基于改进最低水平线方法与遗传算法的矩形件排样优化算法

传统的最低水平线方法用于矩形件排样时可能产生较多未被利用的空白区域,造成不必要的材料浪费.针对此缺陷,在搜索过程中引入启发式判断,实现空白区域的填充处理,提高板材利用率.在应用遗传算法优化矩形件排样顺序时,在进化过程中采用分阶段设置遗传算子的方法,改善算法的搜索性能与效果.通过改进最低水平线方法与基于分阶段遗传算子的遗传算法相结合,共同求解矩形件排样问题.排样测试数据表明,所提出的矩形件排样优化算法能够有效改善排样效果,提高材料利用率.     

求解约束优化问题的改进布谷鸟搜索算法

为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。     

一个实用的矩形件优化排样启发式算法

仔细研究了传统矩形件优化排样近似算法及存在的主要问题，提出一个新的启发式算法。该算法根据最后板材的实际排放情况，采用了多种排放策略，克服了原算法的在零件数较少时的缺陷。在此基础上用Visual C 6．0开发了一个实用的矩形件计算机辅助排样系统。实际应用表明，新算法可获得比原近似算法更好的优化排样结果。笔者给出了算法的具体实现方法和步骤．     

不规则零件优化排样的神经网络混合优化算法

 提出一种利用人工神经网络求解不规则件排样问题的混合优化方法.该方法首先把排样和制造工艺联系起来,将多边形各边向外扩充,为零件预留加工余量;然后采用自组织特征映射模型（SOM）和Hopfield人工神经网络相结合的方法,运用SOM神经网络对初始在板材内随机排布的不规则零件进行平移,逐步减小不规则零件之间的重叠面积,求得各零件的最优位置,再运用Hopfield神经网络对平移后的零件旋转,进行迭代运算,当能量函数达到稳定状态时,得到各排样零件的最优旋转角度组合,实现自动排样.算法可以解决不规则件和矩形件在规则板材以及不规则板材上的排样问题,实例证明了该算法的有效性和实用性.     

基于Backbone的空间收缩与划分算法

搜索空间的规模和复杂程度是决定问题求解难度的重要因素,而解空间的信息往往可以引导搜索找到最优解.在已知JSP空间结构的基础上,提出一种空间收缩与划分算法.算法利用搜索算法获得的较优解,结合组合优化问题解的backbone的概念,将搜索空间收缩并划分为一个或多个优解域,在优解域内再进行小规模问题的优化.该算法不必在求解前或求解过程中进行大量的统计分析工作,可以利用求解信息对解空间的地形进行估计,提高求解速度和解的质量.实验结果也证明了算法的有效性.     

集装箱车辆调度问题的变邻域禁忌搜索算法

研究一类带工作时间约束的集装箱专用车辆调度问题的混合禁忌搜索算法.此问题可分解为车辆路线设定和车辆分配两个组合优化问题,但是两个问题的分开求解最优解的组合却并不一定是总问题的最优解.首先对问题给出数学描述,之后通过引入一个变邻域搜索策略,提出一个解决该问题的混合禁忌搜索算法.该算法使用两行向量进行编码,采用随机扩大禁忌步长,并设计三种邻域变换定义,采用变邻域策略来扩大搜索空间.最后通过对6个不同规模算例求解验证该算法在解决此类问题的有效性.     

基于连续函数优化的禁忌搜索算法

提出了一种连续禁忌搜索算法,用于求解连续函数优化问题.邻域规则及禁忌规则是禁忌搜索算法的核心,针对连续函数解空间的连续性,提出了一种邻域分割法来进行邻域搜索,并对禁忌规则进行了设计.通过经典函数测试可以看出,禁忌搜索算法在连续函数优化问题中显示出很强的"爬山"能力,优化结果与实际最优值非常接近,是一种有效的全局优化算法.     

