Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.

Exponential distribution-based genetic algorithm for solving mixed-integer bilevel programming problems

Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower＇s functions are separable with respect to the follower＇s variables, and the other is that the follower＇s functions are convex if the follower＇s variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader＇s variable x, it is proved that the optimal solution y of the follower＇s mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower＇s programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter λ, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.

Genetic Algorithm for Concurrent Balancing of Mixed-Model Assembly Lines with Original Task Times of Models

The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

Niche pseudo-parallel genetic algorithms for path optimization of autonomous mobile robot

A new genetic algorithm named niche pseudo-parallel genetic algorithm （NPPGA） is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem （TSP）. Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named ＂Robot tour around Pekin＂, and the performance conducted by NPPGA is better than simple genetic algorithm （SGA） and distributed paralell genetic algorithms （DPGA）.

Study on Multi-stream Heat Exchanger Network Synthesis with Parallel Genetic/Simulated Annealing Algorithm

The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.

Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm

While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).

Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.

Multi-Objective Genetic Algorithm to Design Manufacturing Process Line Including Feasible and Infeasible Solutions in Neighborhood

This paper treats multi-objective problem for manufacturing process design. A purpose of the process design is to decide combinations of work elements assigned to different work centers. Multiple work elements are ordinarily assigned to each center. Here, infeasible solutions are easily generated by precedence relationship of work elements in process design. The number of infeasible solutions generated is ordinarily larger than that of feasible solutions generated in the process. Therefore, feasible and infeasible solutions are located in any neighborhood in solution space. It is difficult to seek high quality Pareto solutions in this problem by using conventional multi-objective evolutional algorithms. We consider that the problem includes difficulty to seek high quality solutions by the following characteristics： （1） Since infeasible solutions are resemble to good feasible solutions, many infeasible solutions which have good values of objective functions are easily sought in the search process, （2） Infeasible solutions are useful to select new variable conditions generating good feasible solutions in search process. In this study, a multi-objective genetic algorithm including local search is proposed using these characteristics. Maximum value of average operation times and maximum value of dispersion of operation time in all work centers are used as objective functions to promote productivity. The optimal weighted coefficient is introduced to control the ratio of feasible solutions to all solutions selected in crossover and selection process in the algorithm. This paper shows the effectiveness of the proposed algorithm on simple model.

Optimization of a Lobed Mixer with BP Neural Network and Genetic Algorithm

A Sequential Approximate Optimization framework(SAO)for the multi-objective optimization of lobed mixer is established by using the BP neural network and Genetic Algorithm:the ratio of lobe wavelength to height(η)and the rise angle(α)are selected as the design parameters,and the mixing efficiency,thrust and total pressure loss are the optimization objectives.The CFX commercial solver coupled with the SST turbulence model is employed to simulate the flow field of lobed mixer.A tetrahedral unstructured grid with 5.6 million cells can achieve the similar global results.Based on the response surface approximation model of the lobed mixer,it is necessary to avoid increasing or decreasingαandηat the same time.Instead,theαshould be reduced while theηis appropriately increased,which is conducive to achieving the goal of increasing thrust and reducing losses at the expense of a small decrease in the mixing efficiency.Compared with the normalized method,the non-normalized method with better global optimization accuracy is more suitable for solving the multi-objective optimization problem of the lobed mixer,and its optimal solution(α=8.54°,η=1.165)is the optimal solution of the lobed mixer optimization problem studied in this paper.Compared with the reference lobed mixer,theα,β(the fall angle)and H(lobe height)of the optimal solution are reduced by 0.14°,1.34°and 3.97 mm,respectively,and theηis increased by 0.074;its mixing efficiency is decreased by 4.46%,but the thrust is increased by 2.29%and the total pressure loss is decreased by 0.64%.Downstream of the optimized lobed mixer,the radial scale and peak vorticity of the streamwise voritices decrease with the decreasing lobe height,thereby reducing the mixing efficiency.For the optimized lobed mixer,its low mixing efficiency is the main factor for the decrease of the total pressure loss,but the improvement of the geometric curvature is also conducive to reducing its profile loss.Within the scope of this study,the lobed mixer has an optimal mixing efficiency(ε=74.14%)that maximizes its thrust without excessively increasing the mixing loss.

Heuristics for Mixed Model Assembly Line Balancing Problem with Sequencing

The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.

