Abstract: To achieve the minimization of total procurement costs and ensure continuous production in the overseas bases of manufacturing enterprises during their international multi-source procurement,a nonlinear integer programming model with hard constraints was constructed.Moreover,an improved integer coding genetic algorithm was proposed for solving this model,which comprised three enhancement mechanisms:heuristic population initialization,infeasible solution optimization and repair,and structure-oriented chromosome evolution.Subsequently,numerical experiments were devised to compare the performance of the multiple adaptive genetic algorithm and modified discrete particle swarm optimization.The results validated the superior performance of the proposed algorithm in terms of solution quality and stability.Furthermore,ablation experiments were conducted to verify the effectiveness of the three enhancement mechanisms of the algorithm.Through case analysis,the importance of considering supply lead times in optimizing procurement plan was further confirmed.

Key words: overseas base, supply lead time, cross-border multiple sourcing, procurement plan optimization, genetic algorithms

摘要： 为了实现制造企业海外基地跨国多源采购过程中采购综合成本最小化,同时保障生产的连续性,构建了带有硬性约束的非线性整数规划模型,并提出了一种改进的整数编码遗传算法进行求解。该算法包含启发式种群初始化、不可行解优化与修复及结构导向染色体进化三种改进机制。通过设计数值实验对比多重自适应遗传算法及改进的粒子群算法的运行结果,验证了该算法在求解质量及稳定性方面表现更优;同时,设计消融实验验证了该算法三种改进机制的有效性。最后,通过案例分析进一步证实了考虑供货提前期在采购计划优化时的重要性。

关键词: 海外基地, 供货提前期, 跨国多源采购, 采购计划优化, 遗传算法