Ant colony optimization algorithm to solve for the transportation problem of cross-docking network

This paper addresses the transportation problem of cross-docking network where the loads are transferred from origins (suppliers) to destinations (retailers) through cross-docking facilities, without storing them in a distribution center (DC). We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and then route them either directly to the customers or indirectly to cross-docking facilities so the loads can be consolidated. For generating a truck operating plan in this type of distribution network, the problem was formulated using an integer programming (IP) model and solved using a novel ant colony optimization (ACO) algorithm. We solved several numerical examples for verification and demonstrative purposes and found that our proposed approach finds solutions that significantly reduce the shipping cost in the network of cross-docks and considerably outperform Branch-and-Bound algorithm especially for large problems.     

A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment

One of the most important problem in supply chain management is the design of distribution systems which can reduce the transportation costs and meet the customer's demand at the minimum time. In recent years, cross-docking (CD) centers have been considered as the place that reduces the transportation and inventory costs. Meanwhile, neglecting the optimum location of the centers and the optimum routing and scheduling of the vehicles mislead the optimization process to local optima. Accordingly, in this research, the integrated vehicle routing and scheduling problem in cross-docking systems is modeled. In this new model, the direct shipment from the manufacturers to the customers is also included. Besides, the vehicles are assigned to the cross-dock doors with lower cost. Next, to solve the model, a novel machine-learning-based heuristic method (MLBM) is developed, in which the customers, manufacturers and locations of the cross-docking centers are grouped through a bi-clustering approach. In fact, the MLBM is a filter based learning method that has three stages including customer clustering through a modified bi-clustering method, sub-problems’ modeling and solving the whole model. In addition, for solving the scheduling problem of vehicles in cross-docking system, this paper proposes exact solution as well as genetic algorithm (GA). GA is also adapted for large-scale problems in which exact methods are not efficient. Furthermore, the parameters of the proposed GA are tuned via the Taguchi method. Finally, for validating the proposed model, several benchmark problems from literature are selected and modified according to new introduced assumptions in the base models. Different statistical analysis methods are implemented to assess the performance of the proposed algorithms.     

A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems

Cross-docking is a material handling and distribution technique in which products are transferred directly from the receiving dock to the shipping dock, reducing the need for a warehouse or distribution center. This process minimizes the storage and order-picking functions in a warehouse. In this paper, we consider cross-docking in a supply chain and propose a multi-objective mathematical model for minimizing the make-span, transportation cost and the number of truck trips in the supply chain. The proposed model allows a truck to travel from a supplier to the cross-dock facility and from the supplier directly to the customers. We propose two meta-heuristic algorithms, the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO), to solve the multi-objective mathematical model. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a numerical example. The numerical results show the relative superiority of the NSGA-II method over the MOPSO method.     

供应链环境下带越库配送的多配送中心车辆路径问题

从零售业纵向供应链管理入手,考虑由供应商、零售商和多个配送中心构成的城市物流协同配送网络,研究带有越库配送的多配送中心车辆路径问题.分析越库配送的实施要求和操作准则,将配送过程分为集货、送货阶段.应对产品种类多样化需求,设置集货过程车辆协同作业返回配送中心,送货过程需求可拆分的运作机制.以最小化车辆运输成本和操作成本为目标,建立多配送中心车辆路径问题优化模型.针对模型特性设计改进遗传算法进行求解.最后通过仿真实例验证模型的可行性和算法的有效性, 结果表明,越库配送模式能有效服务城市区域零售门店的及时供货,在配送时间和运输成本方面具有显著优势.     

