Multi-objective optimization of high-speed milling with parallel genetic simulated annealing

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.     

Constrained grinding optimization for time,cost, and surface roughness using NSGA-II

Selection of parameters in machining process significantly affects quality, productivity, and cost of a component. This paper presents an optimization procedure to determine the optimal values of wheel speed, workpiece speed, and depth of cut in a grinding process considering certain grinding conditions. Experimental studies have been carried out to obtain optimum conditions. Mathematical models have also been developed for estimating the surface roughness based on experimental investigations. A non-dominated sorting genetic algorithm (NSGA II) is then used to solve this multi-objective optimization problem. The objectives under investigation in this study are surface finish, total grinding time, and production cost subjected to the constraints of production rate and wheel wear parameters. The Pareto-optimal fronts provide a wide range of trade-off operating conditions which an appropriate operating point can be selected by a decision maker. The results show the proposed algorithm demonstrates applicability of machining optimization considering conflicting objectives.     

Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method

The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem. The VCMs enabled both the knowledge-based simplification of the optimization model and the variable-precision flow analyses of different injection molding parameter schemes. The Moldflow analyses were applied to collect the precise sample data for developing BPNNs and to fine-tune the Pareto-optimal solutions after the CNSGA-based optimization while the approximate BPNNs were utilized to efficiently compute the fitness of every individual during the evolution of CNSGA. The case study of optimizing the mold and process parameters for manufacturing mice with a compound-cavity mold demonstrated the feasibility and intelligence of proposed methodology.     

基于多目标混合殖民竞争算法的设备维护与车间调度集成优化

在制定调度计划时考虑设备预防性维护可以提高设备利用率和资产效率。首先,依据实际制造车间生产环境,在每台机器的可靠度降低到阈值的时候安排预防性维护,建立柔性作业车间设备预防性维护与调度集成优化的数学模型,以最小化最大完工时间、总生产成本和平均总维修成本为目标。然后,提出一种多目标混合殖民竞争算法求解该模型,设计相应的编码、解码、殖民国家同化过程以及多目标混合殖民竞争算法的流程,并采用改进加权TOPSIS方法在获得的Pareto解集中选择满意解,以达到提高设备的可靠性、按期交货和节省成本的目的。最后通过具体实例验证提出策略的可行性和有效性。     

Storage location assignment and order picking optimization in the automotive industry

The objective of this study is to design storage assignment and order picking system using a developed mathematical model and stochastic evolutionary optimization approach in the automotive industry. It is performed in two stages. At the first stage, storage location assignment problem is solved with a class-based storage policy with the aim of minimizing warehouse transmissions by using integer programming. At the second stage, batching and routing problems are considered together to minimize travel cost in warehouse operations. A warehouse in the automotive industry is analyzed, and an optimum solution is obtained from an integer programming model. Due to the computational time required for solving the integer programming problem, a faster genetic algorithm is also developed to form optimal batches and optimal routes for the order picker. The main advantage of the algorithm is the quick response to production orders in real-time applications. The solutions showed that the proposed approach based on genetic algorithms can be applied and integrated to any kind of warehouse layout in automotive industry.     

Multi-objective optimization and evaluation method of modular product configuration design scheme

Product configuration is one of the key technologies for mass customization. Traditional product configuration optimization targets are mostly single. In this paper, an approach based on multi-objective genetic optimization algorithm and fuzzy-based select mechanism is proposed to solve the multi-objective configuration optimization problem. Firstly, the multi-objective optimization mathematical model of product configuration is constructed, the objective functions are performance, cost, and time. Then, a method based on improved non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the configuration design optimization problem. As a result, the Pareto-optimal set is acquired by NSGA-II. Due to the imprecise nature of human decision, a fuzzy-based configuration scheme evaluation and select mechanism is proposed consequently, which helps extract the best compromise solution from the Pareto-optimal set. The proposed multi-objective genetic algorithm is compared with two other established multi-objective optimization algorithms, and the results reveal that the proposed genetic algorithm outperforms the others in terms of product configuration optimization problem. At last, an example of air compressor multi-objective configuration optimization is used to demonstrate the feasibility and validity of the proposed method.     

Optimization and graphical representation of machining conditions in multi-pass turning operations

This paper describes a procedure to calculate the machining conditions, such as the cutting speed, feed rate and depth of cut for turning operations with minimum production cost or the maximum production rate as the objective function. The optimum number of machining passes and the depth of cut for each pass is obtained through the dynamic programming technique and optimum values of machining conditions for each pass are determined based on the objective function criteria by search method application to the feasible region. Production cost and production time values are determined for different workpiece and tool material for the same input data. In the optimization procedure, the objective functions are subject to constraints of maximum and minimum feed rates and speeds available, cutting power, tool life, deflection of work piece, axial pre-load and surface roughness. By graphical representation of the objective function and the constraints in the developed software, the effects of constraints on the objective function can be evaluated. The parameters that are assumed to be most effective in determining the optimum point can easily be changed and the revised graph can be inspected for possible improvements in the optimum value.     

