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| Research on Multi-Objective Optimization Approach for Distributed Flexible Flow Shop Scheduling with Integration of Third-Party Warehousing |
| Received:December 22, 2025 Revised:January 14, 2026 |
| DOI：10.11981/j.issn.1000-6842.2026.02.190 |
| Key Words:distributed flexible flow shop scheduling;collaborative optimization of production and logistics;constrained multi-objective optimization;sub-population synergy evolution |
| Fund Project:五邑大学联合研发基金（2019WGALH21）；香港和澳门特别行政区基金（2022WGALH18）。 |
| Author Name | Affiliation | Postcode | | WANG Tianlei* | 1School of Electronics and Information Engineering， Wuyi University， Jiangmen， Guangdong Province， 529020 | 529020 | | MO Jinpeng | 1School of Electronics and Information Engineering， Wuyi University， Jiangmen， Guangdong Province， 529020 | 529020 | | ZENG Zhiqiang* | 2School of Mechanical Engineering， Dongguan University of Technology， Dongguan， Guangdong Province， 523808 | 523808 |
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| 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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