Search Results (2,107)

The Line Balancing Problem (LBP) is a classic optimization topic in production management, aiming to improve efficiency through task allocation. With the transformation of the manufacturing industry towards intelligence, customization, and sustainability, its research scope has been significantly expanded. This study systematically reviews the recent research progress and proposes the C|H|V|E framework to analyze the LBP in four dimensions: (i) extension of the core line problem; (ii) horizontal integration with shop-floor decision-making; (iii) vertical coordination with enterprise-level operations; and (iv) extension of the value from efficiency improvement to sustainability and resilience enhancement. The review focuses on emerging trends, including artificial intelligence and data-driven approaches, digital twin-based optimization, flexible human-machine collaboration, and system integration across the lifecycle and circular economy. This paper provides a systematic overview of the current state of LBP research and explains how it continues to expand its boundaries by incorporating knowledge from new fields. Full article

The rapid growth of road freight has increased urban carbon emissions, but decarbonization in this sector remains slow compared to other areas. This study examines city-level road freight decarbonization, focusing on extreme values, with the goal of establishing a quantitative reference indicator for tailored policies. Using data from 342 Chinese cities, we applied K-means clustering and Extreme Value Theory (EVT) to estimate the extreme levels of freight vehicles decarbonization (FVDEL) under various economic scenarios. Results show notable differences among city types. High-Tech and Light Industry Cities (Type I) display a more substantial decarbonization potential, with a key threshold around 1.27%. Surpassing this level indicates higher readiness for zero-emission road freight, while Heavy Industry-Manufacturing Cities (Type II) tend to behave more predictably during economic ups and downs because of their close ties between industry and freight activities. The study also finds that purchase subsidies tend to have diminishing returns, whereas operational incentives like electricity price discounts and road access advantages are more effective in maintaining adoption. By proposing EVT-based thresholds as practical benchmarks, this research connects statistical modeling with policy implementation. The proposed reference indicator offers useful guidance for assessing urban decarbonization capacity and developing customized strategies to promote zero-emission freight systems. Full article

In the highly competitive manufacturing environment, customers are increasingly demanding punctual, flexible, and customized deliveries, compelling enterprises to balance profit, energy efficiency, and production performance while seeking new scheduling methods to enhance dynamic responsiveness. Although deep reinforcement learning (DRL) has made progress in dynamic flexible job shop scheduling, existing research has rarely addressed profit-oriented optimization. To tackle this challenge, this paper proposes a novel multi-objective dynamic flexible job shop scheduling (MODFJSP) model that aims to maximize net profit and minimize makespan on the basis of traditional FJSP. The model incorporates uncertainties such as new job insertions, fluctuating due dates, and high-profit urgent jobs, and establishes a multi-agent collaborative framework consisting of “job selection–machine assignment.” For the two types of agents, this paper proposes adaptive state representations, reward functions, and variable action spaces to achieve the dual optimization objectives. The experimental results show that the double deep Q-network (DDQN), within the multi-agent cooperative framework, outperforms PPO, DQN, and classical scheduling rules in terms of solution quality and robustness. It achieves superior performance on multiple metrics such as IGD, HV, and SC, and generates bi-objective Pareto frontiers that are closer to the ideal point. The results demonstrate the effectiveness and practical value of the proposed collaborative framework for solving MODFJSP. Full article

This research investigates the strategic interplay between pricing, delivery promises, and handling strategies for service-level returns—products returned by consumers due to operational issues like late delivery rather than product defects. In a vertical decentralized supply chain with a manufacturer and an e-tailer, a shorter promised delivery lead time (PDL) attracts more customers but also increases the risk of late delivery, making products more return-prone. Modeling the return rate as an endogenous variable dependent on the e-tailer’s PDL decision, we develop a Manufacturer-Stackelberg (MS) game-theoretic model to examine whether service-level returns should be handled by the manufacturer (Buy-Back strategy) or the e-tailer (No-Returns strategy). The results suggest that the optimal handling strategy depends on the e-tailer’s reselling ratio—a measure of its efficiency in extracting value from returns. A win-win situation is achieved when the reselling ratio is smaller than a threshold, as the manufacturer’s decision to buy back these returns also benefits the e-tailer. Surprisingly, when the manufacturer leaves the e-tailer to handle FFRs, a higher reselling ratio is not necessarily profitable for the e-tailer. Extending the analysis to a retailer-Stackelberg (RS) scenario reveals that the supply chain’s power structure is a fundamental determinant of the optimal returns handling strategy, shifting the equilibrium from a counterintuitive, power-distorted outcome in a MS system to an intuitive, profit-driven one in a RS system. Full article

In the interior decoration panel industry, automated production lines have become the standard configuration for large-scale enterprises. However, during the panel processing procedures such as sanding and painting, the loss of traditional identification markers like QR codes or barcodes is inevitable. This creates a critical technical bottleneck in the assembly stage of customized or multi-model parallel production lines, where identifying individual panels significantly limits production efficiency. To address this issue, this paper proposes a high-precision measurement method based on close-range photogrammetry for capturing panel dimensions and hole position features, enabling accurate extraction of identification markers. Building on this foundation, an identity discrimination method that integrates weighted dimension and hole position IDs has been developed, making it feasible to efficiently and automatically identify panels without physical identification markers. Experimental results demonstrate that the proposed method exhibits significant advantages in both recognition accuracy and production adaptability, providing an effective solution for intelligent manufacturing in the home decoration panel industry. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm. Full article

