Optimization of multi-pass turning using particle swarm intelligence

This paper proposes a methodology for selecting optimum machining parameters in multi-pass turning using particle swarm intelligence. Often, multi-pass turning operations are designed to satisfy several practical cutting constraints in order to achieve the overall objective, such as production cost or machining time. Compared with the standard handbook approach, computer-aided optimization procedures provide rapid and accurate solutions in selecting the cutting parameters. In this paper, a non-conventional optimization technique known as particle swarm optimization (PSO) is implemented to obtain the set of cutting parameters that minimize unit production cost subject to practical constraints. The dynamic objective function approach adopted in the paper resolves a complex, multi-constrained, nonlinear turning model into a single, unconstrained objective problem. The best solution in each generation is obtained by comparing the unit production cost and the total non-dimensional constraint violation among all of the particles. The methodology is illustrated with examples of bar turning and a component of continuous form.     

Dynamic cell formation and the worker assignment problem: a new model

In this paper a new model is developed to deal with a simultaneous dynamic cell formation and worker assignment problem (SDCWP). Part routing flexibility and machine flexibility and also promotion of workers from one skill level to another are considered. The proposed model is formulated as a single objective nonlinear integer programming which is converted to a linear one. The objective function consists of two separate components. The first part of the objective function is related to machine-based costs such as production cost, intercell material handling cost, machine costs in the planning horizon. The second part is related to human issues and consists of hiring cost, firing cost, training cost and salary. It is the first time that worker assignment and dynamic cell formation are considered simultaneously. To verify the performance of the proposed model, some numerical examples are presented. Computational and sensitivity analysis results imply the significance of SDCWP.     

Project time–cost trade-off scheduling: a hybrid optimization approach

This paper develops a hybrid approach for stochastic time–cost trade-off problem (STCTP) in PERT networks of project management, where activities are subjected to linear cost functions. The main objective of proposed approach is to improve the project completion probability in a prespecified deadline from a risky value to a confident predefined probability. To this end, we construct a nonlinear mathematical program with decision variables of activity mean durations, in which the objective function is concerned with minimization of project crashing direct cost. In order to solve the constructed model, we present a hybrid approach based on cutting plane method and Monte Carlo (MC) simulation. To illustrate the process of proposed approach, the approach was coded using MATLAB 7.6.0 and two illustrative examples are discussed. The results obtained from the computational study show that the proposed algorithm is an effective approach for the STCTP.     

Manufacturing environment-oriented robust tolerance optimization method

Tolerance design has a great impact on the cost and quality of a product. Previous research focused on process tolerances or robust tolerance design with little consideration on real manufacturing context. This paper presents a nonlinear method for robust tolerance design based on the real manufacturing context in three stages. The objective function to be minimized is the total manufacturing cost. The constraint equations for the optimization model are also deduced, which select suitable manufacturing processes based on the manufacturing environment. Simulation annealing (SA) is used for the nonlinear optimization. The approach is finally illustrated by a practical example. The results of the comparison with different models indicate that the proposed approach is more effective with the manufacturing resource. The robust and reliable tolerance can be obtained.     

一种求解集成生产计划的混合协同进化算法

讨论了一类带有序列相关的机器调整时间和有限缓冲空间的流水车间批量计划与调度的集成优化问题,给出了该问题的非线性混合整数规划模型,提出了一种求解混合协同进化问题的算法.模型的目标函数是使库存费用、缺货费用和加班费用之和最小,约束函数考虑了库存平衡约束和需求平衡约束.算法采用协同进化算法与遗传算法的并行混合搜索结构,通过迁移算子把协同进化的子种群和独立进化的公共种群有机联系起来,同时算法采用基于邻域的进化策略,以提高算法性能.最后,对三种不同规模的问题进行了数值仿真实验,结果验证了算法的有效性.     

Dynamic optimization of spur gears

This paper presents a global optimization method focused on gear vibration reduction by means of profile modifications. A nonlinear dynamic model is used to study the vibration behavior; such model is validated using data available in literature. The optimization method considers different regimes and torque levels; the objective function can be the static transmission error or the maximum amplitude of the gear vibration in terms of dynamic transmission error. The procedure finds the optimal profile modification that reduces the vibrations over a wide range of operating conditions. In order to reduce the computational cost, a Random-Simplex optimization algorithm is developed; the optimum reliability is estimated using a Monte Carlo simulation. The approach shows good performances for the computational efficiency as well as the reliability of results. Finally, an application to High Contact Ratio (HCR) gears is presented and an extremely good performance is obtained by combining optimization procedures and HCR properties.     

基于工期约束的动态联盟伙伴选择

具有项目投标价格和完工时间因素的带工期约束的伙伴选择问题是企业动态联盟的基本问题，证明了该问题是NP完全问题。为设计求解该问题的分支定界算法，建立了非线性整数规划模型。证明了模型中目标函数和约束函数的单调性，并利用单调性给出了判断问题无解和问题最优解已知的条件，构造了收缩求解区域的二分法。实验表明，基于这些结果所构造的分支定界算法是有效的。     

Damage identification in skeletal structures using the virtual distortion method in frequency domain

The paper is a continuation of research on the application of a structural reanalysis method—the virtual distortion method—to structural health monitoring. The first approach, formulated previously in the time domain, suffered from a considerable numerical cost. In order to reduce it, this paper presents an alternative approach, formulated in the frequency domain thanks to the assumption of using harmonic excitation (quasi-static problem). The hardware devices supposed to collect structural responses are piezoelectric patch sensors. The software tool for damage identification solves a nonlinear least squares optimization problem by employing analytically derived sensitivities. Strains are the analyzed quantities, contributing to the objective function. Considerations are restricted to skeletal structures and a simplified dynamic problem with no damping. Effectiveness of the frequency-domain identification is demonstrated in numerical examples. Experimental verification is envisaged.     

基于BP神经网络的连续状态并串联系统冗余度优化设计

提出一种以费用为约束条件的连续状态并串联系统冗余度优化设计模型.用传统的优化算法很难求得该模型目标函数的显性表达式,文中提出用BP(back propagation)神经网络对目标函数进行逼近,得到的近似目标函数是标准sigmoid函数的线性组合.仿真结果表明,该方法可以精确地求得系统的最优冗余度.     

Tolerance synthesis adopting a nonlinear programming approach

One of the main objectives in tolerance charting is to determine the working dimensions and tolerances at the lowest cost without violating the blueprint specifications. In view of this, this paper presents a simple dimensional chains identification method. After establishing all necessary equations and constraints, a nonlinear objective function is formulated. Subsequently, all these relationships are submitted to an optimisation software, OPTIVAR written in FORTRAN for the determination of the unknown variables. In addition, an example is incorporated to demonstrate the effectiveness of this proposed methodology.