测量散射参数的交互换位双信号方法及其应用

叙述了外内向功率波复数比的测量方法,给出了散射参数测量的交互换位双信号测量线方法。用此方法测量了双栅ＧａＡｓＭＥＳＦＥＴ适用散射参数,并用它设计Ｓ波段混合集成双栅ＧａＡｓＭＥＳＦＥＴ可控增益放大器。测试结果与设计吻合。     

机械图参数化绘图通用AutoLISP函数设计与应用

介绍在AutoCAD环境下,用AutoLISP语言开发机械图参数化绘图通用函数方法．提出了尺寸公差表输入方式的自动判别模式．函数的建立,可提高编程效率,缩短软件开发周期。程序已在AutoCAD10.0版上运行通过,并应用于换热器CAD项目中,取得了较好的经济效益．     

Integrated mathematical optimisation approach for the tower crane hook routing problem to satisfy material demand requests on-site

Optimising decisions around the location and operation of tower cranes can improve the workflow in construction projects. Traditionally, the location and allocation problems involved in tower crane operations in the literature have been solved separately from the assignment of material supply points to demand points and the scheduling of the crane’s activity sequence across supply and demand points on a construction site. To address the gap, this paper proposes a binary integer programming problem, where location of the tower crane, allocation of supply points to material-demanding regions, and routing of hook of the crane based on activity sequencing of the hook across supply and material-demanding regions on site are optimised. The novelty in this work is in the way the crane’s activity scheduling is modelled via mathematical programming, based on routing the hook movement to meet material demand, through minimising tower crane operating costs. A realistic case study is solved to assess the validity of the model. The model is contrasted with results obtained from other solving algorithms commonly adopted in the literature, along with a solution proposed by an experienced practitioner. Results indicate that all instances can be solved when compared to other meta-heuristics that fail to achieve an optimum solution. Compared to the solution proposed by the practitioner, the results of the proposed model achieve a 46% improvement in objective function value. Planners should optimise decisions related to the location of the crane, the crane’s hook movement to meet service requests, and supply points’ locations and assignment to material-demanding regions simultaneously for effective crane operations.     

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.     

A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.     

Using dynamic demand information and zoning for the storage of non-uniform density stock keeping units

The warehouse order-picking operation is one of the most labour-intense activities that has an important impact on responsiveness and efficiency of the supply chain. An understanding of the impact of the simultaneous effects of customer demand patterns and order clustering, considering physical restrictions in product storage, is critical for improving operational performance. Storage restrictions may include storing non-uniform density stock keeping units (SKUs) whose dimensions and weight constrain the order-picking operation given that a priority must be followed. In this paper, a heuristic optimisation based on a quadratic integer programming is employed to generate a layout solution that considers customer demand patterns and order clustering. A simulation model is used to investigate the effects of creating and implementing these layout solutions in conjunction with density zones to account for restrictions in non-uniform density SKUs. Results from combining layout optimisation heuristics and density zoning indicate statistical significant differences between assignments that ignore the aforementioned factors and those that recognise it.     

One-side value-added service investment and pricing strategies for a two-sided platform

To widen user participation and increase profit, two-sided platforms may invest on value-added services (VASs) for users. Due to the cross-market network externality, the investment for one side would affect the utility of users on two sides, thus affect the demand and profit of the platform. In this paper, we investigate one-side VAS investment and pricing strategies for a two-sided platform. It is revealed that it is optimal to invest at the maximum level for any marginal investing cost below a certain threshold, and to decrease the investment when the marginal investing cost increases above the threshold. We find that compared with the case of no investment, the invested user side will always be priced higher, while the uninvested user side may either be priced higher or lower, depending on the relative magnitude of mutual cross-market network externalities. If both sides are priced higher after the investment, the price increment for uninvested user side could be larger than that for invested user side.     

基于ACO和SPN的FMS故障诊断和可用性评价方法

提出一种柔性制造系统(FMS)的故障诊断和可用性评价方法;针对传统的随机Petri网在解决FMS故障诊断上极大受限于底层马尔科夫链规模而容易产生状态爆炸的问题,首先,将蚁群优化算法(ACO)融入随机有色网(SPN)中,提出并定义了一种能对柔性制造系统的故障进行诊断的诊断器;然后,通过马尔科夫链计算制造单元的可用性,得到FMS到诊断器的映射,从而可以得到FMS中所有可能生产过程;最后,在经典FMS可用性评价方法的基础上,引入覆盖因子,提出了一种新的对FMS生产过程进行可用性评价的方法;仿真实验显示了覆盖因子对系统可用性的影响,通过与传统方法进行比较,表明覆盖因子越大,FMS的可用性越高。     

具有约束多目标优化的进化算法

针对带有约束多目标优化问题,提出一种多目标优化进化算法。在选择过程中,采用约束的Pareto支配和聚集距离定义适应值,根据适应值挑选出有代表性的个体。在变异过程中,沿着权重梯度方向搜索来寻找可行的Pareto最优解。最后,采用两个数值算例测草算法的性能,结果表明该算法能获得多目标约束优化问题的可行Pareto最优解并且具有较好的分散性。     

Markerless human articulated tracking using hierarchical particle swarm optimisation

In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.