Abstract: To address the challenges of prolonged solution times, low efficiency, and inadequate collaborative optimization in handling large-scale order scheduling and product distribution scheduling in passenger car engine assembly workshops characterized by multi-variety, small-batch, and order-oriented production, this paper proposes a collaborative optimization method based on modified genetic algorithm and simulated annealing (MGASA). The optimization model aims at minimizing customer expectation time, earliness-tardiness costs, and logistics distribution costs. A priority rule and four categories of characteristic indicators are adopted for problem encoding and fitness evaluation. Iterative population evolution and neighborhood solution generation are repeatedly executed at the same annealing temperature to expand the neighborhood of feasible solutions, facilitating the attainment of a globally optimal solution. Consequently, the production and logistics scheduling schemes for the assembly workshops are determined. Experimental validation conducted on datasets of various sizes demonstrates that the proposed method significantly enhances solution efficiency and supports rapid decision-making in collaborative logistics scheduling, highlighting its practical applicability to passenger car engine assembly workshops.
Keywords: assembly workshop logistics; vehicle routing optimization; collaborative optimization; modified genetic algorithm and simulated annealing(MGASA); time window
合肥工业大学学报自科版