Exact and approximation heuristic of mixed model assembly line balancing with parallel lines and task-dependent tooling consideration

• Published in: Computers & industrial engineering Vol. 193; p. 110265
• Main Authors: Alhomaidi, Esam, Askin, Ronald G.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024
• Subjects: Cost minimization; Heuristic; Integer programming; Lower bound; Parallel-mixed lines; Tooling; Integer programming; Cost minimization; Lower bound; Parallel-mixed lines; Tooling; Heuristic
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2024.110265

Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to an appropriate workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. This study seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Previous models are extended to include several realistic characteristics and efficient optimization techniques are developed to provide a more comprehensive model for building assembly systems. The research develops novel comprehensive approaches for obtaining models in the case of mixed models for parallel lines systems, task-dependent tooling consideration, and demand fulfillment. To assess the performance of the developed optimization models and heuristic, data sets from the literature are employed with different problem sizes and structures. The implementation results demonstrate that the proposed models and heuristic provide valuable insights. •Enhanced assembly line models: Optimizing mixed lines, efficient task allocation.•Advanced optimization techniques: Addressing demand, tooling in assembly systems.•Performance evaluation: Datasets validate the efficacy of models and heuristics.•Bridging literature gaps: Integrating realistic aspects into efficient models.•Rapid heuristic development: Efficient task assignment and cost analysis.