缺陷板材非规则件优化排样

针对理论上属于NPC问题的非规则件优化排样问题,论文提出一种基于小生境技术的自适应遗传模拟退火算法与基于内靠接临界多边形最低点的启发式布局算法相结合的方法。考虑到算法中交叉概率和变异概率的选择影响到算法收敛性,提出了自适应的交叉概率和变异概率,通过基于小生境技术的遗传模拟退火算法对非规则件排样的最优顺序和各自的旋转角度进行优化搜索。将非规则件定位在有缺陷原材料和非规则件多边形的内靠接临界多边形最低点以实现个体的解码,同时避开了原材料表面缺陷。排样实例表明,该优化排样算法行之有效,具有广泛的适应性。     

基于融合禁忌搜索粒子群算法的配电网无功优化

从数学角度分析,配电网无功优化是一个非线性、多变量、多约束的混合规划问题。粒子群优化搜索算法被广泛应用于求解配电网无功优化问题。由于粒子群算法粒子群在进化过程易趋向同一化,失去多样性,从而使算法陷入局部最优解。本文在分析配电网无功优化的特性基础上,提出一种改进的紧融合禁忌搜索-粒子群算法用于配电网无功优化问题的求解。通过将禁忌搜索功能融合到粒子历史最优解和全局最优解寻优过程中,避免了粒子群算法寻优过程中出现的局部最优问题,从而提高粒子群算法的全局搜索能力。通过IEEE14节点系统的仿真计算结果表明,改进的算法能取得良好的效果。     

遗传算法和碰撞算法混合求解冲裁件自动排样问题

针对冲裁件的实际情况,提出了一种利用遗传算法和碰撞算法混合求解冲裁件自动优化排样的方法.在排样中对冲裁件的纵向偏距、放置角度和排样方式进行编码,通过碰撞理论来计算每个个体所对应排样的排样步距.论文给出了运用遗传算法求解的步骤、遗传代码的构造方式和排样步距的求解方法.     

Solving non-oriented two dimensional bin packing problem using evolutionary particle swarm optimisation

The non-oriented two-dimensional bin packing problem (NO-2DBPP) deals with a set of integer sized rectangular pieces that are to be packed into identical square bins. The specific problem is to allocate the pieces to a minimum number of bins allowing the pieces to be rotated by 90° but without overlap. In this paper, an evolutionary particle swarm optimisation algorithm (EPSO) is proposed for solving the NO-2DBPP. Computational performance experiments of EPSO, simulating annealing (SA), genetic algorithm (GA) and unified tabu search (UTS) using published benchmark data were studied. Based on the results for packing 3000 rectangles, EPSO outperformed SA and GA. In addition; EPSO results were consistent with the results of UTS indicating that it is a promising algorithm for solving the NO-2DBPP.     

A branch and bound algorithm for the strip packing problem

We propose a new branch and bound algorithm for the two dimensional strip packing problem, in which a given set of rectangular pieces have to be packed into a strip of given width and infinite length so as to minimize the required height of the packing. We develop lower bounds based on integer formulations of relaxations of the problem as well as new bounds based on geometric considerations, and reduce the tree search with some dominance criteria. An extensive computational study shows the relative efficiency of the bounds and the good performance of the exact algorithm.     

Experiments with a strategic oscillation algorithm for the pallet loading problem

This paper concerns the use of strategic oscillation (SO) for packing problems. The SO algorithm spends a certain time exploring infeasible solutions rather than restricting the search to feasible solutions only. The main features of the algorithm are explained and suggestions on different implementations are given. The pallet-loading problem is probably the simplest form of packing problem and is thus suitable for initial experiments with SO. The results obtained suggest that SO is successful for this problem and that it will probably be fruitful to extend the use of SO to other, harder, packing problems.     

三维装箱布局的单向寻优搜索法

研究待布局物体组合时面的各种拼合形式,以及待布局物体与待布局空间之间的各种间隙及其组合形式,形成单向寻优搜索法,使每一个待布局物体均充分地向小于等于自身的物体排序方向搜索适合组合的物体,形成二叉树组合结构,不仅显著提高了装箱率,也直观给出装箱顺序,还有效避免了NP完全问题.     

