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18 pages, 2633 KB

Open AccessArticle

Decision-Making Tools for Large Vessel Collisions with Marine Megafauna Species: Research Gaps and Proposed Application

by Nikolaos Simantiris, Kostas Poirazidis and Katerina Kabassi

Appl. Sci. 2026, 16(2), 1065; https://doi.org/10.3390/app16021065 - 20 Jan 2026

Marine traffic poses a significantly increasing threat to the marine environment, especially marine megafauna species, due to collisions between large vessels and marine organisms that most frequently result in mortality. The adoption of mitigation methods for collisions is critical to avoid population declines. [...] Read more.

Marine traffic poses a significantly increasing threat to the marine environment, especially marine megafauna species, due to collisions between large vessels and marine organisms that most frequently result in mortality. The adoption of mitigation methods for collisions is critical to avoid population declines. Selecting the optimal mitigation method depends on a set of criteria and is best assessed using decision-making tools. The current study reviewed the use of decision-making tools for marine traffic applications and discusses the existing gap regarding environmental applications (especially considering the impact on marine biodiversity). Furthermore, the authors propose a method for estimating hotspots of marine traffic that may overlap with marine biodiversity foraging grounds, and the structure for a decision-making tool for mitigating collisions and conserving the marine environment. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

24 pages, 22710 KB

Open AccessArticle

A Multi-Constraint Point Cloud Registration Method for Machining Error Measurement of Thin-Walled Parts

by Fengyun Huang, Chenxi Shen, Dehao Fang and Jun Xiao

Appl. Sci. 2026, 16(2), 1003; https://doi.org/10.3390/app16021003 - 19 Jan 2026

Abstract

Thin-walled parts are widely used in the automotive manufacturing industry due to their lightweight characteristics and high structural efficiency. However, it is difficult to accurately measure machining errors in key regions due to the feature deformation. To improve the online measurement accuracy of [...] Read more.

Thin-walled parts are widely used in the automotive manufacturing industry due to their lightweight characteristics and high structural efficiency. However, it is difficult to accurately measure machining errors in key regions due to the feature deformation. To improve the online measurement accuracy of complex thin-walled parts, a machining error measurement approach based on multi-constraint point cloud registration is proposed. To address the low overlap and complex geometric features among multi-segment measured point clouds, a point cloud stitching method based on hole boundary features is developed to acquire complete measured point clouds. Meanwhile, a point cloud surface extraction method based on normal neighborhood searching is developed to acquire model point clouds. Since different regions of thin-walled parts require different geometric tolerances, a registration model integrating multiple locating and assembly constraints is proposed to satisfy the requirements for optimal point cloud registration. A measurement system composed of a line-structured light sensor and a six-axis robotic arm is developed to validate the proposed method. Experimental results show that the proposed approach reduces the overall dimensional error of point cloud stitching by approximately 70–86% and decreases the point number deviation between upper and lower surfaces by more than 98%. Furthermore, the measurement accuracy in locating holes and key assembly regions is improved to 0.05 mm and 2 mm, representing improvements of approximately 96.3% and 23.9% compared with registration methods without multi-constraint conditions, and approximately 95.3% and 14.5% compared with commonly used multi-constraint registration methods. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

23 pages, 2622 KB

Open AccessArticle

Score-Based Dispatching Strategy for Twin Rubber-Tired Gantry Cranes Leveraging Spring Elasticity

by Dokyung Kim and Junjae Chae

Appl. Sci. 2026, 16(1), 463; https://doi.org/10.3390/app16010463 - 1 Jan 2026

Viewed by 204

Abstract

Yard crane (YC) operations are critical to the overall productivity of container terminals, especially as terminals move toward higher levels of automation. This study proposes a score-based dispatching strategy for twin RTGCs operating within a single yard block. The proposed logic evaluates each [...] Read more.