考虑随机旅行时间与二维装载约束的越库配送车辆路径优化

面向越库配送模式下二维装载和车辆路径联合优化,考虑现实配送过程的不确定性因素,提出考虑随机旅行时间和二维装载约束的越库配送车辆路径问题.基于蒙特卡洛模拟与场景分析方法,建立以运输成本、车辆固定成本以及时间窗期望惩罚成本之和最小化为目标的带修正随机规划模型.继而根据问题特征,设计改进的自适应禁忌搜索算法和基于禁忌搜索的多重排序最佳适应装箱算法进行求解.其中,改进的自适应禁忌搜索算法在禁忌搜索算法的基础上引入自适应机制,对不同邻域算子进行动态选择,并提出基于移除-修复策略的多样性机制以增强算法的寻优能力.数值实验表明,所提出的模型与方法能够有效求解考虑随机旅行时间和二维装载约束的越库配送车辆路径问题,自适应与多样性机制能一定程度上增强算法的全局搜索能力.     

供应链环境下带越库配送的车辆路径问题

从零售业纵向供应链整合入手,考虑供应商、零售商和配送中心构成的协同配送网络,研究带越库配送的车辆路径问题。分析越库配送实施要求和操作准则,设置协同到达作业时间,将配送过程分为集货、分拣和送货三个阶段,建立最小化车辆运输成本和固定成本为目标的越库配送路径优化模型。考虑模型的复杂性,设计改进遗传算法进行求解。最后由仿真实例验证模型可行性和算法有效性。结果表明,越库配送模式能有效服务城市区域零售门店的及时供货情况,在配送时间和运输成本方面有着显著优势。     

A matheuristic based on large neighborhood search for the vehicle routing problem with cross-docking

The vehicle routing problem with cross-docking (VRPCD) consists in defining a set of routes that satisfy transportation requests between a set of pickup points and a set of delivery points. The vehicles bring goods from pickup locations to a cross-docking platform, where the items may be consolidated for efficient delivery. In this paper we propose a new solution methodology for this problem. It is based on large neighborhood search and periodically solving a set partitioning and matching problem with third-party solvers. Our method improves the best known solution in 19 of 35 instances from the literature.     

A real-time scheduling framework for embedded systems with environmental energy harvesting

Real-time scheduling refers to the problem in which there is a deadline associated with the execution of a task. In this paper, we address the scheduling problem for a uniprocessor platform that is powered by a renewable energy storage unit and uses a recharging system such as photovoltaic cells. First, we describe our model where two constraints need to be studied: energy and deadlines. Since executing tasks require a certain amount of energy, classical task scheduling like earliest deadline is no longer convenient. We present an on-line scheduling scheme, called earliest deadline with energy guarantee (EDeg), that jointly accounts for characteristics of the energy source, capacity of the energy storage as well as energy consumption of the tasks, and time. In order to demonstrate the benefits of our algorithm, we evaluate it by means of simulation. We show that EDeg outperforms energy non-clairvoyant algorithms in terms of both deadline miss rate and size of the energy storage unit.     

A sequential priority-based heuristic for scheduling material handling in a satellite cross-dock

In a less-than-truckload logistic network, the satellite cross-dock is in charge of local deliveries. These terminals operate in two separate shifts: consolidating pickup freight for overnight shipments and processing received products for early morning deliveries. Satellite cross-docks are flexible when scheduling trucks and where the priority is to minimize handling cost. In this paper, we formalize cross-docking process by presenting a mathematical model. We develop a sequential priority-based heuristic algorithm to deal with practical problems. Numerical results show the stability of the heuristic method for fairly large size problems.     

Vehicle routing scheduling for cross-docking in the supply chain

One of the most important factors in implementing supply chain management is to efficiently control the physical flow of the supply chain. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. In various methods, cross-docking is considered a good method to reduce inventory and improve responsiveness to various customer demands. However, previous studies have dealt mostly with the conceptual advantages of cross-docking or actual issues from the strategic viewpoint. It is also necessary, however, to considering cross-docking from an operational viewpoint in order to find the optimal vehicle routing schedule. Thus, an integrated model considering both cross-docking and vehicle routing scheduling is treated in this study. Since this problem is known as NP-hard, a heuristic algorithm based on a tabu search algorithm is proposed. In the numerical example, our proposed algorithm found a good solution whose average percentage error was less than 5% within a reasonable amount of time.     