求解矩形布局问题的自适应算法

矩形布局问题属于NP-Hard问题,其求解算法多为启发式算法。该文侧重于构造布局求解算法中定位函数(规则)的优化,将模拟退火算法的思想融入到遗传算法中,提出了求解矩形布局问题的自适应算法,其利用自适应交叉、变异及接收劣质解的概率等方法对定位函数中各参数进行优化。算法通过两种方式确定初始种群的数目,具有较强的适应性。在算法搜索的后期,利用差异性较大的个体进行交叉操作,从而保持种群的多样性。最后通过实例证明了该算法能够很好的应用于矩形布局问题的求解。     

基于序列模型的三维矩形布局算法

针对三维矩形布局问题进行了研究。在三元序列的基础上,结合布局物体的几何可行域,提出了三元序列结合几何可行域的布局算法。并且利用遗传算法对布局算法进行优化,得到了三元序列结合可行域布局遗传算法。分析和实例证明,三元序列结合几何可行域的改进算法有效地提高了布局效率。     

Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times

In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used in GAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into the GAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm.     

A capacitated vehicle routing problem with order available time in e-commerce industry

In this article, a variant of the well-known capacitated vehicle routing problem (CVRP) called the capacitated vehicle routing problem with order available time (CVRPOAT) is considered, which is observed in the operations of the current e-commerce industry. In this problem, the orders are not available for delivery at the beginning of the planning period. CVRPOAT takes all the assumptions of CVRP, except the order available time, which is determined by the precedent order picking and packing stage in the warehouse of the online grocer. The objective is to minimize the sum of vehicle completion times. An efficient tabu search algorithm is presented to tackle the problem. Moreover, a Lagrangian relaxation algorithm is developed to obtain the lower bounds of reasonably sized problems. Based on the test instances derived from benchmark data, the proposed tabu search algorithm is compared with a published related genetic algorithm, as well as the derived lower bounds. Also, the tabu search algorithm is compared with the current operation strategy of the online grocer. Computational results indicate that the gap between the lower bounds and the results of the tabu search algorithm is small and the tabu search algorithm is superior to the genetic algorithm. Moreover, the CVRPOAT formulation together with the tabu search algorithm performs much better than the current operation strategy of the online grocer.     

低碳环境下冷链物流企业库存-配送优化

目的 优化冷链物流企业库存-配送的路径,以降低企业冷链物流的成本.方法 考虑冷链物流企业库存和配送环节产生成本的因素,并结合我国关于碳减排的碳交易政策,将企业的碳排放代价同其他代价综合考虑,建立以总代价最低为目标的成本模型,设计并改进麻雀搜索算法进行计算.结果 通过使用MatlabR2018b进行仿真实验,将采用麻雀搜索算法计算的仓储-配送作业总代价与其他经典算法进行比较,在1个仓储配送任务内,代价可减少2％～4％,验证了麻雀搜索算法解决文中代价模型的有效性.结论 该研究为冷链物流企业库存-配送优化问题提供了一种新型的解决方法,具有较强的操作性和实际意义.     

基于区块链的食品冷链质量安全信息平台构建

目的 研究区块链技术在食品安全领域,尤其是在生鲜食品冷链物流质量安全监测中的具体应用,以加强食品质量安全管理,提高食品冷链供应链效益.方法 分析生鲜食品冷链物流质量安全管理的薄弱环节,论证区块链技术的食品冷链质量安全监测适用性基础上,以区块链为底层技术构建食品冷链质量安全信息平台,研究区块链技术的创新应用对食品冷链质量安全管理的提升效果.结果 应用区块链技术构建了数据统一、运营高效的生鲜食品冷链质量安全信息平台,对食品质量安全信息进行了实时采集,实现了质量安全风险即时预警、质量安全问题有效溯源,有助于重塑食品质量安全生态系统.结论 区块链作为分布式账本、数字签名、溯源存证等一系列核心技术的组合,基于区块链技术创建食品冷链质量安全信息平台,能够强化微生物污染监测、缩短食品冷链在途时间,并使质量安全问题得到有效追踪溯源,对于加强食品质量安全管理、促进生鲜食品供应链优化有重要意义.