多色服装裁剪分床计划复合优化算法
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浙江工业大学

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中图分类号:

TP273

基金项目:

NSFC-浙江两化融合联合基金(U1709213);浙江省重点研发计划(2020C01109)


Hybrid Optimization Algorithm for Cut Order Planning of Multicolor Garment
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Affiliation:

Zhejiang University of Technology

Fund Project:

NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (No. U1709213);Key Research and Development Program of Zhejiang Province (No. 2020C01109)

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    摘要:

    服装生产工业中服装裁剪分床计划是工艺流程的第一个步骤,对生产管理和成本控制起决定性作用,而大批量不规则多色服装裁剪分床是关键难题,其本质是一个NP难的非线性优化问题.针对该问题,本文提出一种基于NSGAII的复合优化算法,首次将多目标进化算法应用于裁剪分床计划问题中.首先,建立多色服装裁剪分床多目标进化优化模型,以生产过剩量最小和分床数量最少为优化目标;其次,采用岭回归解耦策略将分床优化问题中的尺码组合方案和铺布层数方案进行线性解耦,从而提高求解精度;同时,采用实数编码方式对分床方案进行编码,提高算法运行效率.最后,通过实际应用案例和算法对比实验,表明了本文算法在求解精度和效率上相比传统启发式算法和优化软件工具优势明显.因此,所提算法能有效优化裁剪部门生产管理,减少布料浪费和生产设备投入,具有很好的应用价值和参考意义.

    Abstract:

    Cut order planning (COP) is the first stage of the garment manufacturing process and plays an important role in the production management and cost control. COP for large scale and irregular multicolor garment orders remains a key issue, and it is an NP (Non-deterministic Polynomial)-hard nonlinear optimization problem. To deal with this issue, a hybrid optimization algorithm based on NGSAII was proposed, which is the first time that applied the multi-objective evolutionary algorithm (MOEA) to solve COP problem. First, an MOEA model for multicolor COP was established to minimize the production excess and the number of cutting table. Second, the ridge regression decoupling method was utilized to decouple the size combination scheme and the spreading layer scheme to improve the accuracy of solutions. Meanwhile, the real-number encoding strategy was used to encode COP solutions to promote the solving efficiency. Finally, application cases and comparison experiments of several algorithms were carried out. The results show that the devised algorithm has obvious advantages in accuracy and efficiency over heuristic algorithms and optimization software. As a result, the hybrid optimization algorithm can effectively optimize the production management of the cutting department, thereby reducing the cost of fabric and setup, and has significant application and reference value.

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  • 收稿日期:2020-12-15
  • 最后修改日期:2021-03-18
  • 录用日期:2021-03-29
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