On-line EM algorithm for the normalized gaussian network

A normalized gaussian network (NGnet) (Moody & Darken, 1989) is a network of local linear regression units. The model softly partitions the input space by normalized gaussian functions, and each local unit linearly approximates the output within the partition. In this article, we propose a new on-line EMalgorithm for the NGnet, which is derived from the batch EMalgorithm (Xu, Jordan, &Hinton 1995), by introducing a discount factor. We show that the on-line EM algorithm is equivalent to the batch EM algorithm if a specific scheduling of the discount factor is employed. In addition, we show that the on-line EM algorithm can be considered as a stochastic approximation method to find the maximum likelihood estimator. A new regularization method is proposed in order to deal with a singular input distribution. In order to manage dynamic environments, where the input-output distribution of data changes over time, unit manipulation mechanisms such as unit production, unit deletion, and unit division are also introduced based on probabilistic interpretation. Experimental results show that our approach is suitable for function approximation problems in dynamic environments. We also apply our on-line EM algorithm to robot dynamics problems and compare our algorithm with the mixtures-of-experts family.     

Crossdock truck scheduling with time windows: earliness,tardiness and storage policies

This article proposes to simultaneously plan inbound and outbound truck arrivals and departures in a cross-docking platform, as well as the internal pallet handling. The objective is to minimize both the total number of pallets put in storage and the dissatisfaction of the transportation providers, by creating a truck schedule as close as possible to the wished schedule they communicate in advance. The problem is modeled with an integer program tested on generated instances to assess its performance, especially regarding the computation time. The problem is proven to be np-hard in the strong sense. Since the execution takes too long to be used on a daily basis by platform managers, three heuristics are also proposed and tested. Two are based on integer programs solved sequentially, the third one is a tabu search in which the storage part of the objective function is evaluated by a maximum flow model in a graph. Numerical experiments show in which conditions each heuristic performs best, which can help choosing a solution method when confronted to a real-life problem.     

ADL-RAID: Energy-Saving Data Layout for Dynamic Loads

With the rapid development of information technology and the explosive growth of data, the scale of storage equipment is growing rapidly. The high energy consumption storage devices have become a serious problem for data centers. For data storage, the model of sequential data access a storage system is commonly used. Aiming at the characteristics of a sequential data storage system, this paper, we propose a structure, named Adapted to Dynamic Load based on Redundant Array Independent Disks (ADL-RAID) which is an effective energy-saving data layout for dynamic loads based on the existing Semi-Redundant Array Independent Disks (S-RAID). ADL-RAID inherits the local parallel energy-saving strategy, uses an address mapping mechanism, and allocates storage space to satisfy the performance requirements for the user requested application. By sensing different loads, ADL-RAID allocates storage space for its dynamic loads with the appropriate parallelism. One or several data disks are used when the load is minimized, and all the data disks are used in parallel when the load is maximized. Experimental results show that, for 100% continuous write request, ADL-RAID saves 33.6% energy consumption than S-RAID5 and improves write performance than S-RAID5 by 34.3%. Thus, ADL-RAID has higher availability and is ideal for sequential data storage applications.     

融合门限公钥加密和纠删码的安全云存储模型

针对当前云存储系统中的机密性和容错性问题,展开分析和研究.指出目前的主流解决方案往往仅能解决机密性问题或容错性问题中的一个,而不能将二者兼顾起来进行考虑.为此,将门限公钥加密技术与指数纠删码（Erasure Codes over Exponents,EC-E）相融合,设计并提出了一个同时能满足机密性与容错性要求的安全云存储模型（A Secure Cloud Storage Model with Data Confidentiality and Fault-Tolerant,SCSM-DCF）.给出了模型的形式化定义、安全性定义以及实体间通信协议;对模型的性能进行分析,指出其在能够保证安全性的同时,具有较好的代价优势.     

