An efficient memetic algorithm for the multi-stage supply chain network problem

A supply chain is dynamic and involves the constant flow of information, production, services, and funds from suppliers to customers between different stages. In this paper, a memetic algorithm (MA, a hybrid genetic algorithm) is developed to find the strategy that can give the lowest cost of the physical distribution flow. The proposed MA is combined with the genetic algorithm (GA), a multi-greedy heuristic method (GH), three local search methods (LSMs): the pairwise exchange procedure (XP), the insert procedure (IP), and the remove procedure (RP), the Fibonacci number procedure, and the linear programming technique (LP) to improve the tradition genetic algorithm (GA). Preliminary computational experiments demonstrate the efficiency and performance of the proposed MA.     

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.     

Two metaheuristics to solve a multi-item multiperiod inventory control problem under storage constraint and discounts

In this paper, a multi-item multiperiod inventory control problem with all-unit and/or incremental quantity discount policies under limited storage capacity is presented. The independent random demand rates of the items in the periods are known and the items are supplied in distinct batch sizes. The cost consists of ordering, holding, and purchasing. The objective is to find the optimal order quantities of all items in different periods such that the total inventory cost is minimized and the constraint is satisfied. A mixed binary integer programming model is first developed to model the problem. Then, a parameter-tuned genetic algorithm (GA) is employed to solve it. Since there is no benchmark available in the literature, a memetic algorithm (MA) is utilized as well to validate and verify the results obtained. The model implementation is next presented using some numerical examples and finally the performances of the proposed GA and MA are compared using two statistical tests and a simple additive weighting method. The results show that GA has better performance than MA in terms of average objective function value and average run time using the two comparison procedures.     

New Optimal Method for Complicated Assembly Curves Fitting

The optimisation fitting problems between two 3D parts with complicated profiles are the key focus in the problem of "body-in-white" (BIW) assembly for cars, which causes many assembly fitting issues. The paper present a novel algorithm based on a genetic algorithm (GA) to solve such assembly problems and treats it as a two-space curves-fitting problem. The analytical model of the global optimal fitting position of two space curves is discussed, and the mathematical optimal functions for 2D and 3D curves are set up separately. The global research procedure based on the genetic algorithm is described in detail, and some improved ways for GA in constrained optimisation are also discussed in the paper.     

Applying simulated annealing to cellular manufacturing system design

Cell formation and cellular layout design are the two main steps in designing a cellular manufacturing system (CMS). In this paper, we will present an integrated methodology based on a new concept of similarity coefficients and the use of simulated annealing (SA) as an optimization tool. In comparison with the previous works, the proposed methodology takes into account relevant production data, such as alternative process routings and the production volumes of parts. The SA-based optimization tool is parallel in nature and, hence, can reduce the computation time significantly, so it is capable of handling large-scale problems. Finally, the SA-based procedure is compared with a genetic algorithm (GA) based procedure and it will be shown that the SA-based algorithm can be as effective as a GA-based algorithm, but with less computational time and effort.     

一种运用图论进行FMS无死锁调度的方法

在制造系统中 ,必须防止死锁的发生。本文提出一种在制造系统 (带有限缓冲区 )中搜索最优的无死锁调度算法。此算法建立在遗传算法的基础上 ,运用图论算法来保证无死锁调度结果。为了保证遗传算法生成的调度策略能够满足所要求的约束 ,运用图论方法选择无死锁个体 ,添加缓冲区 ,从而在保证了系统的主要性能指标的同时 ,得到系统可行的无死锁的调度结果。本文的主要创新之处在于提出了一种考虑系统缓冲区的无死锁调度方法。     

Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented .     

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.     

一种求解Job-shop调度问题的遗传局部搜索算法

利用遗传局部搜索算法求解了作业车间调度问题,遗传算法中的染色体编码采用基于工序的编码,并用插入式贪婪解码机制将染色体解码至主动调度。为了克服传统遗传算法易于早熟收敛的缺点,设计了一种改进的优先操作交叉IPOX操作和子代产生模式的遗传算法。对于遗传算法每个染色体个体,使用基于N6邻域结构的局部搜索进一步使它们得到改善。利用所提出的混合遗传算法求解基准问题,验证了算法的有效性。     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

侧铣加工刀具回转轮廓误差预测技术研究

在侧铣加工中,刀具磨损和变形引起的刀具回转轮廓误差在实际加工前难以准确预测。提出一种工件形状刀具轮廓映射的辨识试验方法来获取加工过程刀具回转轮廓误差,并通过多因素正交试验获取了不同工况下刀具回转轮廓误差数据库。基于误差数据,采用最小二乘支持向量机（LS-SVM）技术建立了刀具回转轮廓误差预测模型。运用遗传算法优化对所提模型有重要影响的核函数参数和错误惩罚因子, 建立了基于遗传算法优化的最小二乘支持向量机(GA-LS-SVM)模型,并与未经遗传算法优化的LS-SVM模型进行了对比,试验结果表明,GA-LS-SVM预测模型能更好地适用于刀具回转轮廓误差预测。     

A hybrid genetic algorithm for integrated process planning and scheduling problem with precedence constraints

Process planning and scheduling are two important functions in a modern manufacturing system. Although integrating decisions related to these functions gives rise to a hard combinatorial problem, due to the impressive improvement in system performance which is resulted through this integration, developing effective methods to solve this problem is of great theoretical and practical importance. In this research, after formulating the integrated process planning and scheduling problem as a mathematical program, we propose a hybrid genetic algorithm (GA) for the problem. In the proposed algorithm, problem-specific genetic operators are designed to enhance the global search power of GA. Also, a local search procedure has been incorporated into the GA to improve the performance of the algorithm. The model considers precedence relations among job operations, based on which feasible process plans for each job can be represented implicitly. A novel neighborhood function, considering the constraints of a flexible job shop environment and nonlinear precedence relations among operations, is presented to speed up the local search process. In experimental study, the performance of the proposed algorithm has been evaluated based on a number of problems adopted from the literature. The experimental results demonstrate the efficiency of the proposed algorithm to find optimal or near-optimal solutions.     

