An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system

This paper proposes an improved artificial bee colony (IABC) algorithm for addressing the distributed flow shop considering the distance coefficient found in precast concrete production system, with the minimisation of the makespan. In the proposed algorithm, each solution is first represented by a two-dimensional vector, where the first dimensional vector is the factory and the second dimensional vector lists the operation scheduling sequence of each factory. Second, considering the distributed problem feature, a distributed iterated greedy heuristic (DIG) is developed where destruction and construction processes are designed in detail while considering the distributed structures. Third, an efficient population initialisation method that considers the factory workload balance is presented. Then, a local search approach that randomly replaces two factories with two randomly selected jobs and that finds an optimal position for the two inserted operations via the DIG method is proposed. For the canonical ABC algorithm, using the DIG approach, the main three parts are improved, namely, the employee, onlooker, and scout bees. Finally, the proposed algorithm is tested on sets of extended instances based on the well-known benchmarks. Through an analysis of the experimental results, the highly effective proposed IABC algorithm is compared to several efficient algorithms drawn from the literature.     

改进人工蜂群算法优化的LSSVM在混合气体定量分析中的应用

针对在易燃易爆混合气体定量分析中因交叉敏感易产生测量误差以及最小二乘支持向量机（least squares support vector machine,LSSVM）参数难以确定的问题,提出一种改进人工蜂群（improved artificial bee colony,IABC）算法优化的最小二乘支持向量机。首先,在标准人工蜂群（artificial bee colony, ABC）算法中引入自适应递减因子以更新步长,并结合轮盘赌和反向轮盘赌改进待工蜂跟随概率公式,从而提高收敛精度;然后,利用改进后的人工蜂群算法对最小二乘支持向量机的惩罚参数C和核参数σ²进行优化;最后,利用优化后的参数重建最小二乘支持向量机定量分析模型,并与利用常用的混合气体定量分析方法——粒子群优化（particle swarm optimization,PSO）算法优化的最小二乘支持向量机定量分析模型进行对比。实验结果表明,在交叉敏感状态下,采用改进人工蜂群算法优化的最小二乘支持向量机时的建模总时间和各组分气体浓度测量的平均相对误差均低于采用粒子群算法优化的,有效提高了混合气体的浓度测量精度。研究表明,改进人工蜂群算法优化的最小二乘支持向量机可为混合气体定量分析提供理论支撑,具有一定的工程应用价值。     

Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm

We consider a joint decision model of cell formation and task scheduling in cellular manufacturing system under dual-resource constrained (DRC) setting. On one hand, machines and workers are multi-functional and/or multi-skilled, and they are grouped into workstations and cells. On the other hand, there is a processing sequence among operations of the parts which needs to be dispatched to the desirable workstations for processing. Inter-cell movements of parts can reduce the processing times and the makespan but will increase the inter-cell material handling costs. The objective of the problem is to minimise the material handling costs as well as the fixed and operating costs of machines and workers. Due to the NP-hardness of the problem, we propose an efficient discrete bacteria foraging algorithm (DBFA) with elaborately designed solution representation and bacteria evolution operators to solve the proposed problem. We tested our algorithm using randomly generated instances with different sizes and settings by comparing with the original bacteria foraging algorithm and a genetic algorithm. Our results show that the proposed DBFA has better performance than the two compared algorithms with the same running time.     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

面向印制电路板钻孔任务动态调度的短视策略

印制电路板钻孔任务因随机到达和工艺要求而难以调度。考虑该问题的NP难性质,提出基于优先规则和智能算法的短视策略。该策略采用事件驱动的再调度机制,在任务到达和任务完工时触发优化算法对当前未开工任务进行决策。为了高效求解每个决策时刻的优化问题,构建了嵌入局部优势定理的模拟退火和变邻域搜索算法,其初始解由优先规则获得。通过计算实验,在不同调度环境下对比两种智能算法与经典优先规则的表现。实验结果表明,智能算法在多数目标下的优化效果较优先规则可提升20%以上,变邻域搜索的优化效果略好于模拟退火,但是模拟退火的计算效率高一倍。     

本体概念分类的遗传算法

提出了一种基于遗传算法的本体概念分类规则学习方法(GAOCL).该方法从已有的本体库或知识库中获取实例作为训练样本,通过遗传算法获取相关的概念分类规则,实现概念实例的有效分类,以达到扩充和丰富本体的目的.首先,在编码过程中采用了可变长度的规则集合作为个体,以满足不同概念对分类规则数目的不同要求.其次,定义了规则集相对覆盖率,并以此作为适应值函数,评估个体对概念实例的分类效果,实现优化迭代.最后,给出了基于遗传算法的本体分类规则学习算法.利用一组标准样本集对该算法与同类算法进行了比较,实验结果表明该算法具有很好的收敛性,并且能获得较好的分类效果.     

Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process

Multi-pass milling is a common manufacturing process in practical production. Parameter optimisation is of great significance since the parameters largely affect the production time, quality, cost and some other process performance measures. However, the parameter optimisation of the multi-pass milling process is a nonlinear constrained optimisation problem. It is very difficult to obtain satisfactory results by the traditional optimisation methods. Therefore, in this paper, a new optimisation technique based on the electromagnetism-like mechanism (EM) algorithm is proposed to solve the parameter optimisation problem in a multi-pass milling process. The EM algorithm is a population based meta-heuristic algorithm for unconstrained optimisation problems. As the parameter optimisation problem is a constrained problem, the proposed approach handles the constraints of the problem by improving the charge calculation formula combined with the feasibility and dominance rules at the same time. This paper also puts forward flexible cutting strategies to simultaneously optimise the depth of cut for each pass, cutting speed and feed to improve solutions. A case study is presented to verify the effectiveness of the proposed approach. The results show that the proposed method is better than other algorithms and achieves significant improvement.     

Bi-Directional Evolutionary Topology Optimization Using Element Replaceable Method

In the present paper, design problems of maximizing the structural stiffness or natural frequency are considered subject to the material volume constraint. A new element replaceable method (ERPM) is proposed for evolutionary topology optimization of structures. Compared with existing versions of evolutionary structural optimization methods, contributions are twofold. On the one hand, a new automatic element deletion/growth procedure is established. The deletion of a finite element means that a solid element is replaced with an orthotropic cellular microstructure (OCM) element. The growth of an element means that an OCM element is replaced with a solid element of full materials. In fact, both operations are interchangeable depending upon how the value of element sensitivity is with respect to the objective function. The OCM design strategy is beneficial in preventing artificial modes for dynamic problems. Besides, the iteration validity is greatly improved with the introduction of a check position (CP) technique. On the other hand, a new checkerboard control algorithm is proposed to work together with the above procedure. After the identification of local checkerboards and detailed structures over the entire design domain, the algorithm will fill or delete elements depending upon the prescribed threshold of sensitivity values. Numerical results show that the ERPM is efficient and a clear and valuable material pattern can be achieved for both static and dynamic problems.     

基于技术进化趋势的裁剪方法研究

裁剪是TRIZ中一种有效解决问题的方法,其通过删除问题元件,重组系统有用功能进行产品创新设计,达到简化系统与优化产品的目的.为了启发和引导设计者在裁剪过程中有效地利用资源,在使用裁剪规则引导创新设计的基础上,以具有预测性和启发性的技术进化趋势作为知识源,综合分析进化趋势解题与裁剪规则引导功能重组的资源来源,构建了进化趋势与裁剪规则的映射关系,提出了基于裁剪规则的多层次进化趋势选择策略,利用进化趋势辅助功能重组,构建了基于进化趋势的裁剪方法过程模型.最后通过钢带铠装机裁剪实例验证了此方法的合理性.     

Ambiguity Resolution in Direction of Arrival Estimation with Linear Antenna Arrays Using Differential Geometry

Linear antenna arrays (LAs) can be used to accurately predict the direction of arrival (DOAs) of various targets of interest in a given area. However, under certain conditions, LA suffers from the problem of ambiguities among the angles of targets, which may result in misinterpretation of such targets. In order to cope up with such ambiguities, various techniques have been proposed. Unfortunately, none of them fully resolved such a problem because of rank deficiency and high computational cost. We aimed to resolve such a problem by proposing an algorithm using differential geometry. The proposed algorithm uses a specially designed doublet antenna array, which is made up of two individual linear arrays. Two angle observation models, ambiguous observation model (AOM) and estimated observation model (EOM), are derived for each individual array. The ambiguous set of angles is contained in the AOM, which is obtained from the corresponding array elements using differential geometry. The EOM for each array, on the other hand, contains estimated angles of all sources impinging signals on each array, as calculated by a direction-finding algorithm such as the genetic algorithm. The algorithm then contrasts the EOM of each array with its AOM, selecting the output of that array whose EOM has the minimum correlation with its corresponding AOM. In comparison to existing techniques, the proposed algorithm improves estimation accuracy and has greater precision in antenna aperture selection, resulting in improved resolution capabilities and the potential to be used more widely in practical scenarios. The simulation results using MATLAB authenticates the effectiveness of the proposed algorithm.     

