基于广义遗传粒子群优化算法的供应链优化求解

供应链优化研究是供应链管理中的一个重要问题,也是一个难题,首先提出了一个新型供应链优化模型,针对该优化问题的求解,构造了融入特殊自然演化规则的广义遗传算法(GA),并且与粒子群优化结合,得到了广义遗传粒子群优化算法,克服了粒子群优化算法局部收敛的缺陷,提高了其全局收敛的能力。实验表明,对供应链优化问题的求解,广义遗传粒子群优化算法优于传统的遗传算法、粒子群优化算法和分枝界定法。     

远亲杂交遗传算法及其在供应链优化中的应用

基于生物系统中普遍存在“远亲杂交优于近亲繁殖”的现象,提出了基于远亲杂交的遗传算法：远亲杂交保持演化群体良好的多样性,克服了遗传算法局部收敛的缺陷,提高了算法的全局搜索能力。针对供应链优化研究,本文提出了一个新型供应链优化模型,同时把新算法应用于求解该优化问题,结果表明,对供应链优化问题的求解,远亲杂交遗传算法优于基本遗传算法和分枝界定法。     

遗传算法在供应链优化问题中的应用

本文以新疆某番茄酱厂番茄酱生产过程信息化项目为背景,主要针对生产计划中的供应链出现的问题,提出了一个新型供应链优化模型,并且采用遗传算法对其求解。     

基于实数编码的广义遗传算法及其在优化问题中的应用

对遗传算法中一些具有代表性的算法作了简要分析和评论，在此基础上将实数编码引入广义遗传算法，给出了一个算法框架，设计了相应的遗传操作方法，并将该算法应用于函数优化问题和神经网络训练问题，数值算例表明，该方法具有全局优化功能和快速收敛特性，对于求解复杂优化问题具有广泛的适用性。     

基于Matlab遗传算法工具箱的函数优化问题求解

介绍了遗传算法的基本原理和求解流程,详细阐述了Matlab遗传算法工具箱的使用方法,并通过使用遗传算法工具箱对一个典型的函数优化问题进行求解,验证了该工具箱在解决函数优化问题上的有效性和实用性     

玻璃排版优化中遗传算法的应用

研究了应用遗传算法求解非线性多目标组合优化问题———玻璃排版优化问题 ,详细讲解了如何设计求解该优化系统中三个典型组合优化子问题的遗传算法 ,并对三个子问题的求解关系进行了分析 ,总结出遗传算法的不同构造方法对系统优化结果的影响。     

随机化均匀设计混合遗传算法求解图的二划分问题

图的二划分问题是一个典型的NP—hard组合优化问题,在许多领域都有重要应用．近年来,传统遗传算法等各种智能优化方法被引入到该问题的求解中来,但效果不理想．基于理想浓度模型的机理分析,利用随机化均匀设计抽样的理论和方法,对遗传算法中的交叉操作进行了重新设计,并在分析图的二划分问题特点的基础上,结合局部搜索策略,给出了一个解决图的二划分问题的新的遗传算法．通过将该算法与简单遗传算法和佳点集遗传算法进行求解图的二划分问题的仿真模拟比较,可以看出新的算法提高了求解的质量、速度和精度．     

基于计算智能的射孔枪结构优化设计方法

在对石油射孔枪结构进行有限元静、动力分析的基础上,利用BP神经网络对有限元分析得出的样本数据,建立射孔枪结构设计参数盲孔处的最大应力(输入)与盲孔深度、盲孔直径(输出)的全局性映射关系,获得遗传算法求解结构优化问题所需的目标函数值.最后,用改进的遗传算法进行射孔枪结构的优化设计.结果表明,基于神经网络和遗传算法的优化技术应用在射孔枪结构的优化设计中是有效、合理的.从广义的角度,作为结构优化问题求解方法的一个探讨,本文所提出的优化技术,也为工程领域中复杂、多变量,尤其是优化设计目标无法或难以表示成设计变量的显函数的优化问题的求解提供了新的思路和技术手段.     

拉丁超立方体抽样遗传算法求解图的二划分问题

图的二划分问题是一个典型的NP-hard组合优化问题, 在许多领域都有重要应用. 近年来, 传统遗传算法等各种智能优化方法被引入到该问题的求解中来, 但效果不理想. 基于理想浓度模型的机理分析, 利用拉丁超立方体抽样的理论和方法, 对遗传算法中的交叉操作进行了重新设计, 并在分析图二划分问题特点的基础上, 结合局部搜索策略, 给出了一个解决图二划分问题的新的遗传算法, 称之为拉丁超立方体抽样遗传算法. 通过将该算法与简单遗传算法和佳点集遗传算法进行求解图二划分问题的仿真模拟比较, 可以看出新的算法提高了求解的质量、速度和精度.     

