基于空箱拉动的总装线物料配送研究

针对汽车总装线边空箱拉动供料模式下的物料配送问题,分析了空箱拉动模式下物料配送、空箱回收、需求点物料消耗速度对配送路线规划的影响.以配送路线最短和配送路线需求点配送频次均衡化为优化目标,建立了基于空箱拉动模式的总装线边物料配送路径优化模型.利用改进自适应遗传算法求解模型,根据种群适应度分布变化趋势调整交叉和变异概率,提高了遗传算法搜索能力.实例仿真结果显示,与总装线原配送方案相比,该模型减少了约32%的配送路程.     

数字化车间物料动态配送规划研究

目的 针对数字化生产车间工位物料需求时间的不确定,导致物料配送不准确、不及时的问题,提出一种动态物料配送策略。方法 首先,根据工位关联度和变动时间窗确定实时的配送工位和协同配送工位,设计基于工位排序的动态物料配送路径优化策略。其次,建立以配送成本和时间窗偏离惩罚成本综合最小为目标函数的数学模型。最后,提出并采用系统动力学仿真与蚁群遗传融合算法联合的方法对模型进行求解。结果 模拟算例表明,与静态物料配送优化策略相比,该策略的平均时间成本减少率为30.1%,平均库存减少率为14.86%。结论 该策略能够根据动态时间窗确定配送工位和协同工位,并实时调整配送顺序,实现物料配送的动态自适应性调整,降低总配送成本。融合算法在迭代次数、收敛性、最优解质量方面有明显优越性。     

共享电动车电池配送问题研究

共享电动车电池的配送方案关系到用户的切身体验和企业利益。为制定最优配送方案，真正打通人们出行的“最后一公里”，本文考虑企业对成本的要求和用户对时效性的要求，以总配送成本最小以及用户满意度最高为目标建立了一个带软时间窗的车辆路径问题模型，利用扫描法和基于最佳路径成本的交叉算子改进了传统遗传算法，用算例验证了模型与改进算法的有效性，并通过数值实验找出了种群大小、迭代次数与最优解之间的相关关系。     

考虑余-废料资源回收的车间物料配送路径规划研究

针对离散制造生产过程中物料配送及余-废料资源回收效率低、成本高等问题,在分析车间物料流循环过程基础上结合物料配送时间窗限制,提出了物料配送和余-废料资源回收协同优化的物料再循环策略,并建立了以车间物料流总成本为目标的物料再循环路径规划模型。利用遗传算法在Matlab软件中对实例进行求解,验证了物料再循环策略应用于离散制造车间物料配送和余-废料资源回收的可行性和有效性。     

顾客可选时间窗下城市配送订单接收策略研究

在顾客可选城市配送时间窗的情境下,研究了带硬时间窗约束的动态车辆路径问题,提出了基于辐射半径的订单接收策略,设计了可接受时间窗分配初始化、可接受时间窗动态调整、参照点动态选择及基于时间窗偏差阈值的配送需求评估等方法,构建了求解动态车辆路径问题的插入算法。通过与先到先服务策略的对比表明,提出的策略能够实现配送收益和行驶距离的有效平衡;基于辐射半径的订单接受策略在平均接受决策耗时、路径更新耗时和全局优化耗时与先到先服务策略差异较小;随着参照点数量增多,算例中收益和行驶距离都在逐渐增长,总收益/距离之比呈现先下降后上升的趋势。     

多车程多时间窗车辆路径问题研究

针对末端物流中配送车辆多趟次运输、客户对配送服务时间的多样化需求,研究多车程多时间窗车辆路径问题。构造该问题的最小化车辆数量和总运输成本的双目标混合整数规划模型,设计改进的自适应大邻域搜索算法对其求解;构建了基于路径、车程及客户点3个层级上的多种高效的破坏算子和修复算子来扩大解的搜索空间;使用自适应策略选择高效的搜索算子,以及引入模拟退火新解接受准则避免陷入局部最优解来提高搜索效率。通过多种规模算例实验结果分析,验证了改进的自适应大邻域搜索算法的优越性,并分析了考虑多车程的模型对总运输成本的影响。     

基于改进智能水滴算法的冷链物流配送路径优化问题研究

针对冷链物流配送路径优化问题,以传统的配送路径优化问题为基础,根据冷链物流的特性,构建了带时间窗的冷链物流配送路径优化成本模型。根据智能水滴算法的思想和原理,对智能水滴算法进行改进,把改进后的智能水滴算法运用到该模型中。最后借助MATLAB软件对实例进行仿真并与其他智能算法结果进行对比,证明该方法对于解决带时间窗的冷链物流配送路径优化问题更具准确性和优越性,能够为类似的路径优化问题提供一种可行的研究方法。     