A branch-and-bound algorithm for minimizing the sum of maximum earliness and tardiness with unequal release times

This article addresses the problem of minimizing the sum of maximum earliness and tardiness on a single machine with unequal release times. It is proven that this problem is NP-hard in the strong sense and a branch-and-bound algorithm is developed as an exact method. In the proposed algorithm, modified dispatching rules based on different release times are proposed as the upper bound, while a procedure considering preemption assumption is used to obtain a good lower bound. Also, dominance rules based on no unforced idle time, adjacent pairwise interchanges in the base problem, and job blocks are used to fathom the nodes. In order to evaluate the efficiency of the proposed algorithm, 4,860 instances were randomly generated, varying from 7 to 1,000 jobs. It is shown that the branch-and-bound algorithm was capable of optimally solving 94.1% of the instances, showing its efficiency in solving all problem sizes.     

考虑任务拆分特性与簇准备时间的并行机调度

在印制电路板钻孔任务调度等工程实际中,普遍存在一类具有任务拆分特性与簇准备时间的并行机调度问题,尚缺乏高效的优化模型和方法。针对该问题,首先建立以总拖期最小为目标的数学模型,以约束的形式将两个现有优势定理嵌入其中。为了高效求解实际规模问题,进一步提出嵌入优势定理的模拟退火算法。最后,基于随机生成的算例构造计算实验,以验证所建模型和算法的有效性。实验结果表明,嵌入优势定理的数学模型在问题求解规模和计算效率方面均优于现有数学模型,嵌入优势定理的模拟退火算法同样优于现有模拟退火算法。     

A filtered-beam-search-based heuristic algorithm for flexible job-shop scheduling problem

Scheduling for the flexible job-shop is a very important issue in both fields of combinatorial optimization and production operations. However, due to combination of the routing and sequencing problems, flexible job-shop scheduling problem (FJSP) presents additional difficulty than the classical job-shop scheduling problem and requires more effective algorithms. This paper developed a filtered-beam-search-based heuristic algorithm (named as HFBS) to find sub-optimal schedules within a reasonable computational time for the FJSP with multiple objectives of minimising makespan, the total workload of machines and the workload of the most loaded machine. The proposed algorithm incorporates dispatching rules based heuristics and explores intelligently the search space to avoid useless paths, which makes it possible to improve the search speed. Through computational experiments, the performance of the presented algorithm is evaluated and compared with those of existing literature and those of commonly used dispatching rules, and the results demonstrate that the proposed algorithm is an effective and practical approach for the FJSP.     

A new paradigm for rule-based scheduling in the wafer probe centre

This paper addresses the scheduling problem in the wafer probe centre. The proposed approach is based on the dispatching rule, which is popularly used in the semiconductor manufacturing industry. Instead of designing new rules, this paper proposes a new paradigm to utilize these rules. The proposed paradigm formulates the dispatching process as a 2-D assignment problem with the consideration of information from multiple lots and multiple pieces of equipment in an integrated manner. Then, the dispatching decisions are made by maximizing the gains of multiple possible decisions simultaneously. Besides, we develop a genetic algorithm (GA) for generating good dispatching rules through combining multiple rules with linear weighted summation. The benefits of the proposed paradigm and GA are verified with a comprehensive simulation study on three due-date-based performance measures. The experimental results show that under the proposed paradigm, the dispatching rules and GA can perform much better than under the traditional paradigm.     

