数字化仓储中货位优化分配的研究

货位分配数字化仓储的关键环节,合理的货位分配能够大大地提高出入库效率,降低仓储系统成本。传统的仓库管理系统根据基于经验的固定货物出入库频率设计货位分配策略,而企业的发展和市场的变化导致货物出库频率是一个动态变化的过程。市场的变化导致传统仓库管理系统的货位分配方案无法满足要求的现实情况,提出把RFID技术运用到数字化仓库结合相关算法中对产品入库货位进行动态优化来提高仓库的周转效率。     

智能化立体仓库入库货位分配的综合优化算法

智能化立体仓库对现代企业提高仓储利用率和生产效率具有重要作用。介绍了秦皇岛港务集团公司立体仓库管理系统中仓库货位计算机分配的智能化算法,该算法充分考虑了重量均匀分布、就近货位选择、备件品种均匀分布、备件使用率、货位空置时间等因素,根据整个仓库的当前状态计算入库货位,为入库货位的选择提供辅助决策。实际项目运行表明,所提的综合货位优化分配算法是有效的,对提高仓库的安全性、入库工作效率等具有非常重要的意义.     

基于免疫遗传算法的智能柔性仓库货位自动化分配方法

针对没有充分考虑到货物就近存储、分配不均、入库效率低等的问题,为了有效地提高工作效率,提高空间利用率,设计了一个基于免疫遗传算法的智能柔性仓库货位自动化分配方法。将保证上轻下重的货架承重原则、先入后出的目标、关联性货物就近存储作为分配目标,采用免疫遗传算法不断迭代计算,赋予其自动化属性,对各层的个体给予同样的虚拟适度值,实现智能柔性仓库货位自动化分配。结果表明,所提出方法在应用后,能够将较重货物分配在底层,提高算法搜索效率,保证入库时间较短,满足智能柔性仓库自动化运维模式需求。     

基于嵌套分区算法的立体仓库货位分配优化

根据小型立体化仓库运营特点,基于顺序单目标优化思想,提出一种新的仓库货位分配策略。将考虑存储能耗、货架稳定性、运行效率的多目标仓库货位优化问题,转化为单目标优化,建立了仓库货位优化数学模型。根据数学模型特点,采用嵌套分区算法进行优化求解。通过算例分析证明该分配策略与优化方法,可有效处理多目标仓库库位优化问题,优化效果显著。     

自动化立体仓库存储管理策略的优化

提出立体仓库存储分配最优目标模型，给出货位管理的优化原则和具体优化算法．并提供一种便于仓库管理的数据结构。     

自动化立体仓库存储管理策略的优化

提出立体仓库存储分配最优目标模型，给出货位管理的优化原则和具体优化算法，并提供一种仓库管理的数据结构。     

遗传算法在立体仓库货位优化分配中的研究

存储一定数量货物的自动化仓库中,以基于随机存储策略的库区和货位分配以及堆垛机行驶时间为优化控制目标,针对自动化立体仓库的库区和货位的分配策略问题进行了讨论,提出立体仓库的库区优化数学模型。在库区优化基础上,进一步提出货位优化数学模型,将Pareto最优解的概念与遗传算法相结合,提出了一种解决多目标优化问题的Pareto遗传算法解决货位优化问题,给出了仿真实验及分析。结果表明采用遗传算法优化策略可以有效地解决自动化立体仓库的货位优化分配问题。     

自动化立体仓库货位分配与作业调度集成优化

针对动态提高单载具堆垛机式自动化立体仓库拣选效率的问题,文中提出了一种基于共享货位存储与动态订单拣选策略下的货位分配与作业调度集成优化方法。将动态移库优化扩展到仓库的整个拣选生命周期,建立以双指令循环下堆垛机拣选任务所需的总作业时间最短为评价目标的数学模型,提出了一种基于K-Medoids聚类的粒子群优化(Particle Swarm Optimization,PSO)算法,用K-Medoids算法通过产品与订单的相关性进行初始货位的聚类分析,筛除劣质解的货位范围,并在K-Medoids聚类算法生成的解类簇基础上获得精确解。实验结果表明,考虑动态移库可以使仓库拣选效率提高20%,且该算法与传统PSO算法相比求解时间下降66%左右。     

