A method for optimizing the scheduling of a hybrid assembly line with automated guided vehicles (AGVs).

The algorithm of adaptive population partitioning and hybrid artificial bee colony-firefly optimization for hybrid flow shop scheduling solves the problems of transportation resource constraints and solution quality, improves the utilization rate of equipment and transportation resources, and shortens the maximum production completion time. It is applicable to the optimization of hybrid flow shop scheduling in intelligent manufacturing.

CN122172755BActive Publication Date: 2026-07-17LIAOCHENG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
LIAOCHENG UNIV
Filing Date
2026-05-13
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing research on hybrid flow workshop scheduling neglects transportation resource constraints, making it difficult to balance the efficiency of algorithm optimization with the quality of solutions. Mathematical programming models have insufficient performance in large-scale scenarios, resulting in a significant impact of AGV transportation on the production process, low utilization of equipment and transportation resources, and difficulty in optimizing the maximum production completion time.

Method used

Adaptive population partitioning hybrid artificial bee colony-firefly algorithm (APD-HABCFA) is adopted, combined with dynamic population size adjustment, bidirectional symmetrical population exchange and elite crossover strategy. By constructing a multi-dimensional mathematical modeling system and a hybrid intelligent optimization algorithm framework, the AGV transportation process is optimized, and the overall utilization rate of equipment and transportation resources is improved.

Benefits of technology

It effectively reduces the impact of AGV transportation on the production process, improves the comprehensive utilization rate of workshop equipment and transportation resources, shortens the maximum production completion time, outperforms traditional algorithms and models, and is more in line with the actual intelligent manufacturing production needs.

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Abstract

This invention belongs to the field of hybrid assembly line scheduling technology in intelligent manufacturing, and particularly relates to an optimization method for scheduling hybrid assembly line workshops with automated guided vehicles (AGVs). Addressing the problems of neglecting AGV transportation constraints, the susceptibility of single algorithms to local optima, and the inefficiency of large-scale mathematical programming model solutions in hybrid assembly line scheduling, this invention constructs an APD-HABCFA-CP hybrid optimization framework: employing a dual-population parallel search using artificial bee colony and firefly algorithms, combined with hybrid initialization, dynamic population size adjustment, bidirectional symmetric population exchange, and elite crossover strategies to enhance global and local search capabilities; when the algorithm reaches 7 / 10 of its total execution time, a constrained programming (CP) model is introduced, using the optimal solution obtained by the algorithm as the initial solution for precise optimization. This invention combines the search speed of metaheuristic algorithms with the precise solution advantages of the CP model, effectively improving the utilization rate of workshop equipment and AGV transportation resources.
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Citation Information

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