Yard crane (YC) operations are critical to the overall productivity of container terminals, especially as terminals move toward higher levels of automation. This study proposes a score-based dispatching strategy for twin RTGCs operating within a single yard block. The proposed logic evaluates each job using four factors—distance between crane and job, job waiting time, estimated processing time, and an elasticity term inspired by spring mechanics that reflects the tendency of each crane to stay within its preferred working zone. These factors are normalized and combined into a single score, and the corresponding weights are optimized by a genetic algorithm (GA). Jobs with lower scores are given higher priority for assignment. A discrete-event simulation model of a twin RTGC system is developed using AutoMod^® to assess the performance of the proposed strategy. The score-based rule is compared with conventional dispatching policies such as First-Come-First-Served (FCFS), Nearest-First-Served (NFS), and their weighted combination under various workload scenarios. Relative to the score-based strategy without elasticity, the inclusion of the elasticity term reduces average and maximum truck turnaround time by 7.51% and 7.79%, respectively; these improvements translate into higher yard throughput and strengthen the advantage over the benchmark dispatching rules. In particular, the elasticity term effectively mitigates crane interference while maintaining a balanced spatial distribution of work between the two cranes. These findings indicate that the proposed dispatching logic provides a practical and implementable control strategy for retrofitting existing RTGC systems and integrating them into terminal operating systems. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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29 pages, 2210 KB

Open AccessArticle

Bi-Level Collaborative Optimization for Medical Consumable Order Splitting and Reorganization Considering Multi-Dimensional and Multi-Scale Characteristics

by Peng Jiang, Shunsheng Guo and Xu Luo

Appl. Sci. 2025, 15(14), 7627; https://doi.org/10.3390/app15147627 - 8 Jul 2025

Cited by 1 | Viewed by 791

Abstract

Medical consumable orders are characterized by diverse product types, small batch sizes, frequent orders, and high customization requirements, often leading to inefficient workshop scheduling and difficulties in meeting multiple production constraints. To address these challenges, this study proposes a bi-level optimization model for [...] Read more.

Medical consumable orders are characterized by diverse product types, small batch sizes, frequent orders, and high customization requirements, often leading to inefficient workshop scheduling and difficulties in meeting multiple production constraints. To address these challenges, this study proposes a bi-level optimization model for order splitting and reorganization considering multi-dimensional and multi-scale characteristics. The multi-dimensional characteristics encompass materials, processes, equipment, and work efficiency, while the multi-scale aspects involve finished products, components, assemblies, and parts. At the upper level, the model optimizes order task splitting by refining splitting strategies and preprocessing constraints to generate high-quality input for the reorganization phase. The lower level optimizes sub-task prioritization, batch sizes, and resource scheduling to develop a production plan that balances cost and efficiency. Subsequently, to solve this bi-level optimization problem, a hybrid bi-objective optimization algorithm is designed, integrating a collaborative iterative strategy to enhance solution efficiency and quality. Finally, a case study and comparative experiments validate the practicality and effectiveness of the proposed model and algorithm. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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27 pages, 15483 KB

Open AccessArticle

Online Three-Dimensional Fuzzy Multi-Output Support Vector Regression Learning Modeling for Complex Distributed Parameter Systems

by Gang Zhou, Xianxia Zhang, Hanyu Yuan and Bing Wang

Appl. Sci. 2025, 15(5), 2750; https://doi.org/10.3390/app15052750 - 4 Mar 2025

Cited by 1 | Viewed by 1059

Abstract

Complex distributed parameter systems (DPSs) are prevalent in numerous industrial processes. However, the nonlinear spatiotemporal dynamics inherent in DPS present significant challenges for accurate modeling. In this paper, an innovative online three-dimensional (3D) fuzzy multi-output support vector regression learning method is proposed for [...] Read more.

Complex distributed parameter systems (DPSs) are prevalent in numerous industrial processes. However, the nonlinear spatiotemporal dynamics inherent in DPS present significant challenges for accurate modeling. In this paper, an innovative online three-dimensional (3D) fuzzy multi-output support vector regression learning method is proposed for DPS modeling. The proposed method employs spatial fuzzy basis functions from the 3D fuzzy model as kernel functions, enabling direct construction of a comprehensive fuzzy rule base. Parameters

C

and

ε

in the 3D fuzzy model adaptively adjust according to data sequence variations, effectively responding to system dynamics. Furthermore, a stochastic gradient descent algorithm has been implemented for real-time updating of learning parameters and bias terms. The proposed method was validated through two typical DPS and an actual rotary hearth furnace industrial system. The experimental results show the effectiveness of the proposed modeling method. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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16 pages, 448 KB

