Process Route Optimization Based on FAHP and Adaptive Improved Ant Colony Algorithm

Process route planning directly influences carbon emissions, completion time, and the processing cost of mechanical products, and is crucial for achieving low-carbon, high-efficiency, and cost-effective machining production. To address this, an optimization method based on the fuzzy analytic hierarchy process (FAHP) and an adaptively improved ant colony algorithm is proposed. First, the manufacturing characteristics of the parts are analyzed, the work step element to represent them is introduced, and a carbon emission model for low-carbon manufacturing from the perspectives of material and energy flows is established. Additionally, an optimization model is constructed, targeting carbon emissions, completion time, and machining cost at the process level. To effectively address the fuzzy weight distribution among the optimization objectives, FAHP is employed to determine the weight of each factor and to define a comprehensive objective function. To enhance the solving efficiency of the optimization algorithm, an adaptively improved ant colony algorithm with multi-strategy fusion is utilized. Finally, the machining data of a part are employed as a test case to verify the feasibility and practicality of the proposed method. A comparison with the actual data indicates that, when low carbon, high efficiency, and low cost were treated as multi-objective optimization criteria, the carbon emissions were 1425.06 g, the processing time was 545.5 s, and the processing cost was CNY5.84. In comparison with the results from the other three experiments, the carbon emission, processing time, and processing cost exhibit the best overall performance, aligning with the low-carbon, low-cost, and sustainable production requirements.