基于自适应伸缩因子的变论域模糊PID振动控制方法

针对传统变论域模糊PID控制算法(VUFP)伸缩因子无法自适应调整的问题,建立了基于自适应伸缩因子的变论域模糊PID振动控制方法,从而进一步提升振动控制效果。在传统VUFP算法的基础上,将系统误差及误差变化率作为输入,伸缩因子参数作为输出,提出了伸缩因子自适应调整函数;从非零性、对偶性、单调性、正规性、协调性入手,通过理论证明了所提自适应调整函数的合理性;基于VUFP算法,利用系统误差及误差变化率实现了自适应调整函数参数的实时自适应调整,从而避免了VUFP算法中伸缩因子缺乏模糊规则导致控制效果降低的问题。3层框架理论模型和实验结构的振动控制结果表明:所提控制方法能实现函数型伸缩因子参数的自适应调整,针对框架结构加速度、速度及位移的控制效果均优于PID和VUFP控制算法,为建筑结构振动控制提供了一种新的控制算法。     

A novel branch-and-bound algorithm for the chance-constrained resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) has been widely studied during the last few decades. In real-world projects, however, not all information is known in advance and uncertainty is an inevitable part of these projects. The chance-constrained resource-constrained project scheduling problem (CC-RCPSP) has been recently introduced to deal with uncertainty in the RCPSP. In this paper, we propose a branch-and-bound (B&B) algorithm and a mixed integer linear programming (MILP) formulation that solve a sample average approximation of the CC-RCPSP. We introduce two different branching schemes and eight different priority rules for the proposed B&B algorithm. The computational results suggest that the proposed B&B procedure clearly outperforms both a proposed MILP formulation and a branch-and-cut algorithm from the literature.     

An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two different discount factors are adopted to revise the original evidence sets, based on the result of DBSCAN density clustering. Finally, we conduct the information fusion for the revised evidence sets by D-S combination rules. Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence, with better accuracy, stability and convergence speed.     

Dynamic scheduling of production-assembly networks in a distributed environment

In complex manufacturing environments, meeting the due dates of the jobs and minimizing in process inventories are important performance metrics. One of the common characteristics of complex production systems is production-assembly network of operations. This paper presents an auction-based algorithm for simultaneous scheduling of all manufactured and assembled jobs in a dynamic environment, where the objective function is to minimize both the due date penalties associated with the final products and the inventory cost of the work in process. An auction-based approach using a Mixed-Integer Linear Programming (MILP) model to construct and evaluate the bids so that the auction mechanism mimics a Lagrangian relaxation-based subgradient optimization to ensure global optimality is proposed. The inner structure of the problem enables very efficient calculation of bids for each job or assembly. Using a full factorial experimental design the properties of the proposed algorithm are analyzed. Results show that the proposed auction based algorithm performs better than the popular dispatching rules and is more scalable than the MILP model or direct implementations of the subgradient algorithm. Furthermore, the proposed algorithm is designed to work in a dynamic environment.     

基于测深侧扫声呐的DOA估计算法研究

针对测深侧扫声呐进行波达方向(Direction of Arrival, DOA)估计时会受到阵元幅度、相位误差及低信噪比影响的问题,提出一种改进的波束域加权子空间拟合算法。首先,采用总体最小二乘-旋转不变子空间算法进行回波方向预估计;其次,将连续线阵划分为多个子阵,并将各个子阵在预估计方向做加权波束形成;再次,采用加权子空间拟合(Weighted Subspace Fitting, WSF)算法构造代价函数;最后,采用阻尼牛顿法求解得到高精度的DOA估计结果。仿真结果表明,文中所提算法在阵元出现幅度相位误差条件下的角度估计均方误差相对于WSF算法减少了约0.03°。海试数据分析结果表明,文中所提算法的测深点均方误差整体优于WSF算法,其相对测深精度提高了约9.8个百分点。以上分析结果表明,文中所提算法整体优于WSF算法,可以实现在阵元幅度相位误差及低信噪比情况下的高精度DOA估计。     

The design of composite adaptive morphological filter and applications to Rician noise reduction in MR images

Composite filters based on morphological operators are getting considerably attractive to medical image denoising. Most of such composite filters depend on classical morphological operators. In this article, an optimal composite adaptive morphological filter (F_CAMF) is developed through a genetic programming (GP) training algorithm by using new nonlocal amoeba morphological operators. On one hand, we propose a novel method for formulating and implementing nonlocal amoeba structuring elements (SEs) for input‐adaptive morphological operators. The nonlocal amoeba SEs in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. On the other hand, GP as a supervised learning algorithm is employed for building the F_CAMF. In GP module, F_CAMF is evolved through evaluating the fitness of several individuals over certain number of generations. The proposed method does not need any prior information about the Rician noise variance. Experimental results on both standard simulated and real MRI data sets show that the proposed filter produces excellent results and outperforms existing state‐of‐the‐art filters, especially for highly noisy image. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 15–23, 2015