基于分组策略的多目标三维装箱算法

目的 针对现有三维装箱算法优化目标单一、优化效率低的问题,提出适用于求解大规模货物装载问题的多目标装箱算法,以提高装箱规划效率,确保货物运输安全。方法 考虑5种现实约束条件,以体积利用率和装载垛型重心偏移量为优化目标,建立多目标货物装载优化模型。采用拟人式装箱对货物进行预分组,减小决策空间,然后结合分组信息与装箱算法生成初始解;引入数据驱动的装箱交叉算子提高算法收敛性;设计多策略变异算子提高算法结果的多样性。结果 以公共数据集和真实航空货物数据作为实验数据进行实验。实验结果表明,在满足多种约束条件下,集装箱装载强异构货物平均体积利用率达到92.0%,重心位置空间偏移从20 cm减少到7.5 cm,并且算法运行时间减少了73.5%。结论 本文所提算法应用于求解大规模多目标三维装箱问题,提高了装箱质量和效率,可为三维装箱算法的工程应用提供参考。     

基于 ADAMS 的含间隙酒瓶装箱机构优化设计

利用ADAMS分析软件,对酒瓶装箱机构的建模仿真、优化进行了分析和探讨。在此基础上,基于间隙矢量模型,建立了考虑铰接处两构件由于制造和装配误差引起的间隙运动副模型,描述了间隙运动副两构件间的相对运动关系和基于非线性碰撞接触模式的动力学约束关系,分析了运动副间隙对系统运动学及动力学特性的影响。结果表明利用ADAMS进行仿真,是研究含间隙运动副机构的一种有效方法,可为多体机构设计及动力学行为描述提供精确预测,对其他含间隙运动副机构的动力学研究具有普遍意义。     

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。     

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.     

Dynamic optimization of chemical engineering problems using a control vector parameterization method with an iterative genetic algorithm

An approach that combines genetic algorithm (GA) and control vector parameterization (CVP) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. In the new CVP method, control variables are approximated with polynomials based on state variables and time in the entire time interval. The iterative method, which reduces redundant expense and improves computing efficiency, is used with GA to reduce the width of the search region. Constrained dynamic optimization problems are even more difficult. A new method that embeds the information of infeasible chromosomes into the evaluation function is introduced in this study to solve dynamic optimization problems with or without constraint. The results demonstrated the feasibility and robustness of the proposed methods. The proposed algorithm can be regarded as a useful optimization tool, especially when gradient information is not available.     

精密装甲器材包装箱的优化设计及其堆码形式的抗压性能分析

目的对包装箱进行优化设计,并分析其在不同堆码形式下的抗压性能。方法设计精密装甲器材包装箱的规格以及堆码形式的优化方案,并用有限元分析法分析不同堆码形式下包装箱箱体抗压性能。结果优化设计的5种规格的包装箱在单独堆码和混合堆码形式下,其箱体的最大应力和最大变形均小于线形低密度聚乙烯材料的拉伸屈服应力。结论优化设计的5种规格包装箱在不同堆码形式下箱体的抗压性能满足精密装甲器材包装的性能要求。     

Global optimization for signomial discrete programming problems in engineering design

Signomial discrete programming (SDP) problems arise frequently in a variety of real applications. Although many optimization techniques have been developed to solve an SDP problem, they use too many binary variables to reformulate the problem for finding a globally optimal solution or can only derive a local or an approximate solution. This article proposes a global optimization method to solve an SDP problem by integrating an efficient linear expression of single variable discrete functions and convexification techniques. An SDP problem can be converted into a convex mixed-integer programming problem solvable to obtain a global optimum. Several illustrative examples are also presented to demonstrate the usefulness and effectiveness of the proposed method.     

A deterministic global approach for mixed-discrete structural optimization

This study proposes a novel approach for finding the exact global optimum of a mixed-discrete structural optimization problem. Although many approaches have been developed to solve the mixed-discrete structural optimization problem, they cannot guarantee finding a global solution or they adopt too many extra binary variables and constraints in reformulating the problem. The proposed deterministic method uses convexification strategies and linearization techniques to convert a structural optimization problem into a convex mixed-integer nonlinear programming problem solvable to obtain a global optimum. To enhance the computational efficiency in treating complicated problems, the range reduction technique is also applied to tighten variable bounds. Several numerical experiments drawn from practical structural design problems are presented to demonstrate the effectiveness of the proposed method.     