Open AccessArticle

Evaluating the Impact of Membership Functions and Defuzzification Methods in a Fuzzy System: Case of Air Quality Levels

by Juan Fernando Lima, Andrés Patiño-León, Marcos Orellana and Jorge Luis Zambrano-Martinez

Appl. Sci. 2025, 15(4), 1934; https://doi.org/10.3390/app15041934 - 13 Feb 2025

Cited by 15 | Viewed by 4332

Abstract

Since the 1960s, fuzzy logic has contributed to developing control systems based on modeling nonlinear problems using linguistic terms and inference rules. In the air quality domain, fuzzy logic has allowed us to tackle inferential environmental systems that are tolerant of human uncertainty [...] Read more.

Since the 1960s, fuzzy logic has contributed to developing control systems based on modeling nonlinear problems using linguistic terms and inference rules. In the air quality domain, fuzzy logic has allowed us to tackle inferential environmental systems that are tolerant of human uncertainty and aimed at decision support. These systems are composed of three processes: a function to define a membership degree of the system’s value concerning a human linguistic term; an inference engine for decision making; and defuzzification methods focused on transforming the aggregated fuzzy set into a real-world value. Over the years, multiple mathematical formulas have been proposed to enrich membership functions or defuzzification methods; however, their use is sometimes limited to classical functions, limiting the importance of other proposals. This paper aims to evaluate the impact of the transformation functions in an air quality fuzzy system. The results of this work prove that the defuzzification method has a more significant effect than the others. It should be noted that by considering these results or their evaluation method, the quality of future fuzzy systems can be improved in both industrial and academic domains. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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23 pages, 1357 KB

Open AccessArticle

Three-Dimensional Fuzzy Modeling for Nonlinear Distributed Parameter Systems Using Simultaneous Perturbation Stochastic Approximation

by Xianxia Zhang, Tangchen Wang, Chong Cheng and Shaopu Wang

Appl. Sci. 2024, 14(17), 7860; https://doi.org/10.3390/app14177860 - 4 Sep 2024

Cited by 2 | Viewed by 1660

Abstract

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents [...] Read more.

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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22 pages, 12215 KB

Open AccessArticle

An AI-Powered Product Identity Form Design Method Based on Shape Grammar and Kansei Engineering: Integrating Midjourney and Grey-AHP-QFD

by Chenlu Wang, Jie Zhang, Dashuai Liu, Yuchao Cai and Quan Gu

Appl. Sci. 2024, 14(17), 7444; https://doi.org/10.3390/app14177444 - 23 Aug 2024

Cited by 19 | Viewed by 3836

Abstract

Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&D). The [...] Read more.

Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&D). The design of new product forms must ensure the continuity of PI and concurrently address the emotional needs of users. It demands a high level of experience from designers and significant investment in R&D. To solve this problem, a generative and quantitative design method powered by AI, based on Shape Grammar (SG) and Kansei Engineering (KE), is proposed. The specific method is as follows: Firstly, representative products for Morphological Analysis (MA) are selected, SG is applied to establish initial shapes and transformation rules, and prompts are input into Midjourney. This process generates conceptual sketches and iteratively refines them, resulting in a set of conceptual sketches that preserve the PI. Secondly, a web crawler mines online reviews to extract Kansei words. Factor Analysis (FA) clusters them into Kansei factors, and the Grey Analytic Hierarchy Process (G-AHP) calculates their grey weights. Thirdly, after analyzing the PI conceptual sketches for feature extraction, the features are integrated with CRs into the Quality Function Deployment (QFD) matrix. Experts evaluate the relationships using interval grey numbers, calculating the optimal ranking of PI Engineering Characteristics (PIECs). Finally, professional designers refine the selected sketches into 3D models and detailed designs. Using a Chinese brand as a case study, we have designed a female electric moped (E-moped) to fit the PI and users’ emotional needs. Through a questionnaire survey on the design scheme, we argue that the proposed innovative method is efficient, applicable, and effective in balancing the product form design of PI and user emotions. Full article

(This article belongs to the Special Issue Fuzzy Control Systems and Decision-Making)

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