A step-by-step dual cycle sequencing method for unit-load automated storage and retrieval systems

The sequencing of requests in an automated storage and retrieval system was the subject of many studies in literature. However, these studies assumed that the locations of items to be stored and retrieved are known and the sequencing problem consisted in determining a route of minimal travel time between these locations. In reality, for a retrieval request, an item can be in multiple locations of the rack and so there is a set of locations associated with this item and not only one predetermined location in the rack. In this paper, we deal with the sequencing problem where a required product can be in several rack locations and there is a set of empty locations. Consequently, the retrieval and storage locations are not known a priori. We sequence by the minimum travel time of a double cycle (DC). An optimization method working step-by-step is developed to determine for each DC and according to storage and retrieval requests, the location of the item to be stored and the location of the item to be retrieved allowing the minimum DC time. The storage requests are processed in FCFS and retrieval requests retrievals requests are gathered by block according to wave sequencing.     

A step-by-step dual cycle sequencing method for unit-load automated storage and retrieval systems

The sequencing of requests in an automated storage and retrieval system was the subject of many studies in literature. However, these studies assumed that the locations of items to be stored and retrieved are known and the sequencing problem consisted in determining a route of minimal travel time between these locations. In reality, for a retrieval request, an item can be in multiple locations of the rack and so there is a set of locations associated with this item and not only one predetermined location in the rack. In this paper, we deal with the sequencing problem where a required product can be in several rack locations and there is a set of empty locations. Consequently, the retrieval and storage locations are not known a priori. We sequence by the minimum travel time of a double cycle (DC). An optimization method working step-by-step is developed to determine for each DC and according to storage and retrieval requests, the location of the item to be stored and the location of the item to be retrieved allowing the minimum DC time. The storage requests are processed in FCFS and retrieval requests retrievals requests are gathered by block according to wave sequencing.     

多模式自动化存取系统设备调度与任务分配

多模式自动化存取系统是一种新型仓储系统,其存储空间利用率高、设备配置灵活.为提高作业效率,降低运行成本,需对系统进行设备调度和任务分配,实现按需配置和管理系统资源.通过将系统的作业过程建模为多个服务中心组成的排队网络,对服务中心设备数量进行调度,保证系统稳态运行.基于预期作业时间设计具有两级模糊规则的模糊控制器,为托盘物资上下架任务分配托盘提升机,实现托盘物资上下架任务的优化分配,减少作业过程所需的运输路径,从而提高作业效率,减少作业叉车数量,降低作业成本.文中通过一个托盘物资上架任务仿真算例,验证了模糊控制任务分配策略的有效性.     

A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system

An order storage assignment problem (SAP) is to find an effective way to locate products in a warehouse in order to improve the operational efficiency of order picking. Since SAP is an NP-hard problem, many heuristic algorithms have been proposed. Most of previous researches focused on picker-to-parts warehousing systems or automated storage and retrieval systems. However, pick-and-pass systems play an important role for the faster delivery of small and frequent orders of inventory with the rise of e-commerce and e-business in the global supply chain. Two factors lead to idle time of pickers in a pick-and-pass system: picking line imbalance and shortage replenishment of products. This paper develops a genetic based heuristic method to solve SAP for a pick-and-pass system with multiple pickers to determine the appropriate storage space for each product and balance the workload of each picking zone so that the performance of the system can be improved. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm and compare the throughput for different storage assignment methods as well. The results indicate that the proposed heuristic policy outperforms existing assignment methods in a pick-and-pass system.     

自动化仓库货位分配优化问题研究

研究了自动化立体仓库固定货架的货位分配问题。分配货位时需要同时考虑货架稳定性和出入库操作的效率,将这一问题描述为一个组合多目标优化问题,采用遗传算法对这一问题进行了求解,对交叉算子进行了改进,得到的解可兼顾两个优化目标。仿真实验表明这一方法可较好地解决货位分配问题。     

单巷道固定货架路径规划问题的研究

固定货架系统是自动化立体仓库的重要组成部分,仓库运行效率主要取决于对货物进行拣选的控制策略。针对单巷道固定货架系统拣选作业过程,以存取时间最小为目标,构建了含多个约束条件的拣选作业路径规划问题的数学模型。在基本蚁群算法基础上,采取自适应调整算法参数、候选节点集合等改进措施,设计了一种改进的蚁群算法对问题进行求解。仿真实验表明该算法能够很好地解决中大规模的拣选作业问题,全局寻优能力强,收敛速度快。     

