面向批量订单包装的物流箱规格优化问题研究

目的 针对面向仓储物流环境下多型号多批量产品的订单包装问题,提出一种预制物流箱规格优化模型及算法。方法 对产品订单建立订单分包规则,确定分包方案,以订单包装材料总成本最小为优化目标建立物流箱规格优化模型。针对该模型提出一种改进模拟退火算法,通过贪婪策略求解最优分包方案,降低模型计算复杂度,设计一种新型解更新算子,以提高算法寻优能力,设计一种自适应步长策略,以平衡算法前期全局搜索与后期局部搜索的能力。结果 通过实例证明,文中提出的算法相较于其他算法,具有更强的求解能力,与实例企业仓储包装现状相比,同批订单降低了17%的包装材料成本。结论 该方法可用于解决产品种类多、尺寸差异大、动态更新等应用场景下的系列运输包装纸箱规格优化问题,为企业物流运输管理提供了一种有效的包装优化思路和解决方法。     

基于改进粒子群算法求解单级多资源约束生产批量计划问题

本文提出了用于求解单级多资源约束的生产批量计划问题的改进二进制粒子群算法,阐明了算法的具体实现过程。通过对其它文献中的例子进行计算和结果比较,表明了该算法在寻优能力、求解速度和稳定性方面都明显优于文献中的传统遗传算法和退火惩罚混合遗传算法。     

基于优先保持策略遗传算法的三维装箱问题

目的 针对以空间利用率最大为目标的三维装箱问题,设计基于优先保持策略的改进遗传算法,并对其进行求解.方法 首先,在分析现有相关研究存在不足的基础上,提出优先保持策略的基本改进理念;其次,针对问题特点,设计改进遗传算法的基本流程,重点对交叉和变异的详细实现进行介绍;最后,通过实验仿真的方式,对求解结果和算法性能进行对比分析.结果 实验证明,在所用算例中,算法的求解结果优于对比算法约14％,且收敛更为稳定,时耗满足一般需求.结论 文中算法具有较优异的迭代性能.     

装配作业车间分批调度问题的算法对比

建立最小化makespan的装配作业车间分批调度模型,利用遗传算法构建基于整体集成优化策略、分层迭代优化策略和双层进化策略的求解算法。它们的区别在于处理批量划分问题和子批调度问题的联合优化机制不同。最后通过仿真实验对比了几类算法的求解效果和收敛速度,并分析了它们的适应性特点。     

面向货物装卸需求的越库仓门分配和货车排序

在货物种类多、批量少的越库调度系统中,货物的装卸顺序要求对于优化仓门分配和货车排序问题起着重要作用。针对这种情况,以最小化越库操作完工时间为目标,建立越库调度模型。分别基于优化仓门分配和货车排序问题,设计惯性权重非线性改变和增加交叉操作的改进粒子群算法进行迭代寻优。最后通过不同规模的数值实验,将改进粒子群算法与标准粒子群算法和遗传算法进行对比分析,实验结果表明改进粒子群算法在求解精度上比标准粒子群算法和遗传算法有明显优势,在求解时间上优于遗传算法,略逊色于标准粒子群算法。     

成组单元生产批量计划问题的知识进化算法

有资源约束的成组单元生产批量计划问题是一个NP难题,为有效求解该问题,提出了一种知识进化算法求解方案,并阐明了知识进化算法的具体实现过程。通过对仿真实例的计算和结果比较,表明了该算法在寻优能力等方面都明显优于遗传算法。     

图的度量维数问题的0-1蚁群条件着色分辨算法研究

图的度量维数问题（MDP）是一类在机器导航、声呐系统布置、化学、数据分类等领域有重要应用的组合优化问题．针对该问题,本文通过引入图的分辨表存储结构,建立了非线性求解模型;同时,通过改进现有蚁群算法的参数设计,利用全局搜索和局部搜索相结合的策略,建立了求解模型的改进型蚁群算法．数值对比分析验证了算法的有效性：全局搜索和局部搜索的结合较大程度的改进了算法求解质量;在规则图上提高算法求解质量具有一定挑战;与遗传算法计算结果相比较,本文提出的算法不仅在求解质量方面有所提升,而且在最坏的情况下能为图提供极小分辨集. 最后,本文探索了部分算法参数对算法求解质量的影响,并给出了进一步研究课题．     

