Two meta-heuristics for a multi-period minisum location–relocation problem with line restriction

This paper investigates a multiperiod rectilinear distance minisum location problem, as a mixed-integer nonlinear programming (MINLP) model with a line-shaped barrier restriction, in which the starting point of the barrier uniformly distributed in the plane. The objective function of this model is to minimize the sum of the costs associated with the expected weighted barrier distance of the new facility from the existing facilities and the costs incurred by location-dependent relocation during the planning horizon. Then, a lower bound based on the forbidden region is presented. To show the validation of the presented model, a number of numerical examples are illustrated. The associated results show that the optimization software is effective for small-sized problems. However, the optimization software is unable to find an optimum solution for large-sized problems in a reasonable time. Thus, two meta-heuristics, namely genetic algorithm (GA) and imperialist competitive algorithm (ICA), are proposed. Finally, the associated results are compared and discussed.     

考虑不同类型可靠性-成本函数下的可靠性优化设计

在可靠性冗余优化设计时，完成某个子系统功能的元件可能不止一个种类。不同种类的元件有不同的可靠性、成本及重量等参数指标，且可靠性-成本函数也可能为非连续函数。考虑子系统中冗余组件的多样性以及可靠性-成本之间可能的三种函数关系，即简单的阶跃函数、连续函数以及分段连续而在分段点不连续的非连续函数，建立了可靠性冗余优化的混合整数非线性规划数学模型，并以实例进行了说明。     

A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem

This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration.     

存在共因失效的复杂可修系统可靠性评估

鉴于传统方法在处理存在由部件选择性失效传播引起的共因失效的复杂可修系统可靠性与可用性评估问题时的缺陷,提出一种基于元胞自动机思想的蒙特卡洛模拟方法用以评估存在共因失效的复杂可修系统的可靠性。该方法首先将系统转化成二终端网络,然后基于问题的基本假设去模拟不同时刻下网络各边对应的状态值,利用元胞自动机并行搜索的优势判断每次模拟过程中不同时刻下网络的连通性,从而推导出计算系统可靠性、瞬时可用性、区间可用性、系统平均维修时间以及系统单位时间平均维修次数的具体算法。最后,通过算例对算法进行了说明。     

基于遗传算法的复杂系统可靠性冗余优化研究

由于复杂系统可靠度函数常具有非线性的特点，因此如何确定复杂系统各单元冗余数，既满足设计可靠度要求、又使系统成本最小是冗余优化问题中的一个难点。文中针对复杂系统的特点，提出一种基于遗传算法的多目标可靠性冗余优化方法。同时，将其和现有的可靠性冗余优化方法进行了比较分析，结果表明该方法具有全局最优和更强的适应性与求解能力。     

Production planning in the presence of remanufactured spare components: an application in the airline industry

The process of remanufacturing is attractive economically and environmentally for both manufacturers and customers. This paper addresses a problem in the repairable spare parts remanufacturing industry to find the cost-optimal production strategy incorporating reconditioned components. New and reconditioned components are used to carry out replacements in order to honor warranty commitments. Key production decisions, such as when remanufacturing should commence, how long the warranty period should be, and how many returned components should be reconditioned are considered. The availability of reconditioned components and their discounted costs are also incorporated in the mathematical model. The goal is to investigate the interaction between these decisions and their impacts on the manufacturing system and the customer. An application to the remanufacturing of rotable spare parts in the airline industry is presented.     

基于可用度的预防维修和均值控制图联合优化模型

针对高可用度要求的生产系统(即可修复系统,以下简称系统),提出一种以单位时间成本为约束,可用度最大化为目标的预防维修和均值控制图联合优化模型。首先,考虑到系统运行存在受控、失控两种状态以及均值控制图两类错误(漏报警和误报警)发生的概率,在完美维修假设前提下,构建了4种系统运行(更新)情景(S1~S4)。然后,建立了每种更新情景的可用度和经济模型,并在此基础上建立了系统的可用度目标函数和经济约束函数,以实现单位时间成本约束条件下预防维修和均值控制图联合优化的系统可用度最大化目标。针对所建立的联合优化模型,通过实例对比验证了模型的有效性,并利用遗传算法对决策变量进行优化,实例优化结果表明,该模型能够有效提高系统可用度,并能降低系统成本。最后,通过正交试验、回归分析等方法对模型进行了参数的灵敏度分析。     

Reliability-based robust optimization design of automobile components with non-normal distribution parameters

In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (>0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (>0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.     

GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

An extended crowding genetic algorithm (ECGA) is introduced for solving optimal pumpconfiguration problem, which was presented by T.Westerlund in 1994. This problem has been found tobe non-convex, and the objective hation contained several local optima and global optimality couldnot be ensured by all the traditional MINLP optimization method. The concepts of species conservingand composite encoding are introduced to crowding genetic algorithm (CGA) for mantain the diver-sity of population more effectively and coping with the continuous and/or discrete variables in MINLPproblem. The solution of three-levels pump configuration got from DICOPT++ software (OA algo-rithm) is also given. By comparing with the solutions obtained from DICOPT++, ECP method, andMIN-MIN method, the ECGA algorithm proved to be very effective in finding the global optimalsolution of multi-levels pump configuration via using the problem-specific information.     