复合材料层合板的极限强度分析与可靠性优化设计

研究层合板结构基于最终层失效准则的可靠性分析方法,在此基础上,对复合材料层合板可靠性优化设计的方法和算法进行讨论.层合板作为一个系统考虑,其可靠性分析含以下几个步骤:(1)随机载荷和强度参数作为随机变量,利用一次二阶矩法(first order reliability method, FORM)分析单层板的失效概率.(2)假定失效概率最大的单层板首先破坏,然后通过修改刚度矩阵对结构进行再分析,得到剩余结构各单层板的失效概率,从而建立主要失效序列.(3)计算系统的可靠度.在确立复合材料基于最终层失效准则的可靠性分析方法的基础上,以系统可靠度指标最大为目标,研究层合板的可靠性优化问题.建立和发展基于最终层失效准则的复合材料层合板可靠性优化方法以及相应的算法,并对比讨论单变量和多变量优化设计问题.     

基于遗传算法的e-制造调度系统研究

为了实现e-制造系统中制造任务的调度,基于e-制造哲理,对基于产品BOM流的e-制造调度问题进行了研究,提出并设计了e-制造调度系统的求解机制与数学模型,针对所建立的数学模型,设计和开发了一个基于遗传算法的物理e-制造单元调度系统,进而根据物理/逻辑e-制造单元、e-制造系统和e-制造任务之间的映射关系和动态关联,实现了e-制造系统调度的求解过程。最后,通过调度算例分析,表明该系统能有效完成e-制造系统中制造任务的调度。     

ESD模型参数识别的差分进化方法

提出了一种以差分进化算法对静电放电模型进行参数辨识的新方法。以基于Heidler雷电流方程的静电放电模型参数为辨识对象,分别以仿真和实验数据验证了该方法的可行性,并从电流波形整体和局部两方面对拟合效果进行了评估。结果表明,与遗传算法相比,差分进化算法的执行速度更快,所得的辨识参数精度更高,对电流波形的整体和局部关键点的拟合度均高于遗传算法。因此,差分进化算法比遗传算法更适用于解决静电放电模型参数辨识问题。从实例可以看出,差分进化算法不需要过多的初始参数值先验知识,而只需提供一个较宽的初始参数搜索范围即可获得良好的辨识结果。此外,本文还以差分进化算法对Bruce-Golden和Gaussian函数静电放电模型进行参数辨识,验证了该方法的适用性。     

AUTONOMOUS STRUCTURAL HEALTH MONITORING—PART I: MODAL PARAMETER ESTIMATION AND TRACKING

A growing interest for techniques and systems allowing the early detection and monitoring of damage based on vibration analysis is clearly present in different research and application areas, such as civil constructions, mechanical systems, and aircraft and aerospace industry. However, an important element for the applicability of modal-based damage assessment techniques in practice is the automation of the identification and tracking procedure.In this contribution it is shown that, by using a frequency-domain maximum likelihood estimator, features such as high accuracy and confidence bounds for the estimated parameters and robustness for high measurement noise levels create the possibility to automate the modal identification process. For the validation of the model, criteria based on a statistical approach were developed in addition to the well-known criteria such as modal phase colinearity and mode complexity, while the final mode selection is done by means of a fuzzy clustering algorithm. At the same time, an accurate mode-tracking algorithm is presented. It is shown that the problem of coinciding poles, due to shifting or crossing modes under changing structural dynamics, can hamper the mode tracking and a robust solution is proposed. The possibilities and limitations of the automated approach are investigated and tested for a slat track of an Airbus A320 commercial airplane, providing a preliminary study for future monitoring practices during life cycle tests on slat tracks.     

一种结构损伤评估的两阶段法研究

结构损伤会引起结构动力特性的改变,这种改变可以用模态参数和结构参数表示。文中将柔度投影法和遗传算法相结合,提出一种基于不完整模态数据的结构损伤定位和定量评估的两阶段法。第一阶段,用柔度投影法进行损伤定位,并进行深入分析。第二阶段,用遗传算法确定损伤程度。文中提出一种用于遗传搜索的新的目标函数形式。最后,用一六跨平面桁架桥模型进行数值模拟,验证文中所提方法的有效性。     

Genetic algorithm with an improved fitness function for (N)ARX modelling

In this article a new fitness function is introduced in an attempt to improve the quality of the auto-regressive with exogenous inputs (ARX) model using a genetic algorithm (GA). The GA is employed to identify the coefficients and the number of time lags of the models of dynamic systems with the new fitness function which is based on the prediction error and the correlation functions between the prediction error and the input and output signals. The new fitness function provides the GA with a better performance in the evolution process. Two examples of the ARX modelling of a linear and a non-linear (NARX) simulated dynamic system are examined using the proposed fitness function.     

基于遗传算法和神经网络的塔机结构动态优化设计

利用遗传算法和BP神经网络建立复杂结构系统动态优化的计算模型,该模型可代替系统原来的有限元模型,用于振动系统的快速重分析。首先对塔式起重机结构系统进行模态分析及谐响应动力学分析,找出对结构动态特性影响最大的模态频率,再利用灵敏度分析,确定对动态特性较敏感的设计变量作为神经网络的输入变量,并利用正交试验法确定神经网络训练样本,用有限元模型计算出样本点数据,建立反映结构振动特性的人工神经网络模型,最后利用遗传算法对所建立的神经网络模型寻优,得到使结构动态性能最优的设计参数。