基于三方博弈的改进遗传算法求解多目标柔性作业车间调度

针对柔性作业车间的特点,以最小化完工时间、总机器负荷最小和临界机器负荷最小为目标,提出了基于三方博弈的改进遗传算法求解多目标柔性作业车间调度模型。通过三方博弈,使三个优化目标之间的博弈策略实现最优组合,从而获得子博弈完美纳什均衡,即为问题的优化组合解。为优化种群质量,将改进遗传算法应用于多目标柔性作业车间调度问题的求解过程,采用帕累托分类思想,对种群进行选择和精英保留,以优化种群结构;通过设计交叉、变异和局部搜索机制进一步寻找目标函数的最优解。为证明算法的有效性,运用基准算例对算法的求解性能进行了验证。其结果表明,所提算法在求解结果上有明显的改善,求解效率更高。     

基于改进遗传算法的海底地层参数反演计算

为了提高对海底地层参数变量的反演计算能力,设计了一种基于双种群协同进化策略的改进遗传算法。针对标准遗传算法局部搜索能力差且易于出现早熟现象的缺点,在标准遗传算法基础上引入双种群同时进行优化搜索,两个种群分别给予不同的控制参数,实现协同进化,最终给出一个综合的最优解。通过两个算例对遗传算法的寻优能力进行测试,实验结果表明,提出的改进算法不仅提高了搜索性能,并且对遗传控制参数的依靠度大大降低,特别是对大型复合参数反演问题的求解计算更为有效。     

基于实数编码的改进遗传算法在结构可靠度中的应用研究

本文将人工智能的关键技术之一演化算法中的遗传算法用于结构可靠度的计算,并在算法中采用实数编码技术及一系列目前较先进的策略和算子,同时将模拟退火的思想引入变异算子。通过算例证明这种改进遗传算法在求解可靠度尤其求解复杂非线性问题可靠度时具有良好收敛性和高效性。     

Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines

This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model.     

Jobshop lot streaming with routing flexibility,sequence-dependent setups,machine release dates and lag time

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage manufacturing system so that operations of a given lot can overlap. This technique can reduce the manufacturing makespan and is an effective tool in time-based manufacturing. Research on lot streaming models and solution procedures for flexible jobshops has been limited. The flexible jobshop scheduling problem is an extension of the classical jobshop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. In this paper we develop a lot streaming model for a flexible jobshop environment. The model considers several pragmatic issues such as sequence-dependent setup times, the attached or detached nature of the setups, the machine release date and the lag time. In order to solve the developed model efficiently, an island-model parallel genetic algorithm is proposed. Numerical examples are presented to demonstrate the features of the proposed model and compare the computational performance of the parallel genetic algorithm with the sequential algorithm. The results are very encouraging.     

包装废弃物回收车辆路径问题的改进遗传算法

目的采用优化传统遗传算法(GA)研究包装废弃物回收车辆路径问题(VRP)的性能。方法提出改进遗传算法(IGA)。首先,设计基于贪婪算法的初始种群生成算子,提高初始种群质量;其次,设计根据适应度值大小、进化代数等自适应调整的交叉和变异概率;然后,设计最大保留交叉算子,保证种群的多样性;最后,对企业实例和标准算例进行仿真测试。结果采用IGA算法、蚁群算法(ACO)能求得算例最优解,且IGA算法运行速度快于ACO算法,分支界定算法(BBM)、传统GA算法无法求得算例最优解。结论与BBM算法、传统GA算法和ACO算法相比,IGA算法求解包装废弃物回收VRP问题的整体性能更优。     

基于几何参数标定的串联机器人精度提升

为了提升串联机器人绝对定位精度,提出了基于零参考模型(ZRM)的机器人几何参数标定方法。建立了包含方向矢量和连接矢量的机器人零参考模型;针对模型特点,利用改进遗传算法(IGA)优化求解零位方向分量和位置方向分量,给出了用IGA标定几何参数目标函数值计算方法及求解几何参数误差的具体步骤。通过对ER10L-C10工业机器人不同测点下仿真标定及实测研究结果表明:IGA方法能够快速对机器人ZRM的几何参数实现标定,当标定点设定为50个左右时,标定后的机器人在测试点的精度提升泛化能力较好,对ER10L-C10机器人在整个工作空间内实测标定其末端绝对定位精度提升约90%,该方法适于在有高定位精度要求的串联机器人中推广应用。     