面向钣金混流生产线的仿真优化研究

文章以某电梯钣金加工公司的钣金混流生产线为背景,通过虚拟仿真方法对钣金生产环节中的投产序列和瓶颈问题进行优化。首先,根据某电梯门板生产线的实际生产状况,建立生产线的仿真模型,以工件完工时间、设备空闲时间和设备总切换时间为目标,基于遗传算法,优化钣金混流生产线的投产序列;其次,通过分析缓存区添加位置和容量对钣金生产线产量的影响,解决生产线堵塞、利用率不平衡等问题。结果表明,对生产线的优化有效,经优化后的投产序列相比原始投产序列整体时间缩短约15%,通过适宜的缓存区设置,各工位设备平均利用率提高了9.6%,产量提高了14.15%。

Genetic inverse algorithm for retrieval of component temperature of mixed pixel by multi-angle thermal infrared remote sensing data

After carefully studying the results of retrieval of land surface temperature(LST) by multi-channel thermal infrared remote sensing data, the authors of this paper point out that its accuracy and significance for applications are seriously damaged by the high correlation coefficient among multi-channel information and its disablement of direct retrieval of component temperature. Based on the model of directional radiation of non-isothermal mixed pixel, the authors point out that multi-angle thermal infrared remote sensing can offer the possibility to directly retrieve component temperature, but it is also a multi-parameter synchronous inverse problem. The results of digital simulation and field experiments show that the genetic inverse algorithm (GIA) is an effective method to fulfill multi-parameter synchronous retrieval. So it is possible to realize retrieval of component temperature with error less than 1K by multi-angle thermal infrared remote sensing data and GIA.

Mixed H_2/H_∞ road feel control of EPS based on genetic algorithm

In view of the existence of uncertainties such as system model and disturbance signal in the electric power steering （EPS） system, and the demand for system dynamic performance, the mixed H2/H∞, controller based on genetic algorithm is proposed. In order to obtain satisfactory steering feel, robust performance and steering stability, models of EPS system and a two-degree- of-freedom car are set up, then the state space model and the augmented matrixes are built. The H∞, method is introduced to minimize the effect of disturbances on the outputs, and the H2 method is applied to optimizing the system performance based on genetic algorithm. The simulation results show that the modified mixed H2/H∞ controller, which synthesizes the advantage of H2 control and Ha, control, has better robust performance and robust stability. The designed controller can attenuate the noises and disturbances caused by road random motivation, torque sensor measurement and model parameter uncertainty, enabling the driver to obtain satisfactory road feel.

考虑场景差异性的混合车型公交调度优化方法

纯电动公交因其低碳和节能环保的特性,已成为车辆电动化转型的必然选择,但纯电动公交车在实际运营中仍面临低温条件下性能下降和电池老化导致续航里程降低等挑战。考虑在运营中混合使用燃油车和电动车,以弥补纯电动公交车在特定场景下的性能下降,提升公交运营效率和服务质量。本文考虑公交动态运行特征建立公交时刻表分段优化模型,以优化后的车次为输入,构建混合车型运营条件下的公交行车计划编制优化模型,并设计改进的遗传算法实现模型求解。最后,以北京市公交线路为例,选取单线路运营、异地充电及区域集中调度等不同典型运营场景开展案例研究,验证优化模型在差异化运营场景条件下的适用性和优化效果。结果表明,对比本地充电场景,异地充电场景下的运营成本增加5.15%,运营车辆数量增加5.88%;在多线路联合编制行车计划的区域集中调度场景下,运营成本较单线路运营场景降低4.68%;在给定的车型比例阈值下,使用混合车型运营效果优于使用单一车型运营,有效降低运营成本和碳排放。本文研究为公共交通企业结合不同运营场景,制定科学灵活的电动公交运营调度方案提供了重要支撑。

串联双级蒸发有机朗肯循环系统的多目标优化及工质优选

为了探究串联双级蒸发有机朗肯循环系统在特定工况下的最佳混合工质,选取了3组不同特性的混合工质,即R21/R113、R1234ze/R141b和R227ea/R245fa,引入热力学性能指标、经济指标和环境指标,利用非支配排序遗传算法进行多目标优化.通过求解Pareto边界,采用3种决策方法对每组工质流体进行最优解的选择,根据偏移量选取最优决策方法.结果表明:在热源温度为150℃、冷源温度为15℃的工况下,混合工质R1234ze/R141b最优解的[火用]效率相对较高,平准化度电成本(levelized cost of energy, LCOE)和当量二氧化碳排放量(equivalent carbon emission, ECE)相对也较低,热源温度也适配,为该工况下较为合适的混合工质.