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.     

A hybrid method of modified NSGA-II and TOPSIS for lightweight design of parameterized passenger car sub-frame

This paper presents a hybrid method integrating modified NSGA-II and TOPSIS, used for lightweight design of the front sub-frame of a passenger car. Firstly, the FE model of the sub-frame is constructed and is validated by modal test. Then, the strength performance of the sub-frame is analyzed under four typical load conditions consisting of braking, acceleration, steady state cornering and vertical bump. After that, a parameterized model of the sub-frame, in which 12 geometric parameters are defined as design variables, is developed based on the mesh morphing technology. Subsequently, modified NSGA-II is employed for multi-objective optimization of the sub-frame considering weight, maximum von-Mises stress and first order natural frequency as three conflicting objective functions. Accordingly, a set of Pareto-optimal solutions are obtained from the optimization process. Finally, the entropy weight theory and TOPSIS method are adopted to rank all these solutions from the best to the worst for determining the best compromise solution. In addition, the effectiveness of the proposed hybrid lightweight design method is demonstrated by the comparisons among baseline design and optimum solutions.     

基于Pareto最优的多企业协同计划调度优化

为解决协同制造环境下多协作企业的协同计划调度问题,针对多企业协同生产链实际运作过程,建立了一种考虑综合成本和完工时间的多目标计划调度优化模型。基于Pareto最优概念,采用NSGA-Ⅱ算法（快速非支配排序遗传算法）来解决多目标优化问题。为了保证解的收敛性和多样性,设计了有效的编解码方式和遗传操作程序,通过局部变异种群重复个体,并采用分布函数自适应选取精英数量,得到一系列Pareto最优解。最后通过仿真实例对多目标优化模型和算法进行了求解,结果表明,该方法可快速有效地实现全局多目标寻优,从而找到更多更合理的协同计划调度方案。
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Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm

In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.     

Multi-objective optimization and sensitivity analysis of tube hydroforming

Tube hydroforming is a manufacturing process used to produce structural components in cars and trucks, and the success of this process largely depends on the careful control of parameters such as internal pressure and end-feed force. The objective of this work was to establish a methodology, and demonstrate its effectiveness, to determine the optimal process parameters for a tube hydroformed in a die with a square cross section. The Taguchi method was used to establish a design of virtual hydroforming experiments, and numerical simulations were carried out with the finite element code LS-DYNA®. A sensitivity analysis was also carried out with analysis of variance. Multi-objective functions that consider necking/fracture, wrinkling, and thinning were formulated, and the response surface methodology was used with the most sensitive factors to obtain a defect-free part. An objective function, based on the final corner radius in the part, was also included in the optimization model. The forming severity of virtual hydroformed parts was evaluated using the forming limit stress diagram and the forming limit (strain) diagram. Finally, the normal-boundary intersection method and the L ₂ norm were used to obtain the Pareto-optimal solution set and the optimal solution within this set, respectively. The hydroforming process for this part was also optimized using the commercial optimization software LS-OPT®, with two different single-objective algorithms. However, the optimum load path predicted with the proposed methodology was shown to achieve a smaller corner radius. The proposed optimization technique helped to define a process window that leads to a robust manufacturing process and improved part quality.     

基于支持向量机的多目标模糊稳健优化设计

为了提高模糊稳健优化设计的计算效率,探讨了基于支持向量机回归机(SVR)的多目标模糊稳健设计方法,该方法以SVR作为非线性约束函数的替代模型,并采用SVR对模糊概率进行仿真计算,可显著降低模糊稳健优化设计的机时消耗;采用字典序优先级的目标规划法,建立了多目标稳健优化设计模型;把SVR与遗传算法相结合,构建了一种混合智能优化算法;通过多目标稳健设计实例,对所提出的方法进行了验证。     

Pareto lexicographic α-robust approach and its application in robust multi objective assembly line balancing problem

Robustness in most of the literature is associated with min-max or min-max regret criteria. However, these criteria of robustness are conservative and therefore recently new criteria called, lexicographic α-robust method has been introduced in literature which defines the robust solution as a set of solutions whose quality or jth largest cost is not worse than the best possible jth largest cost in all scenarios. These criteria might be significant for robust optimization of single objective optimization problems. However, in real optimization problems, two or more than two conflicting objectives are desired to optimize concurrently and solution of multi objective optimization problems exists in the form of a set of solutions called Pareto solutions and from these solutions it might be difficult to decide which Pareto solution can satisfy min-max, min-max regret or lexicographic α-robust criteria by considering multiple objectives simultaneously. Therefore, lexicographic α-robust method which is a recently introduced method in literature is extended in the current research for Pareto solutions. The proposed method called Pareto lexicographic α-robust approach can define Pareto lexicographic α-robust solutions from different scenarios by considering multiple objectives simultaneously. A simple example and an application of the proposed method on a simple problem of multi objective optimization of simple assembly line balancing problem with task time uncertainty is presented to get their robust solutions. The presented method can be significant to implement on different multi objective robust optimization problems containing uncertainty.     

