军事物流系统中定位-运输路线安排模型及算法

考虑到战时物资需求的紧迫性和保障资源的有限性,从决策者的角度出发,以军事物流系统总体供应时间最短为目标,构建了两级军事配送网络的定位-运输路线安排模型,并给出一种启发式算法.算法分为两个阶段,首先利用蚁群算法和线性规划的方法解决运输路线安排问题,然后运用贪婪搜索算法解决军事物流配送中心选址问题.最终,将两种算法结合起来进行逐步搜索,从而得到模型的解,并运用实例说明了算法的有效性和可行性.     

启发式蚁群算法及其在高填石路堤稳定性分析中的应用

模仿蚁群之群体行为而发展起来的蚁群算法 ( ACA) ,目前多用于求解组合优化问题 .为了让 ACA能求解复杂边坡工程问题 ,本文对蚁群算法的结构和蚂蚁转移概率的确定方法进行改进 ,得到一个新的评价复杂边坡稳定性的方法——启发式蚁群算法 ( HACA) ,探讨了 HACA在高填石路堤稳定性分析中的应用 .     

B2C电子商务仓库拣货路径优化策略应用研究

当前国内B2C电子商务仓库多为人至物的拣货模式,拣货作业成为其核心作业之一,占据仓库大量时间成本和资金成本,拣货路径优化成为企业亟需解决的问题。本文基于TSP对拣货路径进行建模,利用蚁群算法、模拟退火算法和禁忌搜索对该NP-hard问题进行求解,并同当前企业普遍采用的S型启发式策略进行对比,拣货时间节约13.35%。进一步得出当拣货品数量较少时应采用模拟退火算法求解,而当拣货品数量较大时采用蚁群算法仅进行一次迭代,则可以实现短时间得到相对较优的解。所得结果已应用于某大型电子商务企业,效果明显。     

An improved ant colony optimisation heuristic for graph colouring

The focus of this paper is an ant colony optimisation heuristic for the graph colouring problem. We start by showing how a series of improvements enhance the performance of an existing ant colony approach to the problem and then go on to demonstrate that a further strengthening of the construction phase, combined with a tabu search improvement phase, raise the performance to the point where it is able to compete with some of the best-known approaches on a series of benchmark problems.     

经过改进的求解TSP问题的蚁群算法

介绍了一种求解TSP问题的算法—改进的蚁群算法,算法通过模拟蚁群搜索食物的过程,可用于求解TSP问题,算法的主要特点是:正反馈、分布式计算、与某种启发式算法相结合.通过对传统蚁群算法的改进可以得到较好的结果.计算机仿真结果表明了该算法的有效性.     

Scheduling a single batch-processing machine with arbitrary job sizes and incompatible job families: An ant colony framework

This paper investigates the first attempt on the batch-processing machine scheduling problem, where the machine can process multiple jobs simultaneously, using an ant colony optimization metaheuristic. We consider the scheduling problem of a single batch-processing machine with incompatible job families and the performance measure of minimizing total weighted completion time. Jobs of a given family have an identical processing time and are characterized by arbitrary sizes and weights. Based on a number of developed heuristic approaches, we propose an ant colony framework (ACF) in two versions, which are distinguished by the type of embedded heuristic information. Each version is also investigated in two formats, that is the pure ACF and the hybridized ACF. To verify the performance of our framework, comparisons are made based on using a set of well-known existing heuristic and meta-heuristic algorithms taken from the literature, on a diverse set of artificially generated test problem instances. Computational results show the high performance of the proposed framework and signify its ability to outperform the comparator algorithms in most cases as the problem size increases.     

一种改进的蚁群算法及其在TSP中的应用

蚁群算法是一种求解复杂组合优化问题的新的拟生态算法,也是一种基于种群的启发式仿生进化算法,属于随机搜索算法的一种,并用于较好地解决TSP问题.然而此算法也有它自己的缺陷,如易于陷入局部优化、搜索时间长等.通过对基本蚁群算法的介绍及相关因素的分析,提出了一种改进的蚁群算法,用于解决TSPLAB问题的10个问题,并与参考文献中的F-W、NCSOM、ASOM算法进行比较,计算机仿真结果表明了改进算法的有效性.如利用改进的蚁群算法解决lin105问题,其最优解为14382.995933(已知最优解为14379),相对误差是0.0209%,计算出的最小值几乎接近于已知最优解.     