基于混合果蝇优化算法的选址-库存联合优化策略

针对装备维修保障仓库系统运营费用高、仓库点位布局不合理、备件库存结构不合理等问题,建立以多品种联合补货问题为基础的装备维修备件仓库选址-库存控制决策联合优化模型,模型可用于求解仓库的开设位置、维修活动需求点的指派情况、仓库补货时间以及库存水平等.根据模型的结构特点,利用多种群协同进化的方法改进传统果蝇优化算法的位置更新方式,设计一种内外两层搜索策略的混合果蝇优化算法,外层搜索策略作为算法的主程序用于搜索仓库选址决策变量,内层搜索策略采用改进的RAND算法用于搜索库存控制决策变量.仿真结果表明,混合果蝇优化算法具有良好的求解效率,能够确保库存系统在一定服务水平的基础上有效降低库存运营总成本.     

基于遗传算法的药品仓库货位布局优化研究

在研究遗传算法的基础上,结合某制药企业仓储管理的环境现状,提出一种有效的药品仓库货位布局算法,以实现货位的自动化分配并提高仓库的空间利用率和出/入库效率.     

采用自适应优化权重的出库货位优化方法研究

当前出库货位优化研究对货物生产日期的考虑大多数仅仅是简单地使用先进先出原则,如何更合理考虑货物生产日期对货位优化的影响,是亟待解决的问题。针对此问题,提出以出库代价和货物剩余价值率为优化因素的货位优化模型,并采用基于自适应算子、精英策略和灾变算子的改进遗传算法结合基于仓库繁忙度的自适应优化权重对出库货位优化模型进行求解。采用企业实际生产数据进行验证,实验结果表明采用改进遗传算法的出库货位优化算法效果更优,并且使用基于仓库繁忙度的自适应优化权重,能够有效降低货物因过久存放而造成货物价值下降的风险同时又能在仓库繁忙时优先考虑出库效率。     

供应链环境下的装备多级库存优化研究

研究了一个战役级、多个战术级仓库组成的树型多级库存系统,建立了战役级仓库分别采用等量等间隔、不等量等间隔订货策略的模型。采用自适应遗传算法实现了模型的求解,并用实例验证了算法的有效性。     

低层人工拣货仓库货位优化问题研究

在现代物流总成本中,仓储成本占很大比重,合理的储位分配是提高仓储拣选效率,降低仓储成本的核心所在。通过对低层人工拣货仓库的出库过程分析,同时考虑商品热销程度、商品之间的关联关系及货架位置等影响因素,设计了基于社区划分的货位优化算法。首先,根据商品关联性构建无向有权网络,并采用社区划分算法进行多次划分;然后,以社区为单位存放到货架,并通过调整阶段补齐货架;最后根据评估指标从多个方案中选出最优方案。评估指标根据缩短行走路径、缓解堵塞和减少拣选人员数量3个优化目标构建。实验结果表明,提出的货位优化算法无论是时间消耗还是货位摆放方案质量,与其他对比方案相比均具有显著优势。     

Pareto遗传算法在货位配置中的应用研究

固定货架是自动化立体仓库应用最广泛的存储设备，货位配置是否优化直接影响货架的稳定性和存取操作效率。因此，建立了货位配置优化问题的数学模型，提出了采用Pareto遗传算法解决多目标组合优化问题，可得到Pareto最优解集。此算法包括5个基本算子：选择、变异、交叉、小生境技术、Pareto集合过滤器。通过仿真实验验证了将Pareto遗传算法应用在实际货位配置优化问题中，取得了较好的结果。     

具有时变需求的配送型多级库存优化研究

考虑到战术仓库的需求率具有时变性的特点,应用供应链管理的思想研究了一个战役仓库、多个战术仓库组成的配送型多级库存优化问题,建立了战役仓库采用等量订货策略,战术仓库分别采用等间隔、等量订货策略时的模型,并采用自适应遗传算法实现了模型的求解,实例验证了模型及算法的有效性.实例表明：战役仓库采用等量订货策略,战术仓库采用等间隔订货策略时总费用最低.     