城市实时交通路网车辆路径优化问题研究

针对日益突出的城市交通拥堵问题,在综合考虑距离、载重量、时间、燃料对成本影响的基础上,研究时变车辆路径优化问题,提出跨时间域计算配送成本的方法,建立以成本为目标的城市配送优化模型。为提高算法的求解质量与效率,采用改进遗传算法,并设计两边逐次修正算子。最后,结合京东在重庆市的配送实例,分析车辆出发时间对成本的影响,同时考虑路径选择的灵活性并及时调整路线。实例验证了模型在城市配送中的适用性。     

单线超市车辆配置与路径问题优化研究

为解决工位对物料需求紧迫程度不同,进而影响配送优先顺序的问题,提出考虑工位配送优先级的单线超市车辆配置及配送路径优化模型和求解算法。首先,建立以物料需求紧迫系数表示配送优先顺序,以单线超市车辆配置最少与路径最短为目标的数学模型;其次,提出加入控制搜索因子的改进蚁群算法对该问题进行求解,通过在迭代不同时期设置不同大小的控制搜索因子来改变节点选择概率,提高最优解搜索速度的同时避免算法陷入局部最优;最后,通过算例分析,验证了模型的正确性,同时也证明了改进蚁群算法能够实现对物料需求紧迫程度高的工位优先配送,较基本蚁群算法在计算结果及算法运行时间上具有优势。     

时变路网下电动冷藏车配送路径优化研究

为了解决时变路网中电动车在冷链物流配送过程中的路径选择问题,根据冷链产品和电动冷藏车的特性,引入多模糊时间窗约束及配送车辆电量约束,建立时变路网下考虑充电站的多时间窗约束的电动冷藏车路径优化模型。运用AP聚类算法划分配送区域,在明确配送范围的基础上采用改进的遗传算法对模型进行求解。通过算例仿真,验证模型和算法的有效性和可行性,并将静态路网与时变路网下的配送方案进行对比,利用随机生成不同规模的案例对模型的普适性进行验证。结果表明,与静态路网相比,基于时变路网规划出的配送方案可以减少12.201%的配送成本。研究成果对于帮助企业科学的规划配送路径,降低配送成本等方面具有一定的指导意义。     

Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.     

Research of Dynamic Material Handling in an Automobile General Assembly Line Based on Real-time Information

The status of material delivery of an automobile general assembly line is analyzed,and the technique to achieve the real-time tracking of assembly statas information is proposed based on RFID( Radio Frequency Identification) . Thus the consumption of line-side buffer is obtained dynamically,then the type and quantity of needed material are fed back to the subsystem of material handling; the algorithm for determining the best time departure time of delivery driver based on minimizing of total time penalty function is proposed. This approach makes the ma- terial amount of a single delivery trip maximized and improves the efficiency of delivery drivers significantly in the case of does not affect the assembly line normal throughput. Additionally,although this dynamic material handling method is developed for the automobile assembly plant,it should be pointed out that this method is also applicable to other mixed model assembly plants such as electronics,semiconductor and aerospace industry.     

Order-oriented cooperative sequencing optimisation in multi-mix-model assembly lines

Order-oriented products assembly sequence among different assembly lines becomes a critical problem for mass customisation manufacturing systems. It significantly affects system productivity, delivery time, and manufacturing cost. In this paper, we propose a new approach to extend the traditional products sequencing from mixed model assembly line (MMAL) to multi-mixed model assembly lines (MMMALs) to obtain the optimal assembly sequence with the objectives of minimising consumption waviness of each material in the lines, assembly line setup cost, and lead-time. A multi-objective optimisation algorithm based on variable neighbourhood search methods (VNS) is developed. We perform an industrial case study in order to demonstrate the practicality and effectiveness of the proposed approach.     

城市物流配送系统的多目标优化LRP模型研究

建立了准时到达、总成本最低的多目标定位--运输路线安排问题（LRP）的数学模型;此LRP问题包括配送中心的分派和运输路线优化两方面的决策,属于NP难题.在构建模型算法过程中,采用了两阶段法：首先采用启发式算法确定配送中心分派问题;其次,给出各个目标的优先级,根据优先因子建立以准时到达为最高优先级、总成本最低为次优先级的新的数学模型,并运用遗传算法求解优化运输路线问题.     

Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach

In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.     

