基于粒子群算法的分布式多工厂批量计划问题研究

研究了分布式多工厂协同生产的约束批量计划问题,以产品的生产成本、库存成本、调整准备成本和运输成本之和最小为目标,构建了生产能力有限情况下的数学模型,提出了用于求解该问题的粒子群算法方案,阐明了该算法方案的具体实现过程.对典型算例进行了仿真,并与LINGO软件的求解结果进行了比较,结果表明粒子群算法方案的有效性和可行性.     

基于改进的粒子群算法求解供应链网络均衡问题

传统的供应链求解方法为投影法,针对其要对投影进行计算,十分复杂的缺点,提出用改进的粒子群算法求解供应链均衡问题,利用动态异步调整学习因子来有效的提高了算法搜索能力与精度。本文介绍了供应链网络均衡问题转变为无约束优化问题的方法,然后用改进的粒子群优化算法进行求解。通过四个数值算例,将实验结果与标准粒子群算法、蜂群算法、学习因子同步变化的粒子群算法进行比较,验证了改进的粒子群优化算法在解决供应链网络均衡问题中的有效性与优越性,为供应链网络求解提供了一种新的方法。     

超越方程的优化解法

针对粒子群算法局部搜索能力差,后期收敛速度慢等缺点,提出了一种改进的粒子群算法,该算法是在粒子群算法后期加入拟牛顿方法,充分发挥了粒子群算法的全局搜索性和拟牛顿法的局部精细搜索性,从而克服了粒子群算法的不足,把超越方程转化为函数优化的问题,利用该算法求解,数值实验结果表明,算法有较高的收敛速度和求解精度。     

装配型企业订货量分配与订单排产联合决策研究

针对供应商交货数量不确定环境下,多品种小批量装配型制造企业因生产物料不配套造成生产计划不可行甚至客户订单拖期的问题,从企业运作整体出发,考虑订货量分配决策对订单生产和交货的影响,以最小化采购成本和最小化订单排产相关成本为优化目标,在允许零部件拖期交货且供应商提供拖期价格折扣条件下,建立订货量分配与订单排产联合优化模型。针对可行解空间巨大、传统数学规划方法难以求解的问题,从增强搜索性能角度出发,设计基于自定义邻域搜索算子的局部搜索机制和基于随机与种群重构变异机制的改进粒子群算法的模型求解策略。通过应用实例对本文模型和算法进行了有效性验证和灵敏度分析,结果表明,相比于传统的分散决策方案,本文模型能够有效降低整体成本水平,引入的改进机制能够显著提升算法搜索性能,为企业供应风险下的运营决策制定提供理论参考。     

基于粒子群算法的大型网络计划资源优化

现有求解网络计划资源优化的方法中,解析法不能解决大型复杂网络优化问题,启发式方法过多依赖具体问题、求解效率低,遗传算法生成新一代优化解种群依据的三个算子的实现参数选择,大部分依靠经验并严重影响解的品质,粒子群算法存在大型网络计划资源优化计算量过大和缺少大型网络计划资源优化算例问题.借助设计网络计划时间参数的计算机算法、建立评价函数、设计进化方程等基础工作,选择与工作开始时间相关的变量作为粒子空间位置,用蒙特卡洛方法和限制条件优化初始粒子群,设置可行解范围,用二维动态数组解决大型网络计划资源优化运行image超限问题,通过粒子群算法进化,寻求大型网络计划资源优化解,算例表明基于粒子群算法的大型网络计划资源优化效果明显,粒子群算法参数分析表明:粒子群算法的参数会影响网络计划资源优化结果,而且初始粒子群限制条件和优化目标设置的影响程度较大.     

免疫粒子群优化算法在农业水资源优化配置中的应用

求解农业水资源优化配置模型(高维非线性优化模型),较常采用大系统分解协调原理和动态规划相结合的方法,这样减少了变量个数,便于优化求解,但协调的过程需要多次从低阶模型中返回信息,而且对于每层的寻优求解过程存在难以克服的矛盾.采用标准的粒子群优化算法则优化程度不易保证并容易陷入局部最优,优化结果对初始种群依赖性较强.因此应用免疫进化算法对标准粒子群优化算法进行改进并应用于灌区农业水资源优化配置模型的求解.算例分析表明,免疫粒子群算法为求解高维复杂的优化配置问题提供了新思路.     

改进的二进制粒子群算法求解车辆路径问题

设计了一种改进的二进制粒子群优化算法来求解车辆路径问题,算法基于粒子群算法的寻优模式充分考虑粒子之间的导向作用,改进二进制粒子群算法的位取值方式,减小了在进化过程中停滞于局部最优解的概率,并通过构造辅助函数处理优化问题的约束条件,基于分层次实现多个目标的思路来寻优,提高了算法的搜索效率和计算速度.实验测试结果验证了该算法对求解车辆路径问题的适用性和有效性.     