An ant colony optimization approach for the multidimensional knapsack problem

Ant colony optimization is a metaheuristic that has been applied to a variety of combinatorial optimization problems. In this paper, an ant colony optimization approach is proposed to deal with the multidimensional knapsack problem. It is an extension of Max Min Ant System which imposes lower and upper trail limits on pheromone values to avoid stagnation. In order to choose the lower trail limit, we provide a new method which takes into account the influence of heuristic information. Furthermore, a local search procedure is proposed to improve the solutions constructed by ants. Computational experiments on benchmark problems are carried out. The results show that the proposed algorithm can compete efficiently with other promising approaches to the problem.     

An improved approach to attribute reduction with ant colony optimization

Attribute reduction problem (ARP) in rough set theory (RST) is an NPhard one, which is difficult to be solved via traditionally analytical methods. In this paper, we propose an improved approach to ARP based on ant colony optimization (ACO) algorithm, named the improved ant colony optimization (IACO). In IACO, a new state transition probability formula and a new pheromone traps updating formula are developed in view of the differences between a traveling salesman problem and ARP. The experimental results demonstrate that IACO outperforms classical ACO as well as particle swarm optimization used for attribute reduction.     

Solution approaches for the capacitated single allocation hub location problem using ant colony optimisation

Hub and spoke type networks are often designed to solve problems that require the transfer of large quantities of commodities. This can be an extremely difficult problem to solve for constructive approaches such as ant colony optimisation due to the multiple optimisation components and the fact that the quadratic nature of the objective function makes it difficult to determine the effect of adding a particular solution component. Additionally, the amount of traffic that can be routed through each hub is constrained and the number of hubs is not known a-priori. Four variations of the ant colony optimisation meta-heuristic that explore different construction modelling choices are developed. The effects of solution component assignment order and the form of local search heuristics are also investigated. The results reveal that each of the approaches can return optimal solution costs in a reasonable amount of computational time. This may be largely attributed to the combination of integer linear preprocessing, powerful multiple neighbourhood local search heuristic and the good starting solutions provided by the ant colonies.     

General variable neighborhood search for the uncapacitated single allocation <Emphasis Type="Italic">p</Emphasis>-hub center problem

In this paper we propose a general variable neighborhood search heuristic for solving the uncapacitated single allocation p-hub center problem (USApHCP). For the local search step we develop a nested variable neighborhood descent strategy. The proposed approach is tested on benchmark instances from the literature and found to outperform the state-of-the-art heuristic based on ant colony optimization. We also test our heuristic on large scale instances that were not previously considered as test instances for the USApHCP. Moreover, exact solutions were reached by our GVNS for all instances where optimal solutions are known.     

基于Voronoi划分的同城即时配送优化策略研究

近年来经济社会发展及新零售业强势崛起使得平台或商家对大规模即时配送需求日益增加,在求解大规模车辆路径问题时仅使用启发式算法或其融合算法已无法满足实际需求。本文针对基于分众级的同城即时配送模式及现阶段存在的问题,确定了基于Voronoi划分算法的即时配送分区方法和对基础蚁群算法的三个改进策略;并以全程配送产生的总成本最少为目标函数,构建了带用户需求软时间窗的车辆路径问题数学模型;最后选取客户、车辆以及门店共计一百二十个真实地理位置数据,验证了本文提出的求解策略的有效性,并分析最终结果。结果显示,①使用Voronoi分区-改进蚁群算法的两阶段方法求解大规模车辆路径问题能显著减少配送总成本,同时提升客户满意度;②在多门店的条件假设下,采用改进蚁群算法求解得到的超时时间比基础蚁群算法少36%,配送总成本低17%。     