Matrix-geometric solution for semi-open queuing network model of autonomous vehicle storage and retrieval system

In this paper, we model the autonomous vehicle storage and retrieval system (AVS/RS) as a semi-open queuing network (SOQN) and apply a matrix-geometric method (MGM) for analyzing it. An AVS/RS is an automated material handling system for the high-rise pallet storage area of a warehouse and allows pallets to be stored and retrieved quickly and efficiently from their storage locations. It is an alternative to the traditional crane-based AS/RS (automated storage and retrieval system). A combination of lifts and autonomous vehicles store pallets into and retrieve them out of their respective rack storage locations. The crane based AS/RS typically utilizes aisle-captive, mast-mounted cranes that can access any storage location in an aisle via horizontal movement of the mast and vertical movement of the crane on the mast.In an SOQN, it is assumed that an arriving job or customer is paired with another device and the two visit all the stations that must process the job in the appropriate sequence. After all operations are completed on the job, it exits the system, but the device returns back to a device pool and awaits the next customer. Sometimes a job may have to wait for a device to arrive at the pool or a device may have to wait for a job to arrive. Although closed queuing networks (CQNs) and open queuing networks (OQNs) model systems that require pairing of an incoming job with a device, unlike the SOQN, they ignore the time that a device waits for a job or the time that a job waits for a device.In the context of an AVS/RS, the jobs correspond to storage/retrieval (S/R) transaction requests and the autonomous vehicles (AVs) correspond to the devices. Because an AV may sometimes have to wait for an S/R transaction or vice versa, we model the AVS/RS as an SOQN. We build the queuing network by deriving general travel times of pre-defined servers. We model the AVS/RS system as a single-class, multiple-server, SOQN. Then, we solve the network using the MGM and obtain its key performance measures. We apply the MGM technique for solving the SOQN model to a warehouse in France that uses AVS/RS.     

Scheduling the truckload operations in automated warehouses with alternative aisles for pallets

In this study, the scheduling of truck load operations in automated storage and retrieval systems is investigated. The problem is an extension of previous ones such that a pallet can be retrieved from a set of alternative aisles. It is modelled as a flexible job shop scheduling problem where the loads are considered as jobs, the pallets of a load are regarded as the operations, and the forklifts used to remove the retrieving items to the trucks are seen as machines. Minimization of maximum loading time is used as the objective to minimize the throughput time of orders and maximize the efficiency of the warehouse. A priority based genetic algorithm is presented to sequence the retrieving pallets. Permutation coding is used for encoding and a constructive algorithm generating active schedules for flexible job shop scheduling problem is applied for decoding. The proposed methodology is applied to a real problem arising in a warehouse installed by a leading supplier of automated materials handling and storage systems.     

Positioning of goods in a cross-docking environment

Cross docking is one of the options to reduce lead times and inventories and to improve customer response time in supply chains. Cross-docking centres are dynamic environments where products arrive, are regrouped, and leave the same day. In this paper we focus on the process of short-term storage of unit-loads in a cross-docking environment. The goal is to determine temporary storage locations for incoming unit loads such that the travel distances of the forklift trucks with these unit loads are minimised. We model this problem as a novel application of the minimum cost flow problem and show the applicability of the model for different types of layouts and priorities. We demonstrate both the efficiency and effectiveness of the method in the operational and design phase at cross-docking environments by applying it to practice-oriented examples. Furthermore, we show that the approach is superior to a commonly used heuristic method.     

A hybrid case-GA-based decision support model for warehouse operation in fulfilling cross-border orders

The decision-making process is one of the complicated processes involved in warehouse operation for efficiently fulfilling various specific customer orders. This is especially true if the orders require cross-border delivery activities, such as palletization of the delivery goods according to regulation requirements. Case-based reasoning is an intelligent method for complex problem solving that uses past cases to find a solution to new problems. To achieve an appropriate solution, retrieving useful prior cases effectively for the problem is essential. However, current case retrieval methods are mainly based on a fixed set of attributes for different type of orders in which specific order features for case groups are neglected. In this paper a hybrid approach called the case-genetic algorithm-based decision support model (C-GADS), is proposed in classifying new customer orders into case groups with the highest similarity value, allowing for effectively selecting the most similar cases among the group. The proposed model also suggests the types of features considered in each case group. It helps enhance the effectiveness of formulating warehouse order operations based on grouping similar cases. To validate the feasibility of the proposed model, a case study is conducted and the results show that planning effectiveness is enhanced.     

