基于粒子群算法的流程工业生产调度研究

以优化流程工业生产为目标,研究了将基于惯性权重的粒子群算法应用到流程工业的生产调度问题。在对流程工业生产调度问题进行分析的基础上,建立了以总加工完成时间最短为优化目标的生产调度模型。调度算法采用动态惯性权重,使惯性权值在粒子群算法搜索过程中线性变化,以提高粒子群算法的优化性能。给出了粒子编码与解码实现方法,以及具体的算法实现过程。以某流程工业企业生产调度实例为例,利用建立的优化调度模型和设计的粒子群算法进行了实验仿真,结果表明,建立的调度模型和设计的算法是可行的,与蚁群系统方法相比较,有较好的调度性能,适用于解决流程工业实际生产调度问题。     

飞机指派问题优化模型及算法研究

为了提高航空企业飞机排班计划的自动化水平,分析了航空企业飞机排班计划编制流程,将这个复杂组合优化问题分解为3个组合优化问题,重点研究了其中的飞机指派优化问题,归纳了要考虑的主要约束条件,以优化理论为基础,针对飞机排班计划优化问题中的关键问题—飞机指派问题建立了飞机指派优化模型,模型考虑了飞机与航班之间在机型、飞行区域、客流量等条件上的匹配要求,并给出了模型约束条件的编码方法,同时根据大量实际生产数据给出相应的惩罚系数表。为求解模型,构造了一种自适应单亲遗传算法,算法选用了适合模型的遗传算子,采用动态调整遗传算子操作概率的方式加快优化速度。采用航空公司的实际航班数据进行仿真实例研究结果表明,该模型和算法切实可行。     

飞机排班调度中机组指派优化模型及算法研究

分析了航空企业飞机排班计划编制流程,重点研究了其中的空勤机组指派优化问题,建立了机组指派优化模型,模型同时考虑了机组与航班执行飞机之间在机型、飞行区域等条件上的匹配要求。为求解模型,构造了一种改进遗传算法,算法采用自然数编码,动态自适应调整交叉和变异概率,以及智能启发式规则修正的方式加快优化速度。采用航空公司的实际航班数据进行仿真实例研究结果表明,模型和算法切实可行。     

基于遗传算法的港口设备事后维修的单机调度

针对港口设备在损坏后的维修调度问题,即事后维修的调度问题,通过对港口设备的事后维修调度安排进行分析,建立维修设备的调度模型。模型中使用BP神经网络算法来量化港口待维修设备的权值,并利用遗传算法来最小化维修作业任务的总加权完成时间,获得优化后的维修调度顺序和相对应的维修时间安排。通过港口吊具设备的维修算例,展示了优化的调度模型在港机设备中的运用,模型明确了港机的维修顺序,并在保证维修任务完成的情况下节约了维修时间,为港口设备维修计划提供参考。     

设备维修最优化调度策略方法研究

信息化条件下,战时装备维修优化调度问题是装备维修保障过程中的关键问题。本文根据PSO算法建立模型提出了战时装备维修保障调度策略,最大限度的提高战时维修保障系统的效能,同时对PSO算法进行改进,解决算法中的局部最优化问题,最后与基于FCFS算法的维修保障调度策略进行对比,通过仿真实验证明PSO算法对调度性能有明显改善。     

热轧带钢轧制批量计划优化模型及算法

基于奖金收集车辆路径问题模型建立了热轧带钢生产批量计划多目标优化模型．模型综合考虑了生产工艺约束、用户合同需求以及综合生产指标优化等因素．利用加权函数法将多目标优化模型转换为单目标优化模型,针对模型特点设计了蚁群优化求解算法,算法中嵌入了单向插入和2-opt局部搜索过程．引用某钢铁企业热轧生产轧制批量计划编制的实际问题对模型和算法进行了验证,结果表明模型和算法的优化效果和时间效率是令人满意的．     