基于改进粒子群算法的学习路径优化方法

提出一种改进粒子群算法求解在线学习系统中的学习路径优化问题.在建模时综合考虑了学习者的学习目标、知识掌握水平、学习成本和资源相关度等因素;在寻优时采用局部邻域搜索与禁忌搜索相结合的方式,以改进标准粒子群方法的寻优性能.实验结果表明,该方法具有较高的实用性和准确性,是学习路径优化问题的一种有效求解算法.     

基于引力搜索和粒子群混合优化算法的证券投资组合问题研究

本文研究考虑交易成本的投资组合模型,分别以风险价值(VAR)和夏普比率(SR)作为投资组合的风险评价指标和效益评价指标。为有效求解此模型,本文在引力搜索和粒子群算法的基础上提出了一种混合优化算法(IN-GSA-PSO),将粒子群算法的群体最佳位置和个体最佳位置与引力搜索算法的加速度算子有机结合,使混合优化算法充分发挥单一算法的开采能力和探索能力。通过对算法相关参数的合理设置,算法能够达到全局搜索和局部搜索的平衡,快速收敛到模型的最优解。本文选取上证50股2014年下半年126个交易日的数据,运用Matlab软件进行仿真实验,实验结果显示,考虑交易成本的投资组合模型可使投资者得到更高的收益率。研究同时表明,基于PSO和GSA的混合算法在求解投资组合模型时比单一算法具有更好的性能,能够得到满意的优化结果。     

求解旅行商问题的一种改进粒子群算法

本文研究了求解旅行商问题的粒子群算法。针对标准粒子群算法在求解旅行商问题过程中容易出现早熟和停滞现象的缺点,提出了一种改进的粒子群算法。首先,在初始种群的选取过程中,利用改进的贪婪策略直接获得具有较高性能的初始种群以提高算法的搜索效率。其次,通过引入次优吸引子,使粒子在搜索过程中可以更加充分地利用群体的信息来提高自身的性能,有效抑制收敛过程中的停滞现象,提高算法的搜索能力。最后为了验证所提出的方法的有效性和可行性,对TSPLIB标准库中的多个实例进行了测试,并给出了数值结果。     

考虑交货期的生产运输联合优化问题研究

结合企业实际场景研究了考虑交货期的多个工厂、多条生产线、单一产品的生产与运输联合优化问题.已知客户订单需求量和交货时间窗,考虑了各条生产线在不同时段的生产能力约束,在满足交货时间窗约束的前提下,以生产、存储、运输费用之和极小化为目标建立了生产与运输联合优化问题的混合整数规划模型,通过分析模型结构证明了在不考虑固定生产成...     

基于低碳环保因素的“前港后厂”钢铁产成品运输网络优化

中国大部分钢铁企业深居内陆,出口钢材需远距离运输到港口,再通过海运发往世界各地,故建立港口与钢铁企业的合作关系将尤为重要。与铁路、水路运输相比,公路运输单位换算周转量的碳排放量更高。但中国内陆运输多采用公路运输,这将加剧大气污染。本文提出基于“前港后厂”联运的钢铁产成品运输问题,以加强港口与钢铁企业的合作。为降低运营成本和碳排放量,建立以碳排放成本、运输成本、仓储成本及时间窗惩罚成本最小为目标的钢铁产成品运输网络优化模型;并设计融合和声搜索的环形拓扑结构PSO算法进行求解;最后对仓储成本进行灵敏度分析,以探究其对“前港后厂”模式的影响。结果表明,“前港后厂”模式不仅能有效降低运输网络的总成本和碳排放量,且合理的仓储成本更能加强港口和钢铁企业间的紧密性。     

托运人联盟下的订单组合及车货调配问题研究

中小型企业的快速发展使得如何有效利用其物流资源、降低其物流成本成为一个亟需解决的问题。本文基于运输联盟的角度,建立了以最小化总成本为优化目标,综合考虑各运输需求时间窗、运输量等因素的车货调度模型。而后,提出了3种时间窗处理策略,设计了粒子群算法对上述模型进行求解,并通过算例对模型和算法的有效性进行了分析。算例结果表明,该模型一方面能够显著降低物流总成本,另一方面可有效节约使用车辆数。因此,本文研究对降低社会物流成本、整合社会物流资源具有一定的理论意义。     

An Application of Branch and Cut to Open Pit Mine Scheduling

The economic viability of the modern day mine is highly dependent upon careful planning and management. Declining trends in average ore grades, increasing mining costs and environmental considerations will ensure that this situation will remain in the foreseeable future. The operation and management of a large open pit mine having a life of several years is an enormous and complex task. Though a number of optimization techniques have been successfully applied to resolve some important problems, the problem of determining an optimal production schedule over the life of the deposit is still very much unresolved. In this paper we will critically examine the techniques that are being used in the mining industry for production scheduling indicating their limitations. In addition, we present a mixed integer linear programming model for the scheduling problems along with a Branch and Cut solution strategy. Computational results for practical sized problems are discussed.     

