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This paper addresses the cell formation problem with alternative part routes. The problem is considered in the aspect of the natural constraints of real-life production systems such as cell size, separation and co-location constraints. Co-location constraints were added to the proposed model in order to deal with the necessity of grouping certain machines in the same cell for technical reasons, and separation constraints were included to prevent placing certain machines in close vicinity. The objective is to minimise the weighted sum of the voids and the exceptional elements. A hybrid algorithm is proposed to solve this problem. The proposed algorithm hybridises the modified sub-gradient (MSG) algorithm with a genetic algorithm. MSG algorithm solves the sharp augmented Lagrangian dual problems, where zero duality gap property is guaranteed for a wide class of optimisation problems without convexity assumption. Generally, the dual problem is solved by using GAMS solvers in the literature. In this study, a genetic algorithm has been used for solving the dual problem at the first time. The experimental results show the advantage of combining the MSG algorithm and the genetic algorithm. Although the MSG algorithm, whose dual problem is solved by GAMS solver, and the genetic algorithm cannot find feasible solutions, hybrid algorithm generates feasible solutions for all of the test problems. 相似文献
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为了减弱充电速率的时间可变性对能量收集传感器网络的影响,文中提出一种基于高效对偶分解和次梯度策略的算法Quick-Fix来计算数据采样率和路由;另外,为了应对因充电率发生波动所造成的电池断电、溢出、采样丢失和能量收集机会丢失等情况,提出一种本地算法SnapIt,通过对采集率进行调节以维持电池电量在目标水平上;基于TOSSIM模拟器的性能评估表明,联合QuickFix和SnapIt可跟踪网络瞬时最优效用,同时维持电池电量处于目标水平;与基于余压的IFRC相比,文中方法使总体数据速率平均提升42％,同时显著提升了网络效用. 相似文献
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针对一类带最小批量约束的计划问题, 提出了基于拉格朗日松弛策略求解算法. 通过拉格朗日松弛策略,将原问题转为一系列带最小批量约束的动态经济批量W-W(Wagner-Whitin)子问题. 提出了解决子问题且其时间复杂度O(T3)的最优前向递推算法. 对于拉格朗日对偶问题, 用次梯度算法求解, 获得原问题的下界. 若对偶问题的解是不可行的, 通过固定装设变量, 求解一个剩余的线性规划问题来进行可行化处理. 最后, 数据仿真验证了算法的有效性. 相似文献
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针对只有硬模块的布图规划问题,通常将其构建成组合优化模型,但求解过程时间成本高。为提高求解效率,提出了一种基于非光滑解析数学规划的布图规划算法。基于布图中器件的坐标表示,构建了一个泛化的非光滑解析数学规划模型,将不同场景下的布图规划问题的不同优化阶段处理为该泛化模型的特例,并利用共轭次梯度算法（conjugate sub-gradient algorithm,CSA）对其进行求解。针对固定轮廓布图规划问题,通过统一框架下的全局布图规划、合法化、局部优化三个阶段,实现了在固定轮廓约束下的线长优化。针对无固定轮廓约束问题,提出了带黄金分割策略的共轭次梯度算法（conjugate sub-gradient algorithm with golden section strategy,CSA_GSS）,利用黄金分割策略缩小固定轮廓的面积,达到面积和线长双优化的效果。实验在GSRC测试电路上与基于B*-树表示的布图规划算法进行比较,该算法对于大规模电路在线长和时间方面均占据优势。实验结果表明,该算法能以更低的时间复杂度获得更优的线长。 相似文献
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超奈奎斯特(Faster-than-Nyquist,FTN)速率传输可以有效提高频谱效率,但这种非正交传输方式引入的严重码间串扰相应提高了接收端的处理难度。针对该问题,设计了一种基于循环成块传输的低复杂度检测算法。最优检测被建模为无约束的二元二次规划(Boolean Quadratic Program,BQP)问题,为了求解该NP-hard问题,采用无穷范数约束松弛原问题的非凸可行解集,并基于次梯度下降法提出松弛问题的有效优化算法。数值仿真结果表明,所提算法在误比特率(Bit Error Rate,BER)性能上优于频域均衡,且在可接受的性能损失范围内算法执行效率远高于理论最优的最大似然序列估计(Maximum Likelihood Sequence Estimation,MLSE)。 相似文献
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