不同支配关系的多目标算法的柔性作业调度

为了提高多目标进化算法所获得解的质量,研究者做了大量的研究,传统的基于Pareto支配关系的多目标进化算法具有一定的局限性。本文利用不同的支配关系与NSGA-II(Non-dominated Sorting Genetic Algorithm)算法相结合,对单机器人搬运的柔性作业车间调度的多目标优化问题进行求解,通过实验比较分析了不同方法在多目标优化问题求解中的优劣性。本文以NSGA-II为框架结合Lorenz支配关系和CDAS(Control Dominance Area of Solutions)支配关系并与传统的基于Pareto支配关系的NSGA-II三种算法去研究同一优化调度问题,发现基于Lorenz支配关系和CDAS支配关系的优化算法比基于传统的Pareto支配关系的优化算法的效果更佳。     

差分进化算法求解带批处理机的机器人制造单元调度问题

机器人制造单元是智能制造系统的主要载体,研究机器人制造单元的生产调度问题对于提高智能制造系统的生产效率有着重要作用.对此,研究带批处理机的混合流水线机器人制造单元调度问题.首先,针对机器人制造单元与批处理机的生产特性,建立数学优化模型;其次,设计差分进化算法对其进行求解,提出染色体组编码的概念,求解该问题的染色体组由两个染色体构成,第1条染色体确定工件在每个工序选择的机器,第2条染色体确定加工顺序以及机器人的搬运顺序;然后,设计差分变异、交叉以及选择操作;最后,进行数值实验,结果证明,针对带批处理机的机器人制造单元调度问题,差分进化算法能缩短完工时间,得到更好的解.     

带机器人制造单元的作业车间调度仿真

针对带有机器人制造单元的作业车间调度优化问题, 在若干加工机器上可以加工具有特定加工工序的若干工件, 并且搬运机器人可以将工件在装卸载站与各加工机器间进行搬运. 在实际生产过程中, 由于不确定性, 特别是带有存货的加工单元, 要求工件的完工时间在一个时间窗内, 而不是一个特定的时间点. 因此针对此情况的作业车间, 考虑到其在求解问题过程中的复杂性和约束性等特点, 研究了在时间窗约束下, 目标值为最小化工件完成时间提前量和延迟量的总权重. 提出了一种将文化基因算法与邻域搜索技术(变邻域下降搜索)相结合的改进元启发式算法, 在求得最优目标值的同时, 可得到最优值的工件加工序列及机器人搬运序列. 通过实验结果表明, 所提出的算法有效且优于传统文化基因算法与遗传算法.     

一种基于参考点约束支配的NSGA-III算法

针对带约束的高维多目标优化问题,设计一种基于参考点的约束支配关系(RPCDP),将可行解与不可行解作为一个整体看待,进而综合考虑它们的收敛性、多样性和可行性,并基于此提出用于解决约束高维多目标优化问题的NSGA-III算法.将所提出算法与著名的3种约束高维多目标进化算法进行对比,实验结果表明在标准测试函数集CDTLZ上,相对于其他算法,所提出算法的解集具有更好的收敛性和分布性.     

基于模糊物元模型的高维多目标FJSP研究*

为解决高维多目标柔性作业车间调度问题,提出了一种基于模糊物元模型与粒子群算法的模糊粒子群算法（Fuzzy Particle Swarm Optimization,FPSO）。该算法以模糊物元分析理论为依据,采用复合模糊物元与基准模糊物元之间的欧式贴近度作为适应度值引导粒子群算法的进化,并引入具有容量限制的外部存储器保留较优的Pareto非支配解以供决策者选择。此外,构建了优化目标为最大完工时间、设备总负荷、加工成本、最大设备负荷与加工质量的高维多目标优化模型,并以Kacem基准问题与实际生产数据为例进行仿真模拟与对比分析。结果表明,该算法具有良好的收敛性且搜索到的非支配解分布性较好,能够有效地应用于求解高维多目标柔性作业车间调度问题。     