多巷道固定货架拣选作业优化问题的研究

拣选作业的效率直接影响自动化立体仓库系统的整体效益.为满足客户货单动态变化的需求,分析了自动化仓库单存/取机对多巷道固定货架拣选操作的工作特点,构建了含装箱约束条件的多目标货物拣选路径问题的数学模型,并提出一种带选择算子、插入点操作和动态自适应调整算法参数的改进蚁群算法.实验表明,该算法具有较好的全局寻优能力,收敛速度快,是解决货物拣选路径优化问题的有效算法.     

A particle swarm optimization algorithm for the multiple-level warehouse layout design problem

Warehouse operation and management is one of the essential parts of manufacturing and service operations. The warehouse layout problem is a key to warehouse operations. Generally, warehouse layout design models attempt to optimize different objectives such as the orientation of storage racks, the allocation of space among competing uses, the number of cranes, the overall configuration of the facility, etc. The warehousing strategies can be classified as distribution-type, production-type and contract-type warehouse strategies. In this study, a distribution-type warehouse considered that various type products are collected from different suppliers for storing in the warehouse for a determined period and for delivery to different customers. The aim of the study is to design a multiple-level warehouse shelf configuration which minimizes the annual carrying costs. The turnover rates of the products are classified and they are considered while putting/picking them to/from shelves regarding the distances between the shelves and docks. Since proposed mathematical model was shown to be NP-hard, a particle swarm optimization algorithm (PSO) as a novel heuristic was developed for determining the optimal layout.     

Securing distributed data storage and retrieval in sensor networks

Sensor networks have been an attractive platform for pervasive computing and communication. However, they are vulnerable to attacks if deployed in hostile environments. The past research of sensor network security has focused on securing information in communication, but how to secure information in storage has been overlooked. Meanwhile, distributed data storage and retrieval have become popular for efficient data management in sensor networks, which renders the absence of schemes for securing stored information to be a more severe problem. Therefore, we propose three evolutionary schemes, namely, the simple hash-based (SHB) scheme, the enhanced hash-based (EHB) scheme, and the adaptive polynomial-based (APB) scheme, to deal with the problem. All the schemes have the properties that only authorized entities can access data stored in the sensor network, and the schemes are resilient to a large number of sensor node compromises. The EHB and the APB schemes do not involve any centralized entity except for a few initialization or renewal operations, and thus support secure, distributed data storage and retrieval. The APB scheme further provides high scalability and flexibility, and hence is the most suitable among the three schemes for real applications. The schemes were evaluated through extensive analysis and TOSSIM-based simulations.     

A genetic algorithm to solve the storage space allocation problem in a container terminal

In this paper, an efficient genetic algorithm (GA) is presented to solve an extended storage space allocation problem (SSAP) in a container terminal. The SSAP is defined as the temporary allocation of the inbound/outbound containers to the storage blocks at each time period with aim of balancing the workload between blocks in order to minimize the storage/retrieval times of containers. An extended version of a SSAP proposed in the literature is considered in this paper in which the type of container affects on making the decision on the allocation of containers to the blocks. In real-world cases, there are different types (as well as different sizes) of containers consisting of several different goods such as regular, empty and refrigerated containers. The extended SSAP is solved by an efficient GA for real-sized instances. Because of existing the several equality constraints in the extended model, the implementation of the GA in order to quick and facilitate achieve to the feasible solutions is one of the outstanding advantages of this paper. The performance of the extended model and proposed GA is verified by a number of numerical examples.     