Omni-channel retailers arise to address the deficiencies in consumers’ online shopping experiences; the resulting competition between such retailers and traditional online platforms presents substantial challenges for green product manufacturers. A three-level game model is established to examine a manufacturer’s green contract design (product pricing and greenness determination) and mode selection under the competition between an online platform and a new retailer providing omni-channel services to end customers. The manufacturer can select between two modes: supplying a green product to the online platform and new retailer (mode RR) or selling it directly through the online platform and reselling it to the new retailer (mode PR). Our findings indicate that, first, even if the relationship between the manufacturer and new retailer has changed from cooperation under mode RR to competition and cooperation under mode PR, the manufacturer still favors two-channel sales over single-channel sales and affects consumer channel choices to adjust market shares through mode selection. Second, regarding the impacts of the key parameters on the manufacturer, downstream e-commerce platform retailers and environment are intricate and nuanced. While raising the omni-channel service level enhances profitability in the new retailer across both modes, its environmental impacts differ significantly between them. Additionally, it can harm the online platform in some cases. Nevertheless, when the parameters fall within suitable ranges, the manufacturer and both downstream retailers have a consistent preference for improved omni-channel services under both modes. Finally, there is a significant divergence in mode preferences among the manufacturer and both downstream platform retailers. Due to the first-mover advantage, the manufacturer opts for mode RR over mode PR in most cases. Notably, within a specific range of parameters, they consistently prefer mode RR, which also proves beneficial for the environment, resulting in a Pareto optimal outcome. This proposes a concrete cooperation mechanism among the manufacturer, retailers, and consumers from quantitative insights, which can promote green products to achieve the objective of low-carbon environmental protection. Full article

Introduction: 3D-printing is an emerging technology in the field of prosthetics, offering advantages such as cost-effectiveness, ease of customization, and improved accessibility. While previous reviews have focused on limited aspects, the aim of this systematic review is to provide a comprehensive evaluation of the clinical outcomes of 3D-printed prostheses for both upper and lower limbs. Methods: A search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across six databases (PubMed, Web of Science, EBSCO, Scopus, Cochrane Library, and Sage). Studies on 3D-printed prostheses in human rehabilitation that focused on the clinical outcomes of the device were included, while studies lacking clinical data, 3D printing details, or focusing on traditional manufacturing methods were excluded. Finally, the risk of bias was assessed using the modified Downs & Black Checklist. Results: A total of 1420 studies were identified, with 11 meeting the inclusion criteria. The included studies assessed different 3D-printed prosthetic types and upper and lower limb prostheses. The main clinical outcomes analyzed were functional performance, design and material integrity, and overall effectiveness of 3D-printed prostheses. Studies on upper limb prostheses reported improved dexterity, range of motion (ROM), and user satisfaction, despite some durability limitations. Lower limb prostheses showed enhancements in comfort, gait parameters, and customization, particularly in amphibious and partial foot designs. Conclusions: 3D-printed prostheses show potential to improve functional performance, patient satisfaction, fit, and implementation feasibility compared to conventional methods. However, limitations such as small sample sizes, variability in assessment tools, and limited high-quality evidence highlight the need for further research to support broader clinical adoption. Full article

Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

Lignocellulosic agro-forestry residues (LARs), such as rice straw, sugarcane bagasse, and wood wastes, are abundant and low-cost feedstocks for polyhydroxyalkanoate (PHA) bioplastics. However, their complex cellulose–hemicellulose–lignin matrix requires integrated valorization strategies. This review presents a dual-framework approach: “pretreatment–co-substrate compatibility” and “pretreatment–microbial platform matching”, to align advanced pretreatment methods (including deacetylation–microwave integration, deep eutectic solvents, and non-sterilized lignin recovery) with engineered or extremophilic microbial hosts. A “metabolic interaction” perspective on co-substrate fermentation, encompassing dynamic carbon flux allocation, synthetic consortia cooperation, and one-pot process coupling, is used to elevate PHA titers and tailor copolymer composition. In addition, we synthesize comprehensive kinetic analyses from the literature that elucidate microbial growth, substrate consumption, and dynamic carbon flux allocation under feast–famine conditions, thereby informing process optimization and scalability. Microbial platforms are reclassified as broad-substrate, process-compatible, or product-customized categories to emphasize adaptive evolution, CRISPR-guided precision design, and consortia engineering. Finally, next-generation techno-economic analyses, embracing multi-product integration, regional adaptation, and carbon-efficiency metrics, are surveyed to chart viable paths for scaling LAR-to-PHA into circular bioeconomy manufacturing. Full article

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making. Full article

Furniture is an integral part of daily life. Its comfort and usability are key factors that define its success. In recent years, there has been increasing demand for applications that drive businesses toward Industry 4.0. These applications aim to improve productivity through greater automation in both 3D modeling and fabrication processes. This research aims to develop a Computer Aided Design (CAD) platform that automates the design and manufacturing of furniture. The platform is based on visual programming using Grasshopper 3D™ and provides a solid foundation for processing different geometric shapes. These shapes can be customized according to the user’s preferences. The platform’s innovation lies in its ability to process complex geometries with a fully automated algorithm. Once the initial parameters are set, the algorithm generates the results. The input data includes an initial geometry, which can be highly complex. Additionally, a set of construction parameters is introduced, leading to multiple alternative design solutions based on the same initial geometry. The designer and user can select their final choice, and all resulting design and manufacturing outcomes are automatically generated. These outcomes include 3D part models, 3D assembly files, Bill of Materials, G-code for CNC machining, and nesting capabilities for improved material efficiency. The platform ensures high-quality performance. The results of the study show that the platform successfully works with different geometries. Moreover, the study is significant as the Industry 4.0 transformation moves toward more automated design processes. Full article