车间作业调度问题的仿真研究

研究车间作业调度优化问题,使资源、车辆调试、交通分配等达到优化配置,因此车间作业调度问题是一个多约束条件的目标优化问题,采用多项式求解方法不能获得最优解,导致车间作业调度效率低.为了提高车间作业调度效率,提出了一种蚁群算法的车间作业调度优化算法.首先以最小加工时间作为优化目标,蚂蚁爬行路径为作业调度方案,通过蚁群中个体间互相协作和信息交流获得最优车间作业调度方案.通过车间作业调度测试案例对算法进行验证性实验,实验结果表明,蚁群算法提高了车间作业调度效率,能在最短时间找到最优调度方案,为车间作业调度优化提供了依据.     

火箭壳体加工的虚拟单元重构方法

针对火箭壳体加工车间新订单连续到达,排产方案重构频繁导致重构时间花销大、排产方案低效等问题,首先建立虚拟单元重构的多目标规划模型,以多批订单总生产时间、运输设备总运输距离以及车间生产设备负荷均衡因素为目标函数,以车间设备加工能力和火箭壳体加工工艺限制为约束条件;其次提出一种改进的NSGA-II算法对模型进行优化求解,针对NSGA-II算法局部搜索能力的不足,在NSGA-II算法交叉过程中引入“首次改进”和“随机改进”两种局部搜索策略,提高该算法的局部搜索能力;最后基于超体积、均匀性两个多目标评价指标对提出的改进NSGA-II算法与传统的NSGA-II算法以及NSGA-III算法进行比较,结合实例验证了改进NSGA-II算法在进行火箭壳体虚拟单元重构时更加高效.     

求解柔性机器人车间调度问题的混合蚁群算法

在柔性作业车间调度问题的基础上,考虑多台搬运机器人执行不同工序在不同机床之间的搬运,形成柔性机器人作业车间调度问题,提出混合蚁群算法。用改进析取图对问题进行描述,使用混合选择策略、自适应伪随机比例规则和改进信息素更新规则优化蚁群算法,结合遗传算子完成机床选择和工序排序。使用一种多机器人排序算法完成搬运机器人分配和搬运工序排序。通过多组算例仿真测试并与其他算法进行比较,验证了算法的有效性和可靠性。     

基于需求优先的多目标柔性车间调度研究

为满足按时提交客户货物的要求,需要优化企业的生产调度,现实的生产调度问题是传统车间调度问题的扩充,具有多目标、柔性等特性。针对柔性作业车间调度的需要,提出了在精益制造下的基于需求优先的多目标柔性车间调度算法。该算法以工件提前／拖期惩罚代价最小,调度最小生产周期为目标,基于规则的改进启发式调度,在调度过程中通过需求日期计算工件的优先级为每道工序分配合适的机器进行加工,可得到满意的较优解。与其他方法进行对比试验的结果表明,该算法在求解柔性作业车间调度问题是有效的。     

粒子群算法在车间作业调度问题中的仿真研究

研究车间作业调度问题,优化资源配置.车间作业度问题(JSP)是一类典型的NP-hard问题,针对传统方法在JSP应用过程中,存在速度慢、易陷入局部最优,导致车间作业调度效率低.为了解决车间作业调度效率低的难题,提出了一种粒子群算法的车间作业调度方法.该方法将每个粒子代表一种作业调度方案,以最小化加工时间作为算法的优化目标,通过粒子群之间的协作来获得最优作业调度方案.采用JSP标准测试案例在Matlab平台上对该方法进行了验证性实验,实验结果表明,相对于传统方法,该方法能够在最短时间找作业调度的最优解,提高了车间作业调度效率,是一个求解车间作业调度问题的有效方法.     