Recency-based storage assignment and warehouse configuration for recurrent demands

Storage assignment is an important decision problem in warehouse operation management. In conventional problem settings of distribution warehouses, stock items are stored in bulk but retrieved in small quantities. Storage assignment methods typically make use of demand attribute information of order quantity, order frequency and correlation between demands. In this paper, we address a different problem in which the request for the same stock items is stochastically recurrent. The problem arises when the items are needed in production and, after production, are returned to warehouses for later reuse. Examples of such items include tooling in factory, books in library and digital objects in data warehouses. Utilizing the recurrent characteristics, a salient recency-based storage assignment policy and an associated cascaded warehouse configuration are proposed and analyzed in this paper. This paper has four parts. In the first part, a model of recurrent demand is described. In the second part, the efficiency of the recency-based policy and a traditional ID-based policy is analyzed. In the third part, a mathematical programming model for optimal configuration of cascaded warehouses is presented. Finally, a case study of hospital visits is presented. This paper concludes with recommendations on cascading and zoning the warehouse for applying the recency-based policy.     

大规模云存储系统副本布局研究

在基于副本冗余机制的大规模云存储系统中,以往的副本布局算法只能部分地满足副本布局中高可靠、高可扩展并且高效的要求,针对这一问题,提出了一种基于分组和一致性哈希的副本布局算法。首先,将关联性高的存储节点进行分组;然后,通过改进的一致性哈希算法将同一数据对象的多个副本分别分配到不同分组中;最后,再通过改进的一致性哈希算法将分配到各分组的数据副本放置在组内对应的存储节点上。理论分析可知,该方法大大提高数据的可靠性。仿真结果表明,该算法能满足副本布局的均衡性、自适应性要求,并能在几十微秒内完成副本定位。     

MEMS based storage architecture for relational databases

Due to recent advances in semiconductor manufacturing, the gap between main memory and disks is constantly increasing. This leads to a significant performance bottleneck for Relational Database Management Systems. Recent advances in nanotechnology have led to the invention of MicroElectroMechanical Systems (MEMS) based storage technology to replace disks. In this paper, we exploit the physical characteristics of MEMS-based storage devices to develop a placement scheme for relational data that enables retrieval in both row-wise and column-wise manner. We develop algorithms for different relational operations based on this data layout. Our experimental results and analysis demonstrate that this data layout not only improves I/O utilization, but results in better cache performance for a variety of different relational operations. This research is supported by the NSF grants under IIS-0220152 CNF-0423336, and EIA 00-80134     

大型压缩数据仓库上的Iceberg Cube算法

Iceberg Cube操作是OLAP(on-line analysis processing)分析中的一种重要操作.数据压缩技术在有效减小数据仓库所需的数据空间和提高数据处理性能方面的作用越来越明显.在压缩的数据仓库上,如何快速、有效地计算Iceberg Cube是目前亟待解决的问题.简要介绍了数据仓库的压缩,然后给出了在压缩数据仓库中计算Iceberg Cube的算法.实验结果表明,该算法的性能优于先在压缩数据上计算Cube再检查having条件这种方法.     

嵌入式数字视频监控系统通用存储与检索方案

为了解决现有的嵌入式数字视频监控系统的多路视频存储与检索的结构差异,从而严重制约视频监控系统的可复用性与引起系统I/O瓶颈问题,提出一种嵌入式数字视频监控系统通用存储与检索方案.采用组件化思想构建视频存储与视频检索模块,通过设计存储调度算法减少了I/O瓶颈问题,使用检索控制策略降低了检索拥塞.与以前的系统相比在支持多路视频存储与检索方面表现出改进的性能,同时具有更好的可重用性.     

Parallel variable neighborhood search for the min–max order batching problem

Warehousing is a key part of supply chain management. It primarily focuses on controlling the movement and storage of materials within a warehouse and processing the associated transactions, including shipping, receiving, and picking. From the tactical point of view, the main decision is the storage policy, that is, to decide where each product should be located. Every day a warehouse receives several orders from its customers. Each order consists of a list of one or more items that have to be retrieved from the warehouse and shipped to a specific customer. Thus, items must be collected by a warehouse operator. We focus on situations in which several orders are put together into batches, satisfying a fixed capacity constraint. Then, each batch is assigned to an operator, who retrieves all the items included in those orders grouped into the corresponding batch in a single tour. The objective is then to minimize the maximum retrieving time for any batch. In this paper, we propose a parallel variable neighborhood search algorithm to tackle the so‐called min–max order batching problem. We additionally compare this parallel procedure with the best previous approach. Computational results show the superiority of our proposal, confirmed with statistical tests.