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
Computer Science, Interdisciplinary Applications
S. Li, J. Butterfield, A. Murphy
Summary: The aim of this work is to develop a self-adapting digital toolset for manufacturing planning that focuses on minimally constrained assembly line balancing. The approach involves determining the optimal number of workstations, cycle time, and task assignments through a bespoke genetic algorithm. The proposed algorithm consistently outperforms previous studies in terms of convergence time and solution quality, delivering detailed production plans for the simple assembly line balancing problem with minimal inputs.
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
(2023)
Review
Engineering, Manufacturing
Maximilian Johannes Schlueter, Frederik Ferid Ostermeier
Summary: Dynamic line balancing is a promising approach to achieve both equal loads for operators and high throughput for manufacturers. Tasks can be shared between adjacent stations in addition to the fixed allocated tasks.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Multidisciplinary
Nessren Zamzam, Amin K. El-Kharbotly, Yomna Sadek
Summary: This study focuses on balancing the physical effort among workers in two-sided assembly lines commonly used for heavy industries. A genetic algorithm and a new decoding scheme are proposed, along with the introduction of an effort smoothness index (ESI). Results show that balancing physical effort is influential and achievable at no extra cost for most cases, but harder to achieve in difficult problems of high strength order.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Computer Science, Interdisciplinary Applications
Thiago Cantos Lopes, Adalberto Sato Michels, Celso Gustavo Stall Sikora, Nadia Brauner, Leandro Magatao
Summary: This study introduces an economically robust solution to the assembly line balancing problem by designing assembly lines that allow flexible alternation between cycle times in response to demand fluctuations. A mixed-integer linear programming model is used to describe the problem, with a heuristic procedure implemented to quickly generate high-quality solutions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Automation & Control Systems
Hongrui Gao, Yingwei Zhang, Zhuming Bi
Summary: In this article, the genetic transfer learning (GTL) method is proposed to transfer knowledge of sustainable assembly manufacturing systems. Existing methods suffer from limitations such as tedious trial-and-error processes, no utilization of existing solutions, and no consideration of new constraints. To address these problems, GTL is used to migrate knowledge and adapt to new constraints. The contributions include transfer pretreatment, defining system similarity, and determining transfer strategy. A case study shows that transfer learning improves assembly line efficiency and sustainability.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Eduardo Alvarez-Miranda, Jordi Pereira, Mariona Vila
Summary: The simple assembly line balancing problem (SALBP) involves assigning assembly operations to workstations to optimize efficiency. It extends the bin packing problem (BPP) by considering precedence relations between items. Precedence constraints affect solution methods, but previous studies found they are not crucial for optimal solutions.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Yuling Jiao, Yang Wang, Xue Deng, Xinyue Su, Lujiao Huang
Summary: In response to the increasing demand for individualized products in the market, a novel solution method for enhancing the efficiency and intelligence level of assembly lines is proposed. The method utilizes a mathematical model and an improved ant colony algorithm to solve the mixed-model parallel two-sided assembly lines balancing problem, and its effectiveness is validated through comparisons with other algorithms.
ENGINEERING OPTIMIZATION
(2023)
Article
Engineering, Industrial
Eduardo Alvarez-Miranda, Jordi Pereira, Camila Vargas, Mariona Vila
Summary: This study proposes a new local search algorithm for solving simple assembly line balancing problems, which improves the best known solution and is comparable to existing methods. The algorithm explores the solution space using variable-length sequences. Additionally, the characteristics of instances where the algorithm outperforms previous construction procedures are investigated.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Celso Gustavo Stall Sikora
Summary: This paper presents an exact procedure for solving the integrated balancing and sequencing problem in mixed-model assembly lines with stochastic demand. The solution must be flexible to cope with different demand scenarios, and the integration of strategic balancing with operational sequencing leads to more robust assembly lines.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Management
Rico Walter, Philipp Schulze, Armin Scholl
Summary: This study tackles a simple assembly line balancing problem with a focus on a smoothness index SX, and optimizes it through a branch-and-bound procedure, outperforming other methods in comprehensive computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Thiago Cantos Lopes, Adalberto Sato Michels, Nadia Brauner, Leandro Magatao
Summary: This paper focuses on optimizing Mixed-model assembly lines with continuous paced line control and proposes a criterion-space method for defining the Pareto front. Comparing the Pareto fronts between cycle time and line length for paced and unpaced lines allows meaningful comparisons between line controls. An industrial case study suggests that paced lines are more efficient than unpaced lines for lower cycle time ranges.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
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
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
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
Wouter Lefever, Faycal A. Touzout, Khaled Hadj-Hamou, El-Houssaine Aghezzaf
Summary: This paper discusses the time-constrained inventory routing problem (TCIRP) on a network with uncertain arc travel times, proposing a robust optimization approach with a controlled level of conservatism and developing a Benders' decomposition-based heuristic to cope with the resulting robust counterpart's complexity. The proposed method is compared with two standard approaches and shown to find robust solutions that are not too conservative in reasonable time.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Management
Mathieu Vandenberghe, Stijn De Vuyst, El-Houssaine Aghezzaf, Herwig Bruneel
Summary: Anticipating the impact of urgent emergency arrivals on operating room schedules is challenging. This study introduces a model for surgery scheduling considering stochastic surgery durations and emergency patient arrivals. Detailed analysis of emergency break-ins can lead to lower total cost, and an efficient heuristic is proposed to estimate the solution value with less computational effort.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
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
Management
Ebenezer Olatunde Adenipekun, Veronique Limere, Nico Andre Schmid
Summary: In the era of mass customisation, supplying parts to mixed-model assembly lines is a complex task. The supply must avoid excessive logistical handling activities while managing line space carefully to prevent shortages that could lead to line stoppage. By using a mixed integer programming model to assign parts and vehicle types simultaneously, total feeding costs can be minimized.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2022)