两部件系统视情维修与生产调度的联合优化模型

为了解决生产调度过程中由系统维护维修产生的资源闲置和时间成本增加问题,将系统维修与生产调度联合建模.在众多学者将系统作为整体进行生产调度与维修研究的基础上,考虑系统内各组成部件之间的复杂关系.针对具有经济相关性的两部件系统,以调度作业加工顺序、预防性维修阈值、机会维修阈值作为决策变量,考虑到两部件同时维修比单部件独立维修更为经济,将机会维修引入到建模之中,制订机会维修、预防性维修、故障后更换的视情维修与生产调度结合的联合策略,通过劣化状态空间划分法给出生产调度过程中所有维修组合及其对应维修概率,推导出联合概率密度函数,建立以最小化总加权期望完成时间为目标的联合优化模型.通过数值实验和灵敏度分析验证所提出的策略及模型的有效性.     

混合粒子群算法和多Agent系统的协同生产调度及其组合优化

为了从整体角度优化调度供应链网络的各个环节,研究了在供应链环境下核心制造商与制造商的协同生产调度方案,考虑在满足产品生产时间节点的最少加工时间。建立了多Agent的供应链环境下的协同生产调度模型,针对此模型设计了协同混合粒子群优化算法并进行求解。通过实例研究表明,供应链环境下制造商的协同优化对生产的计划与实行起到了关键的作用。     

焦炉作业计划与优化调度系统设计与实现

针对钢铁企业焦炉生产计划编排问题,建立一个适合正常工况和异常工况的焦炉作业计划与优化调度系统.该系统由决策支持模块、正常工况计划编制模块和异常工况计划编制模块组成.决策支持模块根据获取的现场数据进行工况判断;在正常工况下采用"5-2"推焦串序操作来编制计划;在异常工况下针对乱笺、推焦事故和病号炉等异常状态,建立优化调度模型,基于蚁群算法进行求解来获得推焦计划.实际应用表明.该系统实现了焦炉作业计划的自动编排与优化调度,保证了结焦时间,提高焦炭质量.     

多目标军用飞机维修作业调度优化研究

为提升维修作业与现代战机的适应程度,对军用飞机维修作业调度模型构建与调度优化算法设计进行探讨。在沿用柔性作业车间调度问题的形式化描述构建维修作业调度模型的基础上,选取遗传算法对执行步骤进行设计,引入耦合算子重新调整工序排序部分染色体以避免染色体违背耦合约束无法解码的情况发生,并采用维修作业调度案例与Brandimarte测试数据验证多目标调度优化算法的适用性与优化性。维修作业调度模型构建与调度优化算法的探讨促进维修管理的精细化,为调度相关领域的深入研究拓宽思路。     

An optimization algorithm inspired by propagation of yeast for fleet maintenance decision making problem involving fatigue structures

To minimize airline maintenance costs and maximize fleet availability, we developed a fleet maintenance decision-making model based on CBM with collaborative optimization (CO) for fatigue structures. The model is divided into two levels: a system level and a subsystem level. Different optimization routines are used at these two levels. The system level focuses on maximizing fleet availability and the subsystem level focuses on minimizing aircraft maintenance costs. Moreover, we proposed an optimization algorithm inspired by the propagation of yeast (OA/PY) to handle the situation where optimal solution is not unique. Finally, a case study regarding a fleet of 10 aircrafts is conducted, and the results demonstrated the effectiveness of the proposed algorithm. In the case study, aircraft maintenance planning (subsystem level) was obtained, and then it was adjusted with OA/PY to obtain optimal fleet maintenance plan (system level). Total incremental maintenance cost caused by the adjustment in the proposed method was reduced by 70.65%.     

Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance

The joint optimization of production scheduling and maintenance planning has a significant influence on production continuity and machine reliability. However, limited research considers preventive maintenance (PM) and corrective maintenance (CM) in assembly permutation flow shop scheduling. This paper addresses the bi-objective joint optimization of both PM and CM costs in assembly permutation flow shop scheduling. We also propose a new mixed integer linear programming model for the minimization of the makespan and maintenance costs. Two lemmas are inferred to relax the expected number of failures and CM cost to make the model linear. A restarted iterated Pareto greedy (RIPG) algorithm is applied to solve the problem by including a new evaluation of the solutions, based on a PM strategy. The RIPG algorithm makes use of novel bi-objective-oriented greedy and referenced local search phases to find non-dominated solutions. Three types of experiments are conducted to evaluate the proposed MILP model and the performance of the RIPG algorithm. In the first experiment, the MILP model is solved with an epsilon-constraint method, showing the effectiveness of the MILP model in small-scale instances. In the remaining two experiments, the RIPG algorithm shows its superiority for all the instances with respect to four well-known multi-objective metaheuristics.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

