| 王天雷,莫金朋,曾志强.融合第三方仓储的分布式柔性流水车间调度的多目标优化方法研究[J].中国造纸学报,2026,41(2):190-201 |
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| 融合第三方仓储的分布式柔性流水车间调度的多目标优化方法研究 |
| Research on Multi-Objective Optimization Approach for Distributed Flexible Flow Shop Scheduling with Integration of Third-Party Warehousing |
| 投稿时间：2025-12-22 修订日期：2026-01-14 |
| DOI：10.11981/j.issn.1000-6842.2026.02.190 |
| 中文关键词: 分布式柔性流水车间调度 生产与物流协同优化 受约束的多目标优化 子种群协同进化 |
| Key Words:distributed flexible flow shop scheduling collaborative optimization of production and logistics constrained multi-objective optimization sub-population synergy evolution |
| 基金项目:五邑大学联合研发基金（2019WGALH21）；香港和澳门特别行政区基金（2022WGALH18）。 |
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| 中文摘要: |
| 针对造纸行业分布式柔性流水车间与第三方仓储物流协同优化问题，以最小化总运营成本、碳排放总量和订单交付延误率为优化目标，构建了集成生产调度、仓储分配与配送路径规划的混合整数规划模型。基于子种群协同的自适应多目标优化算法（ADAMS），通过建立异构协同框架与动态划分机制，融合差异化算子及优选池策略，有效解决了高维强约束下收敛速度与解集多样性的平衡难题。对30组企业真实算例的仿真测试结果表明，ADAMS算法的超体积指标较对比算法平均提升12.5%；通过融合“距离优势度”仓储决策机制与容量约束贪婪路径规划策略，ADAMS算法在100~390规模订单场景下实现了总成本、碳排放与交付延误率的协同优化，其在高维强约束生产与物流协同调度中的收敛性与解集多样性方面具有优势。 |
| Abstract: |
| A mixed-integer programming model integrating production scheduling， warehouse allocation， and vehicle routing was developed for the collaborative optimization problem of distributed flexible flow shops and third-party warehousing logistics in the paper industry， aimed to minimize total operational cost， total carbon emissions， and order delivery tardiness simultaneously. To solve these problems， an adaptive dynamic archive-based multi-objective optimization with sub-population synergy was proposed， which was termed ADAMS. By establishing a heterogeneous collaborative framework and a dynamic population decomposition mechanism， the algorithm incorporated differentiated evolutionary operators and an elite pool strategy， effectively balancing convergence speed and solution diversity under high-dimensional and strongly constrained conditions. The simulation experimental results on 30 real-world industrial instances showed that the hypervolume metric of ADAMS algorithm improved by an average of 12.5% compared with benchmark algorithms. By integrating a “distance superiority” warehouse selection mechanism with a capacity-constrained greedy routing strategy， the proposed algorithm achieved coordinated optimization of total cost， carbon emissions， and delivery tardiness in order-scale scenarios ranging from 100 to 390 orders. The results further demonstrated the superiority of ADAMS algorithm in terms of convergence performance and solution diversity for high-dimensional strongly constrained collaborative scheduling of production and logistics. |
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