Efficient Pareto Frontier Exploration using Surrogate Approximations

In this paper we present an efficient and effective method of using surrogate approximations to explore the design space and capture the Pareto frontier during multiobjective optimization. The method employs design of experiments and metamodeling techniques (e.g., response surfaces and kriging models) to sample the design space, construct global approximations from the sample data, and quickly explore the design space to obtain the Pareto frontier without specifying weights for the objectives or using any optimization. To demonstrate the method, two mathematical example problems are presented. The results indicate that the proposed method is effective at capturing convex and concave Pareto frontiers even when discontinuities are present. After validating the method on the two mathematical examples, a design application involving the multiobjective optimization of a piezoelectric bimorph grasper is presented. The method facilitates multiobjective optimization by enabling us to efficiently and effectively obtain the Pareto frontier and identify candidate designs for the given design requirements.     

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.     

基于混合遗传算法的多箱型三维装箱问题研究

目的 提高电商仓储领域打包环节包装箱的利用率。方法 针对电商仓储领域多箱型多种物品类型的三维装箱问题,建立混合整数规划的数学模型,设计基于启发式经验规则和多种算子组合的装箱过程模块算法。分别从装箱顺序和带有改进型算子这两方面设计多箱型三维装箱问题混合遗传算法,对装箱方案进行优化。结果 经实验证明,在装箱顺序优化环节PSO–HGA算法系列中,PSO–HGA–S1算法最优。在带有改进算子的混合遗传算法中,IPO–HGA–S1算法最优。结论 文中设计的混合遗传算法能很好地提高电商仓储领域打包环节包装箱的利用率。     

包装设计的销售力

分析了在商品销售竞争加剧的今天,商品包装应该以自身具备的销售能力帮助企业实现销售目的,以及当前包装销售力不能达到预期目标的3种原因,即,过度包装、结构同质化和滥用材料。并在此基础上,提出了以绿色包装设计适应消费者、以务实简约的设计思想引导消费者、以注重消费者体验沟通的设计满足消费者,以此3种设计思想来提升商品包装的销售力。     

数字孪生驱动的物联型混单包装线多工位联动优化

目的 解决生产扰动给混单包装线带来的交货期延误、成本额外增加等问题.方法 将传统的混单包装线物联网化,实现生产资源的实时感知,及时获取生产现场的真实信息;面向物联型混单包装线的生产环境,基于数字孪生体系构建起物理世界与数字世界的联系,通过物联网的实体感知和网络的信息传输形成孪生数据集,建立孪生数据驱动的混单包装线多工位联动优化的信息架构;依据包装任务的工序约束,建立起多个工位的动态关联,实现工位间生产信息的实时协调,进而通过算法实现工位任务队列的联动式智能优化.结果 通过在某摩托车外胎包装生产线上的应用,优化后工序任务队列的目标函数值相比最高值下降了36.1％,验证了文中提出的物联型混单包装线多工位联动优化方法的有效性和可行性.结论 文中提出的混单包装线物联网化能为智能制造的实时提供硬件支撑,基于数字孪生的多工位联动优化提供了智能化决策工具支持.     

Topology optimization of acoustic–structure interaction problems using a mixed finite element formulation

The paper presents a gradient‐based topology optimization formulation that allows to solve acoustic–structure (vibro‐acoustic) interaction problems without explicit boundary interface representation. In acoustic–structure interaction problems, the pressure and displacement fields are governed by Helmholtz equation and the elasticity equation, respectively. Normally, the two separate fields are coupled by surface‐coupling integrals, however, such a formulation does not allow for free material re‐distribution in connection with topology optimization schemes since the boundaries are not explicitly given during the optimization process. In this paper we circumvent the explicit boundary representation by using a mixed finite element formulation with displacements and pressure as primary variables (a u /p‐formulation). The Helmholtz equation is obtained as a special case of the mixed formulation for the elastic shear modulus equating to zero. Hence, by spatial variation of the mass density, shear and bulk moduli we are able to solve the coupled problem by the mixed formulation. Using this modelling approach, the topology optimization procedure is simply implemented as a standard density approach. Several two‐dimensional acoustic–structure problems are optimized in order to verify the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.     

