mining companies’ investments in precursor technologies to digital twins, that solutions are available and scalable, and the time to wait is over.]]></description>
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