部分充电策略下多中心混合车队联合配送路径优化

城市物流电动车与燃油车混合运输场景中,运输资源共享调度和充电策略联合优化方面存在不足。基于此,综合考虑客户时间窗、混合动力车队、电动车部分充电策略、多中心间联合配送机制和碳排放等实际因素,研究带时间窗和部分充电的多中心混合车队绿色车辆路径问题。以车辆固定成本、运输成本、充电成本、碳排放成本和时间惩罚成本之和最小化为目标构建优化模型,并设计混合改进遗传-变邻域搜索算法进行求解。基于湖南省某物流企业的实际数据进行仿真实验,验证了上述模型及算法的有效性,并从配送模式、车队配置和充电策略3个方面进行了敏感性分析。研究结果表明:1)联合配送模式有助于加强配送中心间的协同合作,促进运输资源共享调度,降低物流配送成本并减少碳排放,是一种经济环保的配送模式。2)电动车充电时间过长会影响客户时间满意度下降,且对纯电动车队而言,这一影响更为显著。3)混合车队相比纯电动车队具有更低的配送成本和更高的客户满意度,相比纯燃油车队在降低配送成本和减少碳排放方面更有优势。合理的车队配置不仅能减少企业运营成本,还可以同时兼顾客户利益和环境利益。4)在物流配送中采用部分充电策略能有效节省充电时间并提升客户服务体验。研究成果可为物流企业进行运输资源联合调度和配送方案优化决策提供参考依据。

R冷链企业混流生产线排产优化研究

以R冷链企业混流生产线为研究对象,以闲置时间与超载时间之和最小为目标建立排产优化模型。同时,考虑模型混流装配线投产的特性,采用遗传算法对模型求解。结果表明,混流生产线的投入产出比由75.5%提高至85.0%,日产量提高了11.26%,可以为冷链企业提高产能提供一定的参考。

资源约束的模块化服装生产工序编排优化模型与算法

为适应“多品种、小批量、短周期”服装生产现状,考虑服装生产线工作站带有资源设备数量约束的作业平衡问题(RCALB-VRW),以资源设备总数和平滑系数(SI)的极小化建立双目标优化数学模型。针对RCALB-VRW的特点,提出基于合并工作站策略的装箱遗传算法。首先设计工序分配列表与资源设备列表的双层实数编码方式;其次基于传统资源约束的生产线平衡问题的资源配置算法,对工作站与资源设备进行装箱操作,优化工序编排方案,在混合服装生产线的设备资源投入数量最小化的前提下,实现各工作站平稳作业;最后以两款相近衬衫为算例进行测试,并与另外3种资源约束模型比较,结果表明装箱遗传算法能够更高效地求解有资源设备数量约束的服装生产工序编排。所提方法可为服装智能制造与精益生产的推进提供理论指导。

基于Petri网和改进遗传算法的多资源调度问题

针对混流装配线工序加工资源需求多样、工艺复杂、装配工期长等问题,采用Petri网和改进遗传算法对该问题进行优化求解。建立混流装配线赋时库所Petri网(timed place Petri net, TPPN)调度模型,基于模型激发序列,采用基于工序的编码方式进行染色体编码;采用精英保留策略选择优异个体,改进遗传算法的交叉、变异操作,用改进后的遗传算法求解混流装配线调度问题。通过对比案例及实例数据计算结果验证了方案的有效性。

应用遗传算法估计非稳态地震数据的混合相位子波及Q值

地震高分辨率处理或反演的目的是获得精确的反射系数或弹性参数模型。然而地层滤波效应模糊了地震记录中的地层反射信息,因此有必要消除这种滤波效应。现有的大部分提高分辨率方法并不能完全摒弃关于地震子波和Q模型的一些假设。为了获得更为切合实际的地震子波,同时自适应获取Q模型,文中将地震子波相位估计与Q模型估计相结合,提出了基于遗传算法的非稳态地震数据混合相位子波及Q值估计方法。首先通过井旁道记录拟合得到初始地震子波的振幅信息,然后依据子波Z变换的根关于单位圆移动与否构建用于遗传算法的编码链条。另一方面,十进制Q模型对应的二进制表示形式同样能够利用编码链条表征,因此利用全局优化算法能够同时估计地震混合相位子波以及Q模型。结合根变换和遗传算法不断改变子波相位的同时自适应生成Q模型,利用子波和Q模型构造的时变子波矩阵与测井反射系数得到合成地震记录,并与井旁道记录进行匹配,最终得到合理的混合相位子波和地层Q模型,进而构造时变子波矩阵进行时变反褶积。通过井旁道记录与测井数据拟合得到的混合相位子波,与实际地震子波相位更为接近,理论数据和实际数据处理结果证实了该方法的有效性。