A rule based knowledge transaction model for mobile environments

In this paper, we propose and formalize a rule based knowledge transaction model for mobile environments. Our model integrates the features of both mobile environments and intelligent agents. We use logic programming as a mathematic tool and formal specification method to study knowledge transaction in mobile environments. Our knowledge transaction model has the following major advantages: (1) It can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments. (2) It is knowledge oriented and has a declarative semantics inherited from logic programming. (3) It is a formalization that can be applied to general problem domains. We show that our model can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments.     

A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain

The location and routing scheduling problems with cross-docking can be regarded as new research directions for distribution networks in the supply chain. The aims of these problems are to concurrently design a cross-docking center location and a vehicle routing scheduling model, known as NP-hard problems. This paper presents a two-stage mixed-integer programming (MIP) model for the location of cross-docking centers and vehicle routing scheduling problems with cross-docking due to potential applications in the distribution networks. Then, a new algorithm based on a two-stage hybrid simulated annealing (HSA) with a tabu list taken from tabu search (TS) is proposed to solve the presented model. This proposed HSA not only prevents revisiting the solution but also maintains the stochastic nature. Finally, small and large-scale test problems are randomly generated and solved by the HSA algorithm. The computational results for different problems show that the proposed HSA performs well and converges fast to reasonable solutions.     

文化算法在越库作业调度中的应用

研究只有一个入库门和一个出库门的带有限暂存区的越库中心的作业调度问题。以额外搬运成本、暂存成本和换车成本总和最小化为目标,建立动态规划模型。构建了具有两层进化机制的文化算法对问题进行求解。算法的种群空间采用遗传算法作为进化模式,信度空间接收种群空间的优良个体形成知识并指导遗传算法的选择操作。通过在大、小规模情形下进行数值实验,验证了文化算法的有效性。     

A tabu search approach to the truck scheduling problem with multiple docks and time windows

While organizing the cross-docking operations, cross-dock managers are confronted with many decision problems. One of these problems is the truck scheduling problem. This paper presents a truck scheduling problem that is concerned with both inbound and outbound trucks at multiple dock doors. The objective is to minimize the total travel time and the total tardiness. The truck scheduling problem under study is described in detail and a mathematical model of the problem is provided which can be solved to optimality with a mixed integer programming solver, at the expense of a high computation time. Next, a tabu search approach is presented. Experimental results on new benchmark instances indicate that the proposed tabu search is able to find good quality results in a short time period, thus offering potential for integration in cross-docking decision support systems.     

基于稀疏编码的鲁棒说话人识别

目前的说话人识别系统在噪声环境下性能将急剧下降,为了解决这一问题,提出一种新的基于稀疏编码的说话人识别方法。该方法用一个通用背景字典(Universal Background Dictionary,UBD)刻画说话人语音的共性,并为每个说话人和环境噪声训练相应的字典来刻画说话人和环境的特殊变化。这些字典拼接成一个大字典,然后将待测试语音稀疏分解在这个大字典上以实现说话人识别。为了提高说话人字典的区分能力,通过从说话人字典中移除与通用背景字典原子相似的原子来优化说话人字典。为了跟踪变化的噪声,采用带噪声的语音在线更新噪声字典。在各种噪声条件下的实验表明,所提出的方法在噪声环境下具有较强的鲁棒性。     

A Coarse to Fine 3D Registration Method Based on Robust Fuzzy Clustering

An important problem in computer vision is to determine how features extracted from images are connected to an existing model. In this paper, we focus on solving theregistrationproblem, i.e., obtaining Euclidean transformation parameters between several 3D data sets, whether partial or exhaustive. The difficulty of this problem is to obtain a method which is robust with respect to outliers and at the same time accurate. We present a general method performing robust 3D localization and fitting based on a fuzzy clustering method. The fuzzy set approach is known for its practical efficiency in uncertain environments. To illustrate the advantages of this approach on the registration problem, we show results on synthetic and real 3D data.