Article
Engineering, Industrial
Tobias Moench, Arnd Huchzermeier, Peter Bebersdorf
Summary: In today's fast-paced, high customization-demanding era, adopting a highly flexible assembly line can give companies a competitive edge. By aligning the assembly pace with the desired level of output, the optimal takt time can be determined to reduce the complexity of the mixed-model assembly line balancing problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Multidisciplinary Sciences
Ryan L. Clark, Bryce M. Connors, David M. Stevenson, Susan E. Hromada, Joshua J. Hamilton, Daniel Amador-Noguez, Ophelia S. Venturelli
Summary: The study uses a model-guided approach to design diverse synthetic human gut communities for the production of the health-relevant metabolite butyrate. The model accurately predicts community assembly and metabolic functions.
NATURE COMMUNICATIONS
(2021)
Article
Plant Sciences
Sergey Rosbakh, Loic Chalmandrier, Shyam Phartyal, Peter Poschlod
Summary: This study analyzes 16 traits of 167 species to reveal the functional structure of plant communities and the impact of environmental factors on these structures. The research demonstrates that seed traits are independent of other plant traits and can be affected differently by community assembly rules. Abiotic filtering mainly affects vegetative traits, while biotic interactions and dispersal affect germination and seed morphological traits.
JOURNAL OF ECOLOGY
(2022)
Article
Engineering, Industrial
Xuemei Liu, Xiaolang Yang, Mingliang Lei
Summary: This study utilized uncertainty theory and complexity theory to consider uncertain demand in mixed-model assembly line balancing. By introducing scenario probability and triangular fuzzy number to describe uncertain demand, and measuring station complexity based on information entropy and fuzzy entropy, a new optimization model was established. An improved genetic algorithm was applied to solve the model, and the effectiveness of the model was verified on instances of mixed-model assembly line for automobile engines.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Beixin Xia, Mingyue Zhang, Yan Gao, Jing Yang, Yunfang Peng
Summary: This paper focuses on the routing, scheduling, and loading problems of a single towed train that transports parts from one supermarket to the workstation buffer in a mixed-flow assembly line. It aims to optimize the loading, delivery schedule and route, and departure time to minimize shipping and line inventory costs. A novel artificial immune genetic algorithm-based heuristic is proposed to provide reasonable solutions.
Article
Engineering, Industrial
Dian Huang, Zhaofang Mao, Kan Fang, Biao Yuan
Summary: The study addresses a mixed-model two-sided assembly line balancing problem, aiming to minimize the number of mated-stations while also considering the total number of operators. An exact algorithm based on combinatorial Benders decomposition is proposed, along with a sequence-based enumerative search method to calculate effective combinatorial Benders cuts. Extensive computational experiments demonstrate the efficiency of the proposed solution in finding exact solutions even for large-sized instances.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Mathematics, Applied
Yuchen Li, Dan Liu, Ibrahim Kucukkoc
Summary: This paper studies the mixed-model assembly line balancing problem, considering the impact of learning effect and uncertain demand on the level of production. A novel model is proposed to optimize the total expected cost and average cycle time, and two algorithms are proposed to solve the model under different system response time requirements.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Industrial
Binghai Zhou, Zhaoxu He
Summary: This paper focuses on a sustainable material handling scheduling problem in the automobile industry, and proposes a novel Hybrid-load Automated Guided Vehicle (H-AGV) to minimize inventory and energy consumption. A Deep Q network and Non-dominated sorting-based Hyper-Heuristic (DN-HH) algorithm is used to solve the bi-objective scheduling problem and outperforms other algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Hui Zhang, Xiyang Li, Za Kan, Xiaohai Zhang, Zhiyong Li
Summary: This paper proposed a method combining improved genetic algorithm (GA) and Flexsim software to reduce production auxiliary time and improve the efficiency of existing mixed-flow assembly line. By establishing a multi-objective mathematical model and developing a solution model using multi-objective GA, the method effectively reduced idle and overload of the assembly line while rationalizing transportation resources and decreasing line inventory accumulation. The originality lies in the first intuitive and efficient use of the combination of improved GA and Flexsim software to study production line balance and just-in-time feeding of parts.
ASSEMBLY AUTOMATION
(2021)
Article
Computer Science, Artificial Intelligence
Yong Xie, Hongwei Wang, Gang Liu, Hui Lu
Summary: This study proposes a JIT precast production scheduling model for the construction of steel box girders in the HZM bridge project, aiming to minimize total early/tardy costs through job batching algorithm and optimal shifting algorithm. The proposed algorithm shows better performance and efficiency in real-world case studies compared to empirical rules in current engineering practice.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Biology
Diana P. Lopez, Amy L. Freestone
Summary: Biotic interactions in assembly processes vary across time and latitude, affecting trait assembly in marine assemblages. Competition is stronger in later stages and predation is more intense at lower latitudes. Different regions show different patterns of trait assembly, with trait divergence occurring in late assembly at higher latitudes and relaxed predation causing trait divergence in tropical regions.