改进蚁群算法优化周期性车辆路径问题

周期性车辆路径问题(PVRP)是标准车辆路径问题(VRP)的扩展,PVRP将配送期由单一配送期延伸到T(T＞1)期,因此,PVRP需要优化每个配送期的顾客组合和配送路径。由于PVRP是一个内嵌VRP的问题,其比标准VRP问题更加复杂,难于求解。本文采用蚁群算法对PVRP进行求解,并提出采用两种改进措施——多维信息素的运用和基于扫描法的局部优化方法来提高算法的性能。最后,通过9个经典PVRP算例对该算法进行了数据实验,结果表明本文提出的改进蚁群算法求解PVRP问题是可行有效的,同时也表明两种改进措施可以显著提高算法的性能。     

TSP的量子蚂蚁算法求解

在分析量子算法的基本概念的基础上,提出了一种新的算法——量子蚂蚁算法。量子蚂蚁算法结合了量子计算中量子旋转门的量子信息和蚂蚁寻优的特点,为解决实际问题提供的一种新的优化方法。本文将量子蚂蚁算法应用于TSP问题的研究,通过选取国际通用的TSP实例库中多个实例进行测试,表明了新算法具有很好的精确度和鲁棒性,即使对于大规模问题,也能以很小的种群和不长的时间求得相对误差较小的满意解。     

Ant Colony Optimisation for Machine Layout Problems

Flexible machine layout problems describe the dynamic arrangement of machines to optimise the trade-off between material handling and rearrangement costs under changing and uncertain production environments. A previous study used integer-programming techniques to solve heuristically reduced versions of the problem. As an alternative, this paper introduces an ant colony optimisation (ACO) algorithm to generate good solutions. Experimental results are presented, with ACO obtaining better solutions than the reduction heuristic.     

Vehicle routing problems with time windows and multiple service workers: a systematic comparison between ACO and GRASP

This paper systematically compares an ant colony optimization (ACO) and a greedy randomized adaptive search procedure (GRASP) metaheuristic. Both are used to solve the vehicle routing problem with time windows and multiple service workers. In order to keep the results comparable, the same route construction heuristic and local search procedures are used. It is shown that ACO clearly outperforms GRASP in the problem under study. Additionally, new globally best results for the used benchmark problems are presented.     

Track initiation of bearings-only tracking based on ant navigation concept

A novel heuristic track initiation method is developed for the bearing-only multi-sensor–multi-target tracking system, in which each target is assumed to move with a straight line. The key idea of the proposed method is derived from the ant navigation concept in its foraging life, which differs from the traditional ant colony optimization algorithm, and it utilizes the concept of path integration and visual landmarks in ants’ navigation toolkit to find the exact positions of each target. Numerous numerical simulations are conducted and the effectiveness of the proposed track initiation method is verified according to the probability of correct track initiation.     

An artificial bee colony algorithm for the capacitated vehicle routing problem

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem.     

改进蚁群算法在智能交通中的应用

为了诱导车辆在出行时选择较高质量的路线,提出并建立了城市道路权值仿真模型.为求解该模型,从分析基本蚁群算法入手,通过在状态转移规则中加入扰动因子,改进全局更新规则,以及引入信息素更新算子改进了蚁群算法.然后利用道路权值模型对两种算法在路径寻优效果上做了比较和分析,实验结果表明改进后的蚁群算法能有效地避免停留在局部最优解,并提高计算效率,具有良好的寻优性和收敛性,能准确找出路网中满足综合要求的最优路径.     

Bi-criteria single machine scheduling with a time-dependent learning effect and release times

This paper deals with a bi-criteria single machine scheduling problem with a time-dependent learning effect and release times. The objective is to minimize the weighted sum of the makespan and the total completion time. The problem is NP-hard, thus a mixed integer non-linear programming formulation is presented, and a set of dominance properties are developed. To solve the problem efficiently, a procedure is then proposed by incorporating the dominance properties with an ant colony optimization algorithm. In the proposed algorithm, artificial ants construct solutions as orders of jobs based on the heuristic information as well as pheromone trails. Then, the dominance properties are added to obtain better solutions. To evaluate the algorithm performance, computational experiments are conducted.