Set-partitioning-based heuristic for balancing and configuration of automated flexible machining line

Flexible machining lines are used in a wide range of industries due to their ability of reconfiguration to meet high variety of customer demands. A novel problem is proposed in the current research to consider automated flexible machining line (AFML) with automated machining using computer numerical control machines and automated auxiliary operations using robots. A mixed-integer programming model for the current novel problem is developed. Moreover, a novel method named set-partitioning-based heuristic (SPH) is proposed to solve this new flexible machining line balancing problem to minimise the cycle time of the line and the performance is compared with both exact algorithm and random search algorithm. A set of benchmark instances based on different size of problems against different system parameters is made. Furthermore, sensitivity analysis of the system parameter in AFML is performed to know, how the number of machines and processing time can influence the cycle time and the utilisation of AFML. Computational experiments are performed to show the performance of the proposed method SPH against other methods and the results indicate that SPH performs best among all test methods in terms of solution quality and computation on both the proposed benchmark instances.     

Improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee’s behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.     

车载可补货无人配送小车配送路径研究

无人配送小车由于不适合长距离运输,可与货车搭配完成“最后一公里”配送任务以增加服务范围,这对车辆路径优化问题提出了新的挑战。针对配送小车数量有限、城市配送货物量大且货车停靠限制的特点,提出无人配送小车可补货的大车-小车路径优化问题,即一辆货车搭载多台无人配送小车,由无人配送小车给客户送货,无人配送小车可在货车处补充货物并执行多行程配送。构建以总配送距离最短为目标的整数规划模型,针对此模型设计混合遗传大邻域搜索算法,在遗传算法基础上增加大邻域搜索算法对个体优化。在算法优化过程中先优化小车路径,再在小车路径基础上优化大车路径。数值实验表明,对于小规模问题,所提算法最多花费CPLEX求解时间的6%便获得最优解;在改造的Solomon数据上,所提算法相对于遗传算法平均有95.5%的计算结果优势,相对于大邻域搜索算法平均有7.2%的计算结果优势,且数据量越大,优势越大。     

Nondestructive identification of multiple flaws using XFEM and a topologically adapting artificial bee colony algorithm

We present a novel algorithm based on the extended finite element method (XFEM) and an enhanced artificial bee colony (EABC) algorithm to detect and quantify multiple flaws in structures. The concept is based on recent work that have shown the excellent synergy between XFEM, used to model the forward problem, and a genetic‐type algorithm to solve an inverse identification problem and converge to the ‘best’ flaw parameters. In this paper, an adaptive algorithm that can detect multiple flaws without any knowledge on the number of flaws beforehand is proposed. The algorithm is based on the introduction of topological variables into the search space, used to adaptively activate/deactivate flaws during run time until convergence is reached. The identification is based on a limited number of strain sensors assumed to be attached to the structure surface boundaries. Each flaw is approximated by a circular void with the following three variables: center coordinates (x_c, y_c) and radius (r_c), within the XFEM framework. In addition, the proposed EABC scheme is improved by a guided‐to‐best solution updating strategy and a local search (LS) operator of the Nelder–Mead simplex type that show fast convergence and superior global/LS abilities compared with the standard ABC or classic genetic algorithms. Several numerical examples, with increasing level of difficulty, are studied in order to evaluate the proposed algorithm. In particular, we consider identification of multiple flaws with unknown a priori information on the number of flaws (which makes the inverse problem harder), the proximity of flaws, flaws having irregular shapes (similar to artificial noise), and the effect of structured/unstructured meshes. The results show that the proposed XFEM–EABC algorithm is able to converge on all test problems and accurately identify flaws. Hence, this methodology is found to be robust and efficient for nondestructive detection and quantification of multiple flaws in structures. Copyright © 2013 John Wiley & Sons, Ltd.     

Finite-circle method for component approximation and packing design optimization

In this article, the finite-circle method is introduced for 2D packing optimization. Each component is approximated with a group of circles and the non-overlapping constraints between components are converted into simple constraints between circles. Three new algorithms—the bisection algorithm, the three-step algorithm, and the improved three-step algorithm with gap—are developed to automatically generate fewer circles approximating the components. The approximation accuracy, the circle number, and the computing time are analyzed in detail. Considering the fact that packing optimization is an NP-hard problem, both genetic and gradient-based algorithms are integrated in the finite-circle method to solve the problem. A mixed approach is proposed when the number of components is relatively large. Various tests are carried out to validate the proposed algorithms and design approach. Satisfactory results are obtained.