New mathematical model for the bi-objective inventory routing problem with a step cost function: A multi-objective particle swarm optimization solution approach

Inventory management and satisfactory distribution are among the most important issues considered by distribution companies. One of the key objectives is the simultaneous optimization of the inventory costs and distribution expenses, which can be addressed according to the inventory routing problem (IRP). In this study, we present a new transport cost calculation pattern for the IRP based on some real cases. In this pattern, the transportation cost is calculated as a function of the load carried and the distance traveled by the vehicle based on a step cost function. Furthermore, previous methods usually aggregate the inventory and transportation costs to formulate them as a single objective function, but in non-cooperative real-life cases, the inventory-holding costs are paid by retailers whereas the transportation-related costs are paid by the distributor. In this study, we separate these two cost elements and introduce a bi-objective IRP formulation where the first objective is to minimize the inventory-holding cost and the second is minimizing the transportation cost. We also propose an efficient particle representation and employ a multi-objective particle swarm optimization algorithm to generate the non-dominated solutions for the inventory allocation and vehicle routing decisions. Finally, in order to evaluate the performance of the proposed algorithm, the results obtained were compared with those produced using the augmented ε-constraint method, thereby demonstrating the practical utility of the proposed multi-objective model and the proposed solution algorithm.     

A Model for Scheduling Electric Power Generation with Optimal Reliability

High operating costs on the one hand and an ever-increasing dependence of economic and social life on reliable power supplies on the other, require that a balance be established between costs of providing and of withholding a high level of operating supply reliability. Defining the objective as minimizing the overall national cost of electricity generation, this paper presents a combined model for daily scheduling, taking into account the effects of possible supply shortages. The optimization procedure comprises dynamic and heuristic programming routines for cost minimization and stochastic models for treating supply reliability. Some numerical results from a sample application are also presented.     

考虑车辆限速区间的危险品运输网络优化

由于危险品在运输过程中存在极大的危害性,为了降低危险品运输风险,政府可以通过对不同路段设置不同的限速区间来引导危险品运输车辆的路径选择,从而导致不同的运输网络总风险和鲁棒成本。首先基于车辆限速区间的方法,构建了危险品运输网络优化的双层规划模型,上层规划以最大运输网络总风险值最小化为目标,下层规划以危险品运输企业的鲁棒成本最小化为目标;然后,设计了粒子群优化算法求解了该模型;最后,通过两个算例验证了模型和算法的有效性。计算结果表明政府部门运用车辆限速区间的方法不仅能够非常有效地降低危险品运输网络总风险,而且更具有鲁棒性和现实可操作性。     

A particle swarm optimization-based hybrid algorithm for minimum concave cost network flow problems

Traditionally, minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in real applications. Some advanced local search algorithms have been developed to solve concave cost bipartite network problems. These have been found to be more effective than the traditional linear approximation methods and local search methods. Recently, a genetic algorithm and an ant colony system algorithm were employed to develop two global search algorithms for solving concave cost transshipment problems. These two global search algorithms were found to be more effective than the advanced local search algorithms for solving concave cost transshipment problems. Although the particle swarm optimization algorithm has been used to obtain good results in many applications, to the best of our knowledge, it has not yet been applied in minimum concave cost network flow problems. Thus, in this study, we employ an arc-based particle swarm optimization algorithm, coupled with some genetic algorithm and threshold accepting method techniques, as well as concave cost network heuristics, to develop a hybrid global search algorithm for efficiently solving minimum cost network flow problems with concave arc costs. The proposed algorithm is evaluated by solving several randomly generated network flow problems. The results indicate that the proposed algorithm is more effective than several other recently designed methods, such as local search algorithms, genetic algorithms and ant colony system algorithms, for solving minimum cost network flow problems with concave arc costs.     

Hybrid flexible flowshop problems: Models and solution methods

This paper considers the problem of hybrid flowshop scheduling. First, we review the shortcoming of the available model in the literature. Then, four different mathematical models are developed in form of mixed integer linear programming models. A complete experiment is conducted to compare the models for performance based on the size and computational complexities. Besides the models, the paper proposes a novel hybrid particle swarm optimization algorithm equipped with an acceptance criterion and a local search heuristic. The features provide a fine balance of diversification and intensification capabilities for the algorithm. Using Taguchi method, the algorithm is fine tuned. Then, two numerical experiments are performed to evaluate the performance of the proposed algorithm with three particle swarm optimization algorithms available in the scheduling literature and one well-known iterated local search algorithm in the hybrid flowshop literature. All the results show the high performance of the proposed algorithm.