Particle swarm optimization for bitmap join indexes selection problem in data warehouses

Data warehouses are very large databases usually designed using the star schema. Queries defined on data warehouses are generally complex due to join operations involved. The performance of star schema queries in data warehouses is highly critical and its optimization is hard in general. Several query performance optimization methods exist, such as indexes and table partitioning. In this paper, we propose a new approach based on binary particle swarm optimization for solving the bitmap join index selection problem in data warehouses. This approach selects the optimal set of bitmap join indexes based on a mathematical cost model. Several experiments are performed to demonstrate the effectiveness of the proposed method on the bitmap join index selection problem. Further testing of the method is performed using a database environment specific cost function. The binary particle swarm optimization is found to be more effective than both the genetic algorithm and data mining based approaches.     

Developing a multi-objective,multi-item inventory model and three algorithms for its solution

We develop a multi-objective model in a multi-product inventory system.The proposed model is a joint replenishment problem(JRP) that has two objective functions.The first one is minimization of total ordering and inventory holding costs,which is the same objective function as the classic JRP.To increase the applicability of the proposed model,we suppose that transportation cost is independent of time,is not a part of holding cost,and is calculated based on the maximum of stored inventory,as is the case in many real inventory problems.Thus,the second objective function is minimization of total transportation cost.To solve this problem three efficient algorithms are proposed.First,the RAND algorithm,called the best heuristic algorithm for solving the JRP,is modified to be applicable for the proposed problem.A multi-objective genetic algorithm(MOGA) is developed as the second algorithm to solve the problem.Finally,the model is solved by a new algorithm that is a combination of the RAND algorithm and MOGA.The performances of these algorithms are then compared with those of the previous approaches and with each other,and the findings imply their ability in finding Pareto optimal solutions to 3200 randomly produced problems.     

结合批量问题的多目标矩形件优化排样

设计多目标启发式进化算法,研究了一种考虑批量问题的二维矩形件排样问题,建立了含有原材料成本最小化和零件库存成本最小化的多目标优化模型。先用启发式算法初始化下料方式,再用改进的快速非支配排序算法进行优化求解,确定下料方案。通过实验结果以及与其他算法的对比表明,在中等规模的矩形件排样问题中,该算法能够在较快的时间内既保证较高的原料利用率,又能降低该问题的总成本,证明了该算法的有效性。     

A location–inventory supply chain problem: Reformulation and piecewise linearization

In this paper, we study a two-echelon inventory management problem with multiple warehouses and retailers. The problem is a natural extension to the well-known one-warehouse multi-retailer inventory problem. The problem is formulated as a mixed integer non-linear program such that its continuous relaxation is non-convex. We propose an equivalent formulation with fewer non-linear terms in the objective function so that the continuous relaxation of the new model is a convex optimization problem. We use piecewise linearization to transform the resulting MINLP to a mixed integer program and we solve it using CPLEX. Through numerical experiments, we compare the solutions obtained by solving the new formulation using CPLEX with two previously published Lagrangian relaxation based heuristics to solve the original mixed integer non-linear program. We demonstrate that the new approach is capable of providing almost the same solutions without the need of using specialized algorithms. This important contribution further implies that additional variants of the problem, such as multiple products, capacitated warehouses and routing, can be added to result in a problem that will again be solvable by commercial optimization software, while the respective Lagrangian heuristics will fail to solve such variants or extended problems.     

A genetic algorithm approach for location-inventory-routing problem with perishable products

In this paper, we address a location-inventory-routing model for perishable products. The model determines the number and location of required warehouses, the inventory level at each retailer, and the routes traveled by each vehicle. The proposed model adds location decisions to a recently published inventory routing problem in order to make it more practical, thus supporting the prevalent claim that integration of strategic, tactical and operational level decisions produces better results for supply chains. Given that the model developed here is NP-hard, with no algorithm capable of finding its solution in polynomial time, we develop a Genetic Algorithm approach to solve the problem efficiently. This approach achieves high quality near-optimal solutions in reasonable time. Furthermore, the unique structure of the problem requires developing a new chromosome representation, as well as local search heuristics. Finally, an analysis is carried out to verify the effectiveness of the algorithm.