Tree based searching approaches for integrated vehicle dispatching and container allocation in a transshipment hub

This study addresses the integration of Vehicle dispatching and container storage location problem with consideration of loading and unloading activities simultaneously. A MIP model is formulated to describe the interrelation between vehicle scheduling, yard crane scheduling and container storage location. A tree structure is used to represent the whole solution space. This representation has a good property as it captures the neighborhood structure and enhances the performance of local search and adaptive searching algorithms. Three variants of tree based searching approaches are developed, namely, the Nested Partitions method (NP), the Beam Search method (BS), and Stochastic Beam Search method (SBS). Extensive experiments show that these proposed methods can find a promising solution in matter of seconds for a practical problem and the Stochastic Beam Search method (SBS) method performs nearly as well as Nested Partitions method (NP) while gaining great computational efficiency. Due to this merit, SBS method is suggested to solve real time integrated vehicle dispatching problem in a relative large scale and may applied in other real time complex system scheduling.     

Static Analysis Method for Detecting Buffer Overflow Vulnerabilities

In this paper, a new static method for automated detection of vulnerabilities that could result in buffer overflows in programs is suggested. The problem of the software defense against threads related to buffer overflows is very important one. Currently, there does not exist satisfactory approaches to its solution. The existing dynamic methods make it possible to avoid incorrect execution for certain classes of programs. The basic disadvantage of these methods is that the procedure of the error detection after the session of tests is very involved. Moreover, they do not guarantee that the results obtained are correct. Static analysis methods are, as a rule, lexical scanners and do not thoroughly analyze the execution of the program, as well as its memory content (e.g., arrays, variables, and the like). The objective of the suggested method is to improve situation in this field and facilitate the audit of a program code by the programmer.__________Translated from Programmirovanie, Vol. 31, No. 4, 2005.Original Russian Text Copyright © 2005 by Puchkov, Shapchenko.     

Exact methods and a heuristic for the optimization of an integrated replenishment-storage planning problem

In this paper we study the coordination of different activities in a supply chain issued from a real case. Multiple suppliers send raw materials (RMs) to a distribution center (DC) that delivers them to a unique plant where the storage of the RMs and the finished goods is not possible. Then, the finished goods are directly shipped to multiple customers having just‐in‐time (JIT) demands. Under these hypotheses, we show that the problem can be reduced to multiple suppliers and one DC. Afterwards, we analyze two cases; in the first, we consider an uncapacitated storage at DC, and in the second, we analyze the capacitated storage case. For the first case, we show that the problem is NP‐hard in the ordinary sense using the Knapsack decision problem. We then propose two exact methods: a mixed integer linear program (MILP) and a pseudopolynomial dynamic program. A classical dynamic program and an improved one using the idea of Shaw and Wagelmans are given. With numerical tests we show that the dynamic program gives the optimal solution in reasonable time for quite large instances compared with the MILP. For the second case, the capacity limitation in DC is assumed, which makes the problem solving more challenging. We propose an MILP and a dynamic programming‐based heuristic that provides solutions close to the optimal solution in very short times.     

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.     

Load scheduling for multiple quay cranes in port container terminals

This paper proposes a method to schedule loading operations when multiple yard cranes are operating in the same block. The loading scheduling methods in this paper are based on a genetic algorithm and a simulated annealing method, which consider interferences between adjacent yard cranes. It attempts to minimize the make-span of the yard crane operation. We consider the container handling time, the yard crane travel time, and the waiting time of each yard crane, when evaluating the makespan of the loading operation by yard cranes. An encoding method considering the special properties of the optimal solution of the problem is suggested. Numerical experiment was conducted to compare performances of the algorithms suggested in this study. Received: June 2005 / Accepted: December 2005     

Source/sink inventory control for vehicle rental systems

The problem of operating a vehicle rental system in a two-location environment is investigated. Two interdependent processes are identified as operating simultaneously. Units are rented out, ultimately to be returned to their original location. Secondly, units are rented out from one location but returned to a second location. As a result, the number of units of equipment at each location is constantly changing. In general, these “one-way” flows setup equipment imbalances which require periodic rebalancing of the system.Formulation takes the form of a multi-period dynamic program. Both transient and steady-state cases are investigated. Solution specifies the optimal number of units each location should hold on reserve in each period for “one-way” rentals. Finally, a restocking decision is embedded into the problem, in order to determine the optimal quantity of equipment lo be periodically rebalanced between locations due to “one-way” flows.Limitation of the procedure is explained. The size of the resulting dynamic program makes solution of the general multi-location problem computationally unfeasible. Approximation procedures are suggested to circumvent this limitation.     

