A hybrid flow shop scheduling optimization method with automated guided vehicles

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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