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2022)
Article
Management
Nico Andre Schmid, Veronique Limere, Birger Raa
Summary: This study proposes a new mathematical programming model to address the issue of feeding parts in assembly systems, investigating the selection of feeding policies and space allocation at assembly stations. Key findings include the factors influencing these decisions and overall costs.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Engineering, Manufacturing
Helmut A. Sedding
Summary: Car mass production often involves a moving assembly line with multiple car models. Due to limited space, material supplies are placed away from ideal positions, requiring non-productive walking time to retrieve them. To optimize production planning, a time-dependent V-shaped function is used to estimate walking time, which shows significant differences compared to constant estimates. An NP-hard sequencing problem is solved with a model to minimize walking time by optimizing material positions.
JOURNAL OF SCHEDULING
(2023)
Article
Engineering, Industrial
Kang Wang, Qianqian Han, Zhenping Li
Summary: This research investigates the mixed-model assembly line balancing problem in multi-demand scenarios and proposes a solution through a phased heuristic algorithm. The results show that considering demand fluctuations can improve workstation load balance and assembly line production efficiency.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2023)
Article
Engineering, Manufacturing
Yilmaz Delice, Emel Kizilkaya Aydogan, Salih Himmetoglu, Ugur Ozcan
Summary: In the automotive industry, supermarkets are decentralized in-house logistic areas used for parts feeding to mixed-model assembly lines. This study simultaneously considers the mixed-model assembly line balancing problem and supermarket location problem in order to minimize the total costs. A mathematical model is developed and solved using constraint programming, and an approach based on Ant Colony Optimization and Simulated Annealing is presented for large-sized problems. The proposed approach effectively reduces total costs and achieves a more realistic and applicable structure.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2023)
Article
Operations Research & Management Science
Seyed Shamsodin Hosseini, Parham Azimi, Mani Sharifi, Mostafa Zandieh
Summary: This paper addresses the dynamic facility layout problem by proposing a new mathematical model to simultaneously determine the best machine positions and plan transportation operations. A hybrid meta-heuristic approach, combining modified genetic algorithm and cloud-based simulated annealing algorithm, is developed to solve the model effectively. The proposed methodology is compared with two meta-heuristics on a set of test problems.
RAIRO-OPERATIONS RESEARCH
(2021)
Article
Operations Research & Management Science
Nassibeh Janatyan, Mostafa Zandieh, Akbar Alem-Tabriz, Masood Rabieh
Summary: The study addresses uncertainties and sustainability in pharmaceutical distribution networks using a new model and optimal algorithms, improving environmental, social, and economic conditions. Adjusting algorithm parameters, the MOPSO algorithm demonstrated the best performance in achieving optimal Pareto approximation.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Automation & Control Systems
Ashkan Ayough, Farbod Farhadi, Mostafa Zandieh
Summary: This paper explores the role of job rotation in lean manufacturing, proposing an optimization model that considers human behavior parameters and conducting sensitivity analysis of input parameters. The findings suggest that job rotation schedules and human cognitive metrics impact the performance of lean manufacturing, and general rules are derived. Additionally, the authors use response surface methodology for experimental design, revealing important information about the impact of job rotations on operator performance and the overall working cell.
ASSEMBLY AUTOMATION
(2021)
Article
Business
Ehsan Khanmohammadi, Hossein Safari, Mostafa Zandieh, Behnam Malmir, Erfan Babaee Tirkolaee
Summary: This article introduces an integrated framework using balanced scorecard, system dynamics simulation, case-based reasoning method, and adaptive neuro-fuzzy inference system model to help strategy managers determine an organization's strategy. A real-world case study was conducted to validate the methodology's applicability and yielded appropriate strategies in line with managers' objectives.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2022)
Article
Engineering, Industrial
Hamed Jafar-Zanjani, Mostafa Zandieh, Mani Sharifi
Summary: The study discusses the importance of organizations shifting from centralized to decentralized structures and developing multi-factor production networks in the global business market. By proposing a bi-objective optimization model and utilizing robust programming and heuristic methods for maintenance planning and scheduling, as well as resilience strategies for network disruptions, the uncertainty of input parameters is effectively addressed.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Mathematics, Applied
Mahnaz Sohrabi, Mostafa Zandieh, Behrouz Afshar-Nadjafi
Summary: This paper investigates health-economic objectives of a medical system to manage processed corneal tissue inventory. It proposes a practical multi-objective mixed-integer linear programming model and validates its applicability through sensitivity analyses of critical parameters. The results show that the proposed model efficiently handles the real case study.