Robotic material handler scheduling in flexible manufacturing systems for mass customization

The scheduling problem of robotic material handlers in flexible manufacturing systems (FMSs) is NP-hard. This paper proposes a state-dependent algorithm for the FMS robot scheduling problem in make-to-order (MTO) environments for mass customization (MC). A mathematical model of the problem is formulated. A computational study of the proposed algorithm is performed. The algorithm is compared to an effective FMS robot scheduling rule, the shortest remaining processing time first (SRPF) rule. The results reveal the effectiveness of the algorithm in increasing the productivity-based measures of the FMS. Practical application insights are discussed. Further research is also provided.     

An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals

With the development of intelligent manufacturing, production scheduling and preventive maintenance are widely applied in industry to enhance production efficiency and machine reliability. Therefore, according to the different processing states and the physical degradation phenomena of the machine, this paper proposes an accurate maintenance (AM) model based on reliability intervals, which have different maintenance activities in diverse intervals and overcome the shortcoming of the single reliability threshold maintenance model used in the past. Combining the flexible job-shop scheduling problem (FJSP), an integrated multiobjective optimization model is established with production scheduling and accurate maintenance. To strengthen the ability of the evolutionary algorithm to solve the presented model/problem, we propose a novel genetic algorithm, named the approximate nondominated sorting genetic algorithm III (ANSGA-III), which is inspired by NSGA-III. To improve the performance of the Pareto dominance principle, the local search, the elite storage for the original algorithm, the approximate dominance principle, the variable neighborhood search, and the elite preservation strategy are proposed. Then, we employ a scheduling example to verify and evaluate the availability of the above three improved operations and the proposed algorithm. Next, we compare ANSGA-III against five recently proposed algorithms, representing the state-of-the-art on similar problems. Finally, we apply ANSGA-III to solve the integrated optimization model, and the results reveal that the machine can maintain higher availability and reliability when compared to other models in our experiments. Consequently, the superiority of the proposed model based on accurate maintenance of reliability intervals is demonstrated, and the optimal reliability threshold between the yellow and red areas is found to be 0.82.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

考虑元素烧损和仓储成本的特种铝合金配料优化

配料计算是特种铝合金熔炼的重要准备工序,直接影响产品的最终性能.为提高产品质量和配料效率,降低原料和仓储物流成本,建立考虑元素烧损和旧料循环利用等因素的特种铝合金配料优化模型.针对该模型的目标多样性和非线性等特点,设计以投料量和投料时间为决策变量的实数编码规则,提出一种基于第3代非支配遗传算法并融入分布式估计策略的多目标优化算法用于求解该模型.通过基于真实生产数据的仿真实验进行模型和算法验证.实验结果表明,所提出模型能够有效地解决特种铝合金配料优化问题,与传统的多目标优化算法相比,所提出求解算法能够获得更优的结果.     

An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems

Generally, in handling traditional scheduling problems, ideal manufacturing system environments are assumed before determining effective scheduling. Unfortunately, “ideal environments” are not always possible. Real systems often encounter some uncertainties which will change the status of manufacturing systems. These may cause the original schedule to no longer to be optimal or even feasible. Traditional scheduling methods are not effective in coping with these cases. Therefore, a new scheduling strategy called “inverse scheduling” has been proposed to handle these problems. To the best of our knowledge, this research is the first to provide a comprehensive mathematical model for multi-objective permutation flow-shop inverse scheduling problem (PFISP). In this paper, first, a PFISP mathematical model is devised and an effective hybrid multi-objective evolutionary algorithm is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In the proposed algorithm, we take an insert method NEH-based (Nawaz–Enscore–Ham) as a local improving procedure and propose several adaptations including efficient initialization, decimal system encoding, elitism and population diversity. Finally, 119 public problem instances with different scales and statistical performance comparisons are provided for the proposed algorithm. The results show that the proposed algorithm performs better than the traditional multi-objective evolution algorithm (MOEA) in terms of searching quality, diversity level and efficiency. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve PFISP in inverse scheduling domain.