Inverse scattering analysis in acoustics via the BEM and the topological-shape sensitivity method

Inverse scattering acoustics find practical applications in the detection and imaging of objects embedded in continuous media as well as in finding the optimum geometric configuration of an object to produce a given radiation performance. This work introduces a boundary element method (BEM) approach for the solution of acoustic identification and optimization problems via a topological-shape sensitivity method. The devised optimization tool takes advantage of the inherent characteristics of BEM to effectively solve the forward and adjoint acoustic problems arising in the topological derivative formulation and to deal with infinite domains. The objectives for the identification and optimization problems are to achieve a prescribed sound pressure at a given region of the problem domain. The locus giving extreme values for the topological derivative indicates the optimum positions for the placement of sound-hard scatterers in order to minimize the cost function. The proposed implementation has the ability to deal with initially empty design spaces as well as with design spaces containing pre-existent scatterers. The capabilities of the method are demonstrated by solving a number of identification and optimization problems.     

基于非线性规划的包装箱组托优化设计

目的以相同规格包装箱为研究对象,求解包装箱组托码放规则的最优化方案。方法以H2DPP模型为基础,采用块启发式算法,建立非线性规划模型,利用Lingo软件求解最优码放数量及各决策变量值。结果在托盘规格一定的情况下,包装箱规格越大,目标函数值越小;托盘长宽与包装箱长宽分别成整数比时,迭代的次数要远小于其他情况。结论利用Lingo软件求解非线性规划组托模型,用时不超过0.05 s,该方案更高效、准确地解决了流通过程中包装箱的组托问题,实现了组托方法的最优化。     

OPTIMUM SHAPE DESIGN AND POSITIONING OF FEATURES USING THE BOUNDARY INTEGRAL EQUATION METHOD

A design optimization procedure is developed using the boundary integral equation (BIE) method for linear elastostatic two-dimensional domains. Optimal shape design problems are treated where design variables are geometric parameters such as the positions and sizing dimensions of entire features on a component or structure. A fully analytical approach is adopted for the design sensitivity analysis where the BIE is implicitly differentiated. The ability to evaluate response sensitivity derivatives with respect to design variables such as feature positions is achieved through the definition of appropriate design velocity fields for these variables. How the advantages of the BIE method are amplified when extended to sensitivity analysis for this category of shape design problems is also highlighted. A mathematical programming approach with the penalty function method is used for solving the overall optimization problem. The procedure is applied to three example problems to demonstrate the optimum positioning of holes and optimization of radial dimensions of circular arcs on structures.     

An improved weighting method with multibounds formulation and convex programming for multicriteria structural optimization

This paper presents an improved weighting method for multicriteria structural optimization. By introducing artificial design variables, here called as multibounds formulation (MBF), we demonstrate mathematically that the weighting combination of criteria can be transformed into a simplified problem with a linear objective function. This is a unified formulation for one criterion and multicriteria problems. Due to the uncoupling of involved criteria after the transformation, the extension and the adaptation of monotonic approximation‐based convex programming methods such as the convex linearization (CONLIN) or the method of moving asymptotes (MMA) are made possible to solve multicriteria problems as efficiently as for one criterion problems. In this work, a multicriteria optimization tool is developed by integrating the multibounds formulation with the CONLIN optimizer and the ABAQUS finite element analysis system. Some numerical examples are taken into account to show the efficiency of this approach. Copyright © 2001 John Wiley & Sons, Ltd.     

Time-response-based evolutionary optimization

Solutions to engineering problems are often evaluated by considering their time responses; thus, each solution is associated with a function. To avoid optimizing the functions, such optimization is usually carried out by setting auxiliary objectives (e.g. minimal overshoot). Therefore, in order to find different optimal solutions, alternative auxiliary optimization objectives may have to be defined prior to optimization. In the current study, a new approach is suggested that avoids the need to define auxiliary objectives. An algorithm is suggested that enables the optimization of solutions according to their transient behaviours. For this optimization, the functions are sampled and the problem is posed as a multi-objective problem. The recently introduced algorithm NSGA-II-PSA is adopted and tailored to solve it. Mathematical as well as engineering problems are utilized to explain and demonstrate the approach and its applicability to real life problems. The results highlight the advantages of avoiding the definition of artificial objectives.