Article
Engineering, Industrial
Ryan O'Neil, Claver Diallo, Abdelhakim Khatab, El-Houssain Aghezzaf
Summary: This paper proposes a solution method for the multimission selective maintenance problem by combining column-generation and genetic algorithms. By integrating the genetic algorithm within the classical column-generation framework, high-quality solutions can be quickly obtained. The proposed method performs well in solving large-scale systems.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Muhammad Saeed, Thibaut Demasure, Steven Hoedt, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: An execution time estimation model is proposed to accurately estimate the execution time in a robotic assembly workcell. The model utilizes trajectory generation and task specifications to estimate the task time with reasonable accuracy. Possible directions to further improve the estimation accuracy are discussed.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
Lauren Van De Ginste, Alexander De Cock, Axl Van Alboom, Stijn Huysentruyt, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: This paper presents a multidimensional formal skill model that can be used to describe the needs and capacities in an assembly system, connecting resources with processes and products. It discusses the evaluation of a reconfigurable assembly system and highlights the benefits of a skill-based modeling approach.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Matthias Schamp, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: This paper proposes a workflow that utilizes a 3D digital model to provide additional support to automation engineers, allowing for earlier verification of control logic and reducing real commissioning time. The approach records all occurring states and transitions, visualizes the state graph, and highlights unexpected behavior. Validation on a test case confirms the effectiveness of the proposed approach, with future research aimed at validation on real industrial cases.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2023)
Article
Engineering, Industrial
Jaakko Peltokorpi, Steven Hoedt, Thomas Colman, Kim Rutten, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: Cognitive assistance systems help individuals with learning disabilities to improve their skills and increase their employment opportunities. This study focused on the impact of different forms of instruction and types of disability in a manual assembly task. The results showed that projection instructions enhanced the initial assembly cycle and challenging operators with filtered content improved their independence and task understanding. However, adaptive instructions posed barriers for operators who relied heavily on mentor support. The form of instruction should be carefully considered based on each operator's adaptation and needs. These findings have implications for the human-centric and socially sustainable production agenda of Industry 5.0 and highlight future research priorities.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Alp Darendeliler, Dieter Claeys, El-Houssaine Aghezzaf
Summary: This paper tackles the problem of integrated lot-sizing and maintenance decision making in the context of multiple products and stochastic demand. The authors propose a Markov decision process formulation and employ the classic Q-learning algorithm along with a decomposition-based approximate Q-value heuristic to find near-optimal solutions in an efficient manner. To speed up convergence, they introduce a hybrid Q-learning method that initializes Q-values using the output of the approximate Q-value heuristic. The numerical experiments reveal the superiority of the hybrid method and the scalability limitations of tabular methods, leading to the development of three state aggregation schemes that significantly reduce computational time while maintaining good performance.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Article
Engineering, Multidisciplinary
Alp Darendeliler, Dieter Claeys, El-Houssaine Aghezzaf
Summary: This paper investigates the problem of integrated lot-sizing and maintenance decision making with multiple products and stochastic demand. It proposes a combination of Q-learning algorithm and approximate Q-value heuristic to achieve near-optimal solutions. The experiments show that the hybrid Q-learning method outperforms the classic Q-learning algorithm and approximate Q-value heuristic in terms of accuracy and convergence speed. Moreover, to tackle large-scale problems, three state aggregation schemes are developed and applied, with the third scheme achieving similar performance as the hybrid Q-learning method while significantly reducing computational time.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Proceedings Paper
Computer Science, Information Systems
Lauren Van De Ginste, Alexander De Cock, Axl Van Alboom, Yogang Singh, El-Houssaine Aghezzaf, Johannes Cottyn
Summary: In the Industry 4.0 era, the demand for highly customized products in small batch sizes has increased, leading to the need for flexible assembly workstations. A skill-centered model is introduced to describe resource activities and flexibility impacts, allowing for versatile formal models to convert flexibility needs into design and operational decisions. This model offers a framework that combines abstract and executable skills, allowing for easy adaptation and application in various settings.
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, PT V
(2021)
Proceedings Paper
Computer Science, Information Systems
Mehmet Uzunosmanoglu, Birger Raa, Veronique Limere, Alexander De Cock, Yogang Singh, Angel J. Lopez, Sidharta Gautama, Johannes Cottyn
Summary: This paper discusses the aggregate planning of Reconfigurable Assembly Lines (RAL) composed of hexagonal cells, proposing an Integer Quadratic Programming (IQP) model to address module assignment, cell and conveyor installation, and product routing issues simultaneously. The IQP model is implemented using Gurobi for solving an illustrative problem and its extensions.
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT II
(2021)
Proceedings Paper
Computer Science, Information Systems
Cheshmeh Chamani, El-Houssaine Aghezzaf, Abdelhakim Khatab, Birger Raa, Yogang Singh, Johannes Cottyn
Summary: The research considers a two-stage supply chain with two collaborating production plants in industrial symbiosis. Two different formulations are proposed: one using the SAA method to solve the natural model and the other based on a plant location reformulation. Experimental analysis indicates that the plant location reformulation significantly improves optimality gaps and computational times compared to the natural model.
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT II
(2021)
Proceedings Paper
Automation & Control Systems
Veronique Limere, Lisa Popelier, Nico A. Schmid
Summary: This study focuses on the efficient organization of product disassembly as a factor in shifting towards a circular economy. By presenting a profit-oriented optimization model that considers resource needs and limited workstation space, the impact of various problem-specific parameters on objective value and solution characteristics is demonstrated through a sensitivity analysis using a case study from literature.