基于Internet网络的主从式并行遗传算法及其在叶栅优化中的应用

为了解决遗传算法在优化中由于适应度评价很费时而导致计算时间过长的问题 ,本文发展了一种基于In ternet网络实现的主从式并行遗传算法。在函数优化的测试实验中 ,通过控制待优化函数适应度评价的时间 ,验证了主从式模型在适应度评价很费时且远远超过通讯时间时将获得接近于线性的加速比 ,讨论了主从式并行遗传算法应用于气动性能优化中的可行性。通过二维叶栅的优化算例 ,证明了本文提出的算法适合于需要大计算资源的叶栅气动优化设计     

Tolerance design optimization of machine elements using genetic algorithm

An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used for the design of tolerances of machine elements to obtain the global optimal solution. The objective is to design the optimum tolerances of the individual components to achieve the required assembly tolerance, zero percentage rejection of the components and minimum cost of manufacturing. The proposed procedure using GA is described in this paper for two tolerance design optimization problems: gear train and overrunning clutch assemblies. Results are compared with conventional techniques and the performances are analyzed.     

基于多目标优化策略的螺旋弹簧可靠性稳健优化设计

应用可靠性稳健优化设计理论和多目标决策方法,将车辆螺旋弹簧的可靠性稳健优化设计转化为多目标问题.运用层次分析法选取粒子群算法中的全局极值和个体极值,提出基于层次分析法的多目标粒子群算法,并将该算法应用于可靠性稳健优化设计的多目标模型求解中.与传统方法相比,该方法简便、易行,并能迅速准确地得到车辆螺旋弹簧的可靠性稳健优化设计信息.     

粒子群及局部搜索算法在串并联系统结构优化中的应用

将粒子群算法和局部搜索算法相结合,用于解决串并联系统的冗余分配问题。介绍串并联系统结构,确定该冗余分配问题以系统最小费用为优化目标,以系统可用度不能低于某一确定值为约束条件。应用概率生成函数(UGF)方法计算系统可用度,将粒子群算法和局部搜索算法相结合进行优化求解,并给出迭代过程。通过实例对优化迭代过程进行具体说明。     

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for an...     

An approach to multi-criteria assembly sequence planning using genetic algorithms

This paper focuses on multi-criteria assembly sequence planning (ASP) known as a large-scale, time-consuming combinatorial problem. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. Genetic algorithm (GA) is the most widely known type of evolutionary computation method, incorporating biological concepts into analytical studies of systems. In this research, an approach is proposed to optimize multi-criteria ASP based on GA. A precedence matrix is proposed to determine feasible assembly sequences that satisfy precedence constraints. A numerical example is presented to demonstrate the performance of the proposed algorithm. The results of comparison in the provided experiment show that the developed algorithm is an efficient approach to solve the ASP problem and can be suitably applied to any kind of ASP with large numbers of components and multi-objective functions.     

Optimizing bi-objective redundancy allocation problem with a mixed redundancy strategy

Redundancy Allocation Problem (RAP) is a challenging subject which has attracted the attention of many authors. Generally, in the RAP there are two strategies for using the redundant components: active and standby. In this paper a new redundancy strategy, called mixed redundancy, is introduced and considered in a multi-objective optimization RAP. Results demonstrate that the new strategy increases the reliability value of the system considerably. This improvement can be very important for system designers, because the reliability of any systems with the structure of redundant components can be increased by changing the redundancy strategy, not by only adding redundant component. Moreover, this improvement dose not increases the cost and other known physical characteristics of the system.     

New optimization method, the algorithms of changes, for heat exchanger design

Heat exchangers are widely used in the process engineering such as the chemical industries,the petroleum industries,and the HVAC applications etc.An optimally designed heat exchanger cannot only help the optimization of the equipment size but also the reduction of the power consumption.In this paper,a new optimization approach called algorithms of changes (AOC) is proposed for design and optimization of the shell-tube heat exchanger.This new optimization technique is developed based on the concept of the book of changes (I Ching) which is one of the oldest Chinese classic texts.In AOC,the hexagram operations in I Ching are generalized to binary string case and an iterative process,which imitates the I Ching inference,is defined.Before applying the AOC to the heat exchanger design problem,the new optimization method is examined by the benchmark optimization problems such as the global optimization test functions and the travelling salesman problem (TSP).Based on the TSP results,the AOC is shown to be superior to the genetic algorithms (GA).The AOC is then used in the optimal design of heat exchanger.The shell inside diameter,tube outside diameter,and baffles spacing are treated as the design (or optimized) variables.The cost of the heat exchanger is arranged as the objective function.For the heat exchanger design problem,the results show that the AOC is comparable to the GA method.Both methods can find the optimal solution in a short period of time.     

Modeling of binocular vision system for 3D reconstruction with improved genetic algorithms

This paper models the binocular vision system focused on 3D reconstruction and describes an improved genetic algorithm (GA) for estimating camera system parameters. The two-camera system model that takes into account camera radial distortion includes a total of 24 parameters. The proposed improved GA is used to solve this nonlinear optimization problem with high dimension. In our improved GA, the adaptive control of camera parameter search interval and the catastrophe strategy with elitist preservation are employed. The experimental results indicate that our improved GA is effective to solve the multi-peak function optimization problem and the 3D reconstruction accuracy of the binocular vision system is promising.     

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design(CMSOD) belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search(ACAM) is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm(GA) and particle swarm optimization(PSO) are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.     

压电智能桁架作动器配置的优化设计

利用优化设计的思路来探讨作动器的优化配置,并将优化模型分为两类:待控模态数目固定前提下优化作动器的数目和位置;固定作动器的数目,优化作动器的位置以控制更多的模态数目。并根据该类优化问题为多目标优化的性质,提出了一种基于模糊逻辑推理的多目标遗传算法,构造了一个能与适应度相对应的评价个体优劣的评价体系,以引导处理多目标问题的遗传算法的进化过程。数值算例结果表明了所提方法是正确的和有效的。