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首页> 外文会议>ICIC 2011;International conference on intelligent computing >Hybrid Differential Evolution Optimization for No-Wait Flow-Shop Scheduling with Sequence-Dependent Setup Times and Release Dates
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Hybrid Differential Evolution Optimization for No-Wait Flow-Shop Scheduling with Sequence-Dependent Setup Times and Release Dates

机译：序列依赖的设置时间和发布日期的无等待流水车间调度的混合差分进化优化

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摘要

In this paper, a hybrid algorithm based on differential evolution (DE), namely HDE, is proposed to minimize the total completion time criterion of the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs), which is a typical NP-hard combinatorial optimization problem with strong engineering background. Firstly, to make DE suitable for solving flow-shop scheduling problem, a largest-order-value (LOV) rule is used to convert the continuous values of individuals in DE to job permutations. Secondly, a speed-up evaluation method is developed according to the property of the NFSSP with SDSTs and RDs. Thirdly, after the DE-based exploration, a problem-dependent local search is developed to emphasize exploitation. Due to the reasonable balance between DE-based global search and problem-dependent local search as well as the utilization of the speed-up evaluation, the NFSSP with SDSTs and RDs can be solved effectively and efficiently. Simulation results and comparisons demonstrate the superiority of HDE in terms of searching quality, robustness, and efficiency.
机译：本文提出了一种基于差分进化（DE）的混合算法，即HDE，以最小化具有序列依赖建立时间（SDST）的无等待流水车间调度问题（NFSSP）的总完成时间准则，并且发布日期（RDs），这是具有强大工程背景的典型NP-hard组合优化问题。首先，为了使DE适用于解决流水车间调度问题，使用最大订单价值（LOV）规则将DE中个人的连续值转换为工作置换。其次，根据具有SDST和RD的NFSSP的性质，开发了一种加速评估方法。第三，在基于DE的探索之后，开发了一个依赖问题的局部搜索来强调开发。由于基于DE的全局搜索和与问题相关的局部搜索之间的合理平衡，以及利用加速评估，可以有效，高效地解决带有SDST和RD的NFSSP。仿真结果和比较证明了HDE在搜索质量，鲁棒性和效率方面的优越性。

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