基于遗传算法的有限推力交会轨道优化仿真

针对有限推力空间飞行器交会对接逼近段燃料/时间组合最优轨道优化问题,提出了一种采用遗传算法求解的优化策略.以C-W方程为交会模型,将整个逼近段分为若干弧段,在每个弧段内追踪航天器均采用常值推力机动,这样可以求得C-W方程在每个弧段内的解析解,于是,交会对接逼近段的轨道优化问题可以转化为具有非线性约束的数学规划问题,最后采用广义拉格朗日-遗传算法对该问题进行了优化求解.数学仿真结果表明,该方法可以很好的解决交会对接终端逼近段燃料/时间组合最优轨道优化问题.     

求解供应链伙伴选择的泛遗传差异演化算法

伙伴选择是供应链组建过程中的关键问题,也是一个难题,针对该问题,构造了融入特殊自然演化规则的泛遗传算法,并且与差异演化算法结合,得到了泛遗传差异演化算法,克服了差异演化算法局部收敛的缺陷,提高了其全局收敛的能力。实验表明,针对供应链伙伴选择问题,泛遗传差异演化算法优于传统的遗传算法和差异演化算法。     

Simulation-based heuristic method for container supply chain network optimization

With the emerging of free trade zones (FTZs) in the world, the service level of container supply chain plays an important role in the efficiency, quality and cost of the world trade. The performance of container supply chain network directly impacts its service level. Therefore, it is imperative to seek an appropriate method to optimize the container supply chain network architecture. This paper deals with the modeling and optimization problem of multi-echelon container supply chain network (MCSCN). The problem is formulated as a mixed integer programming model (MIP), where the objective is subject to the minimization of the total supply chain service cost. Since the problem is well known to be NP-hard, a novel simulation-based heuristic method is proposed to solving it, where the heuristic is used for searching near-optimal solutions, and the simulation is used for evaluating solutions and repairing unfeasible solutions. The heuristic algorithm integrates genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method and give some managerial implications.     

A genetic algorithm for the vendor-managed inventory routing problem with lost sales

This paper proposes a genetic algorithm (GA) for the inventory routing problem with lost sales under a vendor-managed inventory strategy in a two-echelon supply chain comprised of a single manufacturer and multiple retailers. The proposed GA is inspired by the solving mechanism of CPLEX for the optimization model of the problem. The proposed GA determines replenishment times and quantities and vehicle routes in a decoupled manner, while maximizing supply chain profits. The proposed GA is compared with the optimization model with respect to the effectiveness and efficiency in various test problems. The proposed GA finds solutions in a short computational time that are very close to those obtained with the optimization model for small problems and solutions that are within 3.2% of those for large problems. Furthermore, sensitivity analysis is conducted to investigate the effects of several problem parameters on the performance of the proposed GA and total profits.     

GA和SVM在供应链绩效评价中的应用

研究了基于遗传算法和支持向量机的供应链绩效评价问题。将供应链绩效评价问题用遗传算法进行特征选择并同时对支持向量机参数进行了优化。研究表明该方法能提取出影响供应链绩效的重要属性,减小供应链评价模型的复杂度。应用实例表明基于遗传算法和支持向量机的评价结果从整体上要优于标准支持向量机的评价结果。     

Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems

The optimal design of supply chain (SC) is a difficult task, if it is composed of the complicated multistage structures with component plants, assembly plants, distribution centers, retail stores and so on. It is mainly because that the multistage-based SC with complicated routes may not be solved using conventional optimization methods. In this study, we propose a genetic algorithm (GA) approach with adaptive local search scheme to effectively solve the multistage-based SC problems.The proposed algorithm has an adaptive local search scheme which automatically determines whether local search technique is used in GA loop or not. In numerical example, two multistage-based SC problems are suggested and tested using the proposed algorithm and other competing algorithms. The results obtained show that the proposed algorithm outperforms the other competing algorithms.     

A simulation-based genetic algorithm for inventory optimization in a serial supply chain

One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single‐product serial supply chain operating with a base‐stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base‐stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base‐stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base‐stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base‐stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.     

基于Prim-DMGA算法的闭环供应链网络鲁棒优化研究

针对不确定环境下的闭环供应链网络优化问题,在需求不确定及设施中断风险的条件下,基于鲁棒对等优化方法建立了一种以闭环供应链网络总成本最小为目标的鲁棒优化模型,以解决供应链网络中的不确定性问题,并提出了Prim-DMGA。首先基于Prim算法得到高质量的初始种群,其次让路径规划方案和设施选址方案在两层自适应GA的不断反馈中达到最优。实验结果表明,Prim-DMGA得到的目标函数值优于单层Prim-MGA与传统GA,且在求解大规模算例时,求解结果优于CPLEX软件。研究结论表明,Prim-DMGA能以较少的计算时间获得质量更优的解,鲁棒优化模型可以有效减少不确定因素带来的不利影响,提高闭环供应链网络的鲁棒性能。     

混沌遗传算法在IP网络组播路由中的应用

提出了一种基于混沌优化和遗传算法的新颖的QoS组播路由算法，该路由算法把混沌系列和遗传算法结合起来，并提供了一种新的编码方式和一种自适应的变异遗传操作，从而克服了传统遗传算法中存在的早熟现象，加快了收敛速度。仿真结果显示，该文提出的算法在解题的精度和收敛速度等方面都优于遗传算法。