响应动态约束条件的多目标货位优化算法研究

针对自动化立库货位决策与优化问题,考虑到优化目标多样、托盘使用状态及可分配货位动态变化等因素,提出了一种响应动态约束条件的多目标货位优化算法。以巷道作业均衡、货架重心稳定及作业路径最短建立多目标优化模型,基于变异系数自适应差分进化算法,使用货位随机数编码,根据实时货位可行域进行个体解码,以响应动态货位约束条件。提出了基于层次分析的Pareto解评价方法,从而获得多批作业货位持续优化的目标权重,为仓储货位决策提供合理方案。多批作业算法实验结果表明:所提算法效果显著优于多目标简单加权算法,能够有效应用于动态货位决策与优化。     

Efficient primal-dual heuristic for a dynamic location problem

In this paper the dynamic location problem with opening, closure and reopening of facilities is formulated and an efficient primal-dual heuristic that computes both upper and lower limits to its optimal solution is described. The problem here studied considers the possibility of reconfiguring any location more than once over the planning horizon. This problem is NP-hard (the simple plant location problem is a special case of the problem studied). A primal-dual heuristic based on the work of Erlenkotter [A dual-based procedure for uncapacitated facility location. Operations Research 1978;26:992–1009] and Van Roy and Erlenkotter [A dual-based procedure for dynamic facility location. Management Science 1982;28:1091–105] was developed and tested over a set of randomly generated test problems. The results obtained are quite good, both in terms of the quality of lower and upper bounds calculated as in terms of the computational time spent by the heuristic. A branch-and-bound procedure that enables to optimize the problem is also described and tested over the same set of randomly generated problems.     

Input/output lot sizing in single stage batch production systems under constant demand

This paper develops an integrated input/output lot sizing model under deterministic conditions for a single product manufactured within a batch production environment. Our analysis incorporates the effects of work-in-process inventories resulting from multiple input items that are converted to output at finite rates in a single stage process and encompasses, simultaneously, the lot sizing decisions for the end product, as well as the externally procured input items. The resulting model represents a nonlinear mixed integer optimization problem, for which an efficient heuristic solution technique is suggested.     

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。     

一种求解Job-Shop调度问题的新型蚁群算法

Job-Shop调度问题是一类具有很高理论研究和工程应用价值的问题。针对使用蚁群算法求解Job-Shop调度问题时较难设置合适参数的问题,提出一种动态设置参数的新型蚁群求解算法。分析了蚁群算法中参数对求解结果的影响,给出了算法求解Job-Shop调度问题的关键技术和实现过程。最后对五个基本测试问题进行了仿真实验,并与遗传算法、模拟退火算法、基本蚁群算法进行了比较。结果表明,该算法能得到较优的结果,具有一定的应用价值。     

.物流配送中心动态选址模型及算法研究*

针对传统物流配送中心动态选址模型没有充分考虑配送中心的可能状态和库存持有成本的问题,建立了一种新的模型。首先,利用两步骤近似法构建了在有库存和运输双重能力约束下,每一个周期配送中心的库存成本计算方法;然后,分别给出了配送中心在整个规划期内的打开、运营、关闭和再次打开的成本表达式;最后,分别用遗传算法、克隆选择算法、粒子群算法求解所建立的模型,并从算法的寻优能力、稳定性、运算速度和收敛性方面比较了三种算法的性能。算例测试结果表明,所建立的模型是有效的;从总体上看,遗传算法的适应性要强于克隆选择算法和粒子群算     

Tool Path Generation for Arbitrary Pockets with Islands

Avoiding gouge is crucial when converting cutter contact (CC) data into cutter location (CL) data for arbitrary pockets involving islands. This investigation presents a dynamic method for eliminating global gouges. Moreover, this study proposes using pair-wise bridges to merge pockets and islands. The proposed pair-wise bridge method leaves very small sized scallops. Additionally, an alternative Boolean method is designed to solve the problem of pocket containing numerous islands. Received: January 2005 / Accepted: September 2005     

Dynamic Programming Revisited: Improving Knapsack Algorithms

The contribution of this paper is twofold: At first an improved dynamic programming algorithm for the bounded knapsack problem is given. It decreases the running time for an instance with n items and capacity c from to , which is the same pseudopolynomial complexity as usually given for the 0--1 knapsack problem. In the second part a general approach based on dynamic programming is presented to reduce the storage requirements for combinatorial optimization problems where it is computationally more expensive to compute the explicit solution structure than the optimal solution value. Among other applications of this scheme it is shown that the 0--1 knapsack problem as well as the bounded knapsack problem can be solved in time and space. Received: October 15, 1998; revised March 10, 1999     

Algorithms for solving the single-sink fixed-charge transportation problem

The single-sink fixed-charge transportation problem is an important subproblem of the fixed-charge transportation problem. Just a few methods have been proposed in the literature to solve this problem. In this paper, solution approaches based on dynamic programming and implicit enumeration are revisited. It is shown how the problem size as well as the search space of a recently published dynamic programming method can be reduced by exploiting reduced cost information. Additionally, a further implicit enumeration approach relying on solution concepts for the binary knapsack problem is introduced. The performance of the various solution methods is compared in a series of computational experiments.     