求解资源受限多项目调度的改进遗传规划算法

针对优先级规则调度不具备优化能力的缺陷，提出了一种应用于资源受限多项目调度的改进超启发式遗传规划算法以进化出更理想的优先级规则。通过分析现有优先级规则构建出适用多项目调度的归一化属性集和顶层判别编码方式，并结合NSGA-Ⅱ虚拟适应度分配方法对种群进行评估以实现多目标优化。设计了一种多样性种群更新方式，以避免传统遗传规划易陷入局部最优的缺陷和提高搜索能力。通过基于标准数据集PSPLIB所构造的算例和飞机总装装配线的生产实例验证了该方法的有效性和可行性。     

Concurrent multi-objective tolerance allocation of mechanical assemblies considering alternative manufacturing process selection

Concurrent design of tolerances by considering both the manufacturing cost and quality loss of each component by alternate processes of the assemblies may ensure the manufacturability, reduce the manufacturing costs, decrease the number of fraction nonconforming (or defective rate), and shorten the production lead time. Most of the current tolerance design research does not consider the quality loss. In this paper, a novel multi-objective optimization method is proposed to enhance the operations of the non-traditional algorithms (Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) and systematically distribute the tolerances among various the components of mechanical assemblies. The problem has a multi-criterion character in which three objective functions, one constraint, and three variables are considered. The average fitness factor method and normalized weighted objective function method are used to select the best optimal solution from Pareto-optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto-optimal fronts. Two more multi-objective performance measures namely optimizer overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto-optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MOPSO algorithms are best for this problem.     

Optimum design of short journal bearings by enhanced artificial life optimization algorithm

This paper presents an optimum design of high-speed short journal bearing using an enhanced artificial life algorithm (EALA) to compute the solutions of optimization problem. The proposed hybrid EALA algorithm is a synthesis of an artificial life algorithm (ALA) and the random tabu search method (R-tabu method) to solve some demerits of the ALA. The emergence is the most important feature of the artificial life which is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The artificial life optimization algorithm is a stochastic searching algorithm using the feature of artificial life. The feature of R-tabu method, which prevents converging to the local minimum, is combined with the ALA. One of the features of the R-tabu method is to divide any given searching region into several sub-steps. As the result of the combination of the two methods, the EALA not only converges faster than the ALA, but also can lead to a more accurate solution. In addition, this algorithm can also find all global optimum solutions. We applied the hybrid algorithm to the optimum design of a short journal bearing. The optimized results were compared with those of ALA and successive quadratic programming, and identified the reliability and usefulness of the hybrid algorithm.     

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.     

Integrated aero-structural optimization design of pre-bend wind turbine blades

In the optimization design of a pre-bend wind turbine blade, there is a coupling relationship between blade aerodynamic shape and structural layup. The evaluation index of a wind turbine blade not only shows on conventional ones, such as Annual energy production (AEP), cost, and quality, but also includes the size of the loads on the hub or tower. Hence, the design of pre-bend wind turbine blades is a true multi-objective engineering task. To make the integrative optimization design of the pre-bend blade, new methods for the blade’s pre-bend profile design and structural analysis for the blade sections were presented, under dangerous working conditions, and considering the fundamental control characteristics of the wind turbine, an integrated aerodynamic-structural design technique for pre-bend blades was developed based on the Multi-objective particle swarm optimization algorithm (MOPSO). By using the optimization method, a three-dimensional Pareto-optimal set, which can satisfy different matching requirements from overall design of a wind turbine, was obtained. The most suitable solution was chosen from the Pareto-optimal set and compared with the original 1.5 MW blade. The results show that the optimized blade have better performance in every aspect, which verifies the feasibility of this new method for the design of pre-bend wind turbine blades.

     

多加工路径柔性作业调度方法

为了提高企业生产效率,降低生产成本,以仪器仪表制造业为背景,针对某企业流量计生产车间的特点,建立优化目标函数,提出了多加工路径柔性作业车间调度模型(MPFJSP)。为了避免该模型中非可行解的干扰,采用新的顺序编码方式和在非可行解周围搜索可行解并代替之的方法;且为提高收敛速度,将粒子分为若干小段分别更新和扰动,以保留优秀基因,由此形成分段多目标粒子群优化算法(PMOPSO)。通过算法对比,发现所提方法能在工件的每一条加工路径中寻优,验证了其性能更优越,也验证了MPFJSP相对于传统柔性作业车间调度(FJSP)更灵活,得到的解也更优,在流量计生产车间的实验性生产中,违约成本约下降了12%,生产效率提升约10%。