基于灾变机制的预防性维护和生产调度集成优化方法

为了解决生产车间中多品种任务的生产调度与预防性维护集成优化问题,综合考虑其加工顺序、生产批量及预防性维护策略等要素,在订单充足的前提下,以总制造成本和加工时间最小化为联合优化目标,建立了生产调度与预防性维护集成优化模型。针对模型特点,在非支配排序遗传算法框架的基础上,基于灾变机制和荣誉空间,引入截断和拼接操作算子,提出一种变长度染色体单亲遗传算法对模型进行求解,并在不同参数条件和问题规模下,通过仿真实验验证了该算法解决复杂生产任务调度和预防性维护集成优化问题的有效性。     

有限产能下多部件设备视情维修策略与多订单批量生产联合优化研究

针对有限产能下,面对多个不同要求订单时的订单选取和多部件设备维护决策问题,提出了基于客户满意度的多部件设备视情维修策略和多订单批量生产的联合优化模型。首先,基于比例风险模型描述出各部件的具体劣化趋势,并以虚拟年龄表示各部件的健康状态;其次,根据单次批量生产结束后的实时监测结果和维修阈值,确定不同的维修方式;再次,以利润最大化为优化目标,结合有限产能批量问题模型和客户满意度模型,建立视情维修策略和多订单批量生产联合优化模型;最后,利用模拟退火算法和粒子群算法就行模型求解,并通过算例验证了模型的有效性,使模型更具有现实意义,并对模型相应参数进行了灵敏度分析。     

基于粒子群算法的轧制批量计划问题研究

将轧制批量计划编制问题归结为车辆路径问题,采用粒子群算法对模型求解,设计了轧制批量计划问题的编码方案,阐明了算法的具体实现过程。计算结果表明,利用粒子群算法解决热轧批量计划问题是有效和可行的。     

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.     

Aviation maintenance technician scheduling with personnel satisfaction based on interactive multi-swarm bacterial foraging optimization

This study focuses on the challenges of aviation maintenance technician (AMT) scheduling and constructs a model based on personnel satisfaction and the parallel execution of aircraft maintenance tasks. To obtain the scheduling scheme from the constructed NP-hard model, an interactive multi-swarm bacterial foraging optimization (IMSBFO) algorithm is proposed using multi-swarm coevolution, structural recombination, and three information interactive mechanisms among individuals. Moreover, considering the distributed feature of the AMT scheduling problem, a specific mechanism is designed to convert continuous solution to a binary AMT scheduling scheme. Finally, a series of comparative experiments highlight the efficiency and superiority of our proposed IMSBFO algorithm, and the optimal scheduling scheme owns the delicate balance between the work and rest time.     

双种群混合遗传算法的裁剪分床应用研究

为解决服装生产中的裁剪分床计划问题,结合生产过程的影响因素和订单需求,建立了裁剪分床的多目标数学模型进行优化,使用一种改进的双种群粒子群-遗传混合算法对模型进行求解。混合算法将进化种群划分为普通种群和精英种群,利用改进的遗传算法来全局搜索进化普通群体并筛选精英个体,同时结合粒子群优化算法进化精英群体。交叉和变异保证种群的多样性,粒子群寻优机制提升进化速度,两种群在进化时交叉影响不断寻找最优方案。实验结果表明：混合算法在解决多目标的生产订单裁剪分床问题上表现稳定,相比改进的遗传算法有更快的寻优速度,比手工计算方法减少1个裁床,裁剪时间缩短5?min且超裁数量降低60%,可以适应不同目标需求,针对实际生产中的裁剪分床有一定的应用价值。