Local search algorithms for the problem of competitive location of enterprises

This article deals with the mathematical model that generalizes the known problem of location of enterprises and is represented in the form of the problem of bilevel mathematical programming. In this model two competitive sides sequentially locate enterprises, and each of the sides strives to maximize its profit. As optimal solutions of the investigated problem, optimal cooperative and optimal noncooperative solutions are considered. The method is suggested for calculating the upper bounds of values of the goal function of the problem at optimal cooperative and noncooperative solutions. Simultaneously with the calculation of the upper bound, the initial approximate solution is set up. Algorithms of the local search for improving this solution are suggested. The algorithms involve two stages: at the first stage the locally optimal solution is found, while at the second stage the locally optimal solution relative to the neighborhood called the generalized one is found. The results of computational experiments demonstrating the possibilities of the suggested algorithms are displayed.     

Reinforcement learning for resource allocation in LEO satellite networks.

In this paper, we develop and assess online decision-making algorithms for call admission and routing for low Earth orbit (LEO) satellite networks. It has been shown in a recent paper that, in a LEO satellite system, a semi-Markov decision process formulation of the call admission and routing problem can achieve better performance in terms of an average revenue function than existing routing methods. However, the conventional dynamic programming (DP) numerical solution becomes prohibited as the problem size increases. In this paper, two solution methods based on reinforcement learning (RL) are proposed in order to circumvent the computational burden of DP. The first method is based on an actor-critic method with temporal-difference (TD) learning. The second method is based on a critic-only method, called optimistic TD learning. The algorithms enhance performance in terms of requirements in storage, computational complexity and computational time, and in terms of an overall long-term average revenue function that penalizes blocked calls. Numerical studies are carried out, and the results obtained show that the RL framework can achieve up to 56% higher average revenue over existing routing methods used in LEO satellite networks with reasonable storage and computational requirements.     

A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks

In many data-centric storage techniques, each event corresponds to a hashing location by event type. However, most of them fail to deal with storage memory space due to high percentage of the load is assigned to a relatively small portion of the sensor nodes. Hence, these nodes may fail to deal with the storage of the sensor nodes effectively. To solve the problem, we propose a grid-based dynamic load balancing approach for data-centric storage in sensor networks that relies on two schemes: (1) a cover-up scheme to deal with a problem of a storage node whose memory space is depleted. This scheme can adjust the number of storage nodes dynamically; (2) the multi-threshold levels to achieve load balancing in each grid and all nodes get load balancing. Simulations have shown that our scheme can enhance the quality of data and avoid hotspot of the storage while there are a vast number of the events in a sensor network.     

Multiple objective dynamic programming with forward filtering

In this paper we present a heuristic method for reducing the computational burden in multiple objective dynamic programming (MODP). Using techniques originally suggested for multiple objective linear programming, the solution set for each state (stage) are filtered, giving a representative subset of the set of efficient ways of attaining that state (stage). The method allows for considerable reductions in the amount of storage required to solve the problem, and in the dimensionality of the problem in solution space. It does not guarantee that all the identified solutions are non-dominated; however, the examples presented suggest that the representation of the subset of all efficient solutions is a good one.     

Solving dynamic double row layout problem via combining simulated annealing and mathematical programming

Double row layout problem (DRLP) is to allocate facilities on two rows separated by a straight aisle. Aiming at the dynamic environment of product processing in practice, we propose a dynamic double-row layout problem (DDRLP) where material flows change over time in different processing periods. A mixed-integer programming model is established for this problem. A methodology combining an improved simulated annealing (ISA) with mathematical programming (MP) is proposed to resolve it. Firstly, a mixed coding scheme is designed to represent both of sequence of facilities and their exact locations. Secondly, an improved simulated annealing algorithm is suggested to produce a solution to DDRLP. Finally, MP is used to improve this solution by determining the optimal exact location for each facility. Experiments show that this methodology is able to obtain the optimal solutions for small size problems and outperforms an exact approach (CPLEX) for problems of realistic size.