A new heuristic for the flowshop scheduling problem to minimize makespan and maximum tardiness

This paper presents a new heuristic for solving the flowshop scheduling problem that aims to minimize makespan and maximize tardiness. The algorithm is able to take into account the aforementioned performance measures, finding a set of non-dominated solutions representing the Pareto front. This method is based on the integration of two different techniques: a multi-criteria decision-making method and a constructive heuristic procedure developed for makespan minimization in flowshop scheduling problems. In particular, the technique for order preference by similarity of ideal solution (TOPSIS) algorithm is integrated with the Nawaz–Enscore–Ham (NEH) heuristic to generate a set of potential scheduling solutions. To assess the proposed heuristic's performance, comparison with the best performing multi-objective genetic local search (MOGLS) algorithm proposed in literature is carried out. The test is executed on a large number of random problems characterized by different numbers of machines and jobs. The results show that the new heuristic frequently exceeds the MOGLS results in terms of both non-dominated solutions, set quality and computational time. In particular, the improvement becomes more and more significant as the number of jobs in the problem increases.     

A genetic algorithm for solving the economic lot scheduling problem in flow shops

In this study, we propose a hybrid genetic algorithm (HGA) to solve the economic lot scheduling problem in flow shops. The proposed HGA utilizes a so-called Proc PLM heuristic that tests feasibility for the candidate solutions obtained in the evolutionary process of genetic algorithm. When a candidate solution is infeasible, we propose to use a binary search heuristic to ‘fix’ the candidate solution so as to obtain a feasible solution with the minimal objective value. To evaluate the performance of the proposed HGA, we randomly generate a total of 2100 instances from seven levels of utilization rate ranged from 0.45 to 0.80. We solve each of those 2100 instances by the proposed HGA and the other solution approaches in the literature. Our experiments show that the proposed HGA outperforms traditional methods for solving the economic lot scheduling problem in flow shops.     

An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem

In this article, a water wave optimization algorithm with a single wave mechanism, called single water wave optimization (SWWO), is proposed to solve the no-wait flow-shop scheduling problem (NWFSP) with the objective of minimizing the makespan. In the proposed SWWO, an improved Nawaz–Enscore–Ham (NEH) heuristic is applied to construct a high-quality initial candidate. In the propagation operation, a self-adaptive block-shift operation is employed. In the breaking operation, a variable neighbourhood search operation is utilized to explore the local optimal solution. According to the schema theory as presented in genetic algorithms, a crossover operation is adopted as the refraction operation. Finally, the computational results based on several benchmarks and statistical performance comparisons are presented. The experimental results demonstrate the effectiveness and efficiency of the proposed SWWO for solving the NWFSP.     

智能化遗传算法

针对遗传算法的收敛速度慢、收敛早熟和概率稳定性差等问题提出一种智能化遗传算法(IGA)。首先，建立描述种群进化的统计特征量，为IGA的算法策略提供决策依据。其次，建立种群的自学习算法、种群的自组织算法与遗传算子操作概率的自适应算法，并将这些算法嵌入最优保存简单遗传算法(OMSGA)，从而构成IGA。最后，从理论上对算法收敛性及效率进行了分析。通过遗传算法标准测试函数的仿真结果证明了算法的实用性和有效性。     

Improved genetic algorithm for mixed-discrete-continuous design optimization problems

An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.     

Genetic-based algorithm for power economic load dispatch

An improved genetic algorithm with multiplier updating (IGAMU) to solve practical power economic load dispatch (PELD) problems of different sizes and complexities with non-convex cost curves, where conventional mathematical methods are inapplicable, is developed. The improved genetic algorithm (IGA) provides an improved evolutionary direction operator and a migrating operator, enabling it to efficiently search and actively explore solutions. Multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function, which is adopted to manage the system constraints of PELD problems. The proposed IGAMU integrates the IGA with the MU. Two practical examples are employed to demonstrate that the proposed algorithm has the benefits of straightforwardness, ease of implementation, better effectiveness than previous methods, better effectiveness and efficiency than the genetic algorithm (GA) with MU (GA-MU), automatic adjustment of the randomly assigned penalty to an appropriate value and the requirement for only a small population when applied to real-life PELD operations