COMPUTATIONAL & APPLIED MATHEMATICS
(2021)
Article
Environmental Sciences
Ali Sibevei, Adel Azar, Mostafa Zandieh, Seyed Mohammad Khalili, Maziar Yazdani
Summary: The study found that by using the newly proposed approach, supply chain risks could be assessed more effectively, especially when the number of risks is large. Resolving the root risks of the blood supply chain frequently requires management skills. This paper proposes a new systemic approach that offers a fresh perspective on supply chain risk management.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Engineering, Industrial
Mohammad Ali Nikouei, Mostafa Zandieh, Maghsoud Amiri
Summary: This paper incorporates preventive maintenance activities into the two-stage assembly flow-shop scheduling problem and proposes three maintenance policies. Two hybrid optimization methods are used to find proper job sequencing, with variable neighborhood search with simulated annealing algorithm showing superior solution quality and computational time.
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING
(2022)
Article
Management
Mohsen Abdoli, Mostafa Zandieh, Sajjad Shokouhyar
Summary: This study determines the optimal queuing system capacity by analyzing the properties of the queuing system and appointment window, aiming to minimize the total costs. The findings can guide the management decisions of both public and private healthcare centers.
JOURNAL OF MODELLING IN MANAGEMENT
(2023)
Article
Engineering, Industrial
Vahid Kayvanfar, M. Zandieh, Mehrdad Arashpour
Summary: This research investigates the economic lot scheduling problem and proposes a hybrid algorithm that outperforms other algorithms in terms of solution quality and diversity.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2022)
Article
Engineering, Industrial
Farzaneh Nazarizadeh, Akbar Alemtabriz, Mostafa Zandieh, Abbas Raad
Summary: This paper proposes an analytical model to estimate the dependent failure rate for the Iranian railway system, aiming to accurately assess and predict the reliability of the whole system by considering both common cause failure and interactive failure. The model uses a two-variable Taylor expansion approach to estimate the dependent failure rate and determines the coefficients by expert estimation method. The research results show an improvement in the accuracy of reliability prediction compared to other models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Economics
Mahnaz Sohrabi, Mostafa Zandieh, Mohammad Shokouhifar
Summary: This study examines the challenges of healthcare systems in achieving sustainable inventory management of blood products. The study aims to promote social equity in healthcare provision, optimize cost management, and minimize environmental pollution. A demand-driven multi-objective inventory model is proposed, utilizing hybrid policies in an uncertain environment. The model considers different types of demands, applies a robust fuzzy stochastic programming approach, and implements a combined metaheuristic technique for solution finding. The results demonstrate the superior performance of the proposed model in minimizing costs, reducing shortages and wastage, and addressing health equity and emergencies.
SOCIO-ECONOMIC PLANNING SCIENCES
(2023)
Article
Computer Science, Information Systems
Amin Rahimi, Seyed Mojtaba Hejazi, Mostafa Zandieh, Mirpouya Mirmozaffari
Summary: This paper proposes a surgical case scheduling problem that assigns n surgeries to m identical operating rooms or machines. Since optimization problems in operating rooms are NP-hard, mathematical and metaheuristic methods are used. The ordering of surgical operations in each room is a crucial part of sequencing and scheduling problems. The study introduces a no-wait open-shop surgical case scheduling problem with multi-transportation times and develops a mixed-integer linear program (MILP) to solve small-sized instances. Moreover, a hybrid simulated annealing (SA) algorithm is suggested for solving large-sized problems in an acceptable computational time.
APPLIED SYSTEM INNOVATION
(2023)
Article
Engineering, Multidisciplinary
H. Jafar-Zanjani, M. Zandieh, M. Khalilzadeh
Summary: This study focuses on scheduling periodic services for customers in different locations with different service needs, presenting a novel mixed integer linear programming model with augmented epsilon constraint method. Additionally, a bi-objective meta-heuristic technique based on genetic algorithm is proposed for solving large-scale problems.
Article
Engineering, Multidisciplinary
Ashkan Ayough, Farbod Farhadi, Mostafa Zandieh, Parisa Rastkhadiv
Summary: This paper presents a metaheuristic approach to solve a customer priority based location-allocation problem, considering obstacles and location-dependent supplier capacities in network design. The computational findings show that the best allocation strategies derived from the proposed algorithms can effectively address issues in disaster relief and healthcare management.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2021)
Article
Engineering, Industrial
Xiaoliang Yan, Reed Williams, Elena Arvanitis, Shreyes Melkote
Summary: This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
Article
Engineering, Industrial
Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Guangrui Wen, Wei Cheng, Xuefeng Chen
Summary: In this study, a deep learning framework that combines interpretability and feature fusion is proposed for real-time monitoring of pipeline leaks. The proposed method extracts abstract feature details of leak acoustic emission signals through multi-level dynamic receptive fields and optimizes the learning process of the network using a feature fusion module. Experimental results show that the proposed method can effectively extract distinguishing features of leak acoustic emission signals, achieving higher recognition accuracy compared to typical deep learning methods. Additionally, feature map visualization demonstrates the physical interpretability of the proposed method in abstract feature extraction.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)