Benders decomposition algorithm for MMAL balancing problem with express parallel line

Mixed-model assembly line (MMAL) could be a form of line capable of manufacturing numerous models of a product on one line. In this paper, a particular parallel MMAL balancing problem is studied in a make-to-order (MTO) production system. Customer’s satisfaction is examined in this paper by consider...

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Veröffentlicht in:Operational research Jg. 25; H. 3; S. 74
1. Verfasser: Tanhaie, F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025
Springer Nature B.V
Schlagworte:
Algorithms
Artificial intelligence
Assembly lines
Balancing
Benders decomposition
Business and Management
Computational Intelligence
Customer satisfaction
Customers
Decomposition
Ergonomics
Exact solutions
Flexibility
Genetic algorithms
Image processing systems
Lead time
Linear programming
Management Science
Manufacturing
Mathematical models
Multiple objective analysis
Operations Research
Operations Research/Decision Theory
Original Paper
Particle swarm optimization
Produce to order
Production planning
Sorting algorithms
Work stations
Workloads
Benders decomposition algorithm
Mixed-model assembly line
Make-to-order
Priority orders
Parallel lines
ISSN:1109-2858, 1866-1505
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Zusammenfassung:Mixed-model assembly line (MMAL) could be a form of line capable of manufacturing numerous models of a product on one line. In this paper, a particular parallel MMAL balancing problem is studied in a make-to-order (MTO) production system. Customer’s satisfaction is examined in this paper by considering two parallel assembly lines: a main assembly line for normal orders and an express parallel line capable of quick assembling of models for priority orders. Furthermore, due to change of customer demands in MTO environment, the rebalancing problem is considered to maximize flexibility in the lead-time. To solve this multi-objective balancing problem, an integrated Benders Decomposition Algorithm (BDA) based on the LP-metric method is introduced. In large-size problems, the presented model is compared with the non-dominated sorting genetic algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) and the effectiveness of the proposed integrated BDA is investigated based on computational experiments. The percentage of gap from optimal solutions in the integrated BDA is zero and this gap changes from 0 to 4.2 in the NSGA-II algorithm and from 1 to 4.4 in the MOPSO algorithm. With regard to run times, the results obtained from NSGA-II are relatively superior in calculation time compared to the integrated BDA. However, the solution times for the integrated BDA are not large with respect to exact solutions of this algorithm and dimensions of problems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1109-2858
1866-1505
DOI:10.1007/s12351-025-00917-1