基于瓶颈的制造单元两阶段群组调度程序与仿真

为解决具有瓶颈的制造单元调度问题,提出了一种以瓶颈为基础的两阶段群组调度方法.该方法充分运用瓶颈机器,使所有零件的最大完成时间最小化.为评估调度方法的绩效,文章建立了两阶段群组调度程序的5机制造单元仿真模型.仿真实验结果证明,本方法不仅平均总完工时间最短,而且是绩效表现最稳定的调度方法.     

改进遗传算法求解多时间约束的柔性作业车间调度问题

将加工时间、调整时间和移动时间分别作为独立时间因素考虑到柔性作业车间调度模型中,建立以最大完工时间最小、总调整时间最小、总移动时间最小为目标的考虑多时间约束的柔性作业车间调度模型,并提出改进的遗传算法求解该模型。通过测试标准数据集,并对比其他文献算法,验证了改进的遗传算法的可行性和有效性。     

危化品事故连续消耗型应急物资调度模型

应急物资调度是危化品事故应急救援工作的关键内容,危化品事故会对周边区域造成环境污染,其应急物资调度问题应该考虑多个目标。针对危化品事故应急物资调度特征,建立了连续消耗应急物资调度多目标模型。模型以缺货损失最小、调运时间最短和调运总环境风险最小为优化目标,考虑应急中心物资数量限制和救援时间限制等约束条件,采用NSGAⅡ遗传算法求解。对于求解结果,采用逼近理想解的排序方法进行选择。算例计算结果表明,多目标模型优化方案比单目标模型优化方案有较大的优势,同时说明算法合理可行。     

混流无等待总装分装流水线任务排序研究

为解决混流产品在无等待多条流水线生产条件下,由于产品生产节拍不一致导致总装分装系统中生产连续性较差的问题,研究总装分装任务排序优化方法,实现在保证批量生产、部件齐套供应前提下,使订单能够按期交货.以最小化总加工时间、最小化总提前/拖期和产品转换惩罚为优化目标,建立了优化数学模型,并设计了改进多种群蚁群算法求解该优化模型.以某机床厂某月生产任务为例进行仿真实验,与多种群蚁群算法、传统蚁群算法对比,验证了该算法性能较好.并与现行的调度方法进行对比,验证了该任务排序方法在混流节拍不一致的多条装配线生产上,能够有效地缩短产品生产周期、降低生产成本,提高订单的准时交付率.     

基于空间蚁群算法的混流装配线组批排序方法

针对混流装配线按工作日历调度过程中,组批生产导致订单准时交付率差、加班时间长,以及缺料等干扰因素导致完工周期延长等问题,以零部件配套、按工作日历组批为约束,最小化完工周期、提前/拖期时间以及加班时间为多目标,建立工件排序数学优化模型。验证以完工周期最短为目标的流水线工件最优排序亦具有V型特征。提出空间蚁群权重设计方法,将蚂蚁沿不同的权重向量寻优,提高算法的全局搜索能力;并根据当前Pareto解在各权重子空间的分布情况动态调整各子空间的蚂蚁数量,避免陷入局部最优。通过与文献算法对比,验证空间蚁群算法具有良好的优化性能,并通过实例验证了排序方法的有效性。     

基于行为效应的MRO服务中心多技能工调度优化

人力资源是MRO服务中心调度的重要因素。将多技能工调度与员工行为效应即学习和遗忘效应相结合,以最小化任务总完工时间为目标建立模型,并用分段染色体遗传算法进行求解。在模型中考虑多技能工掌握的技能和任务的技能需求相匹配,并考虑员工在一个任务点可以使用其掌握的多项技能。通过算例对模型进行分析,发现任务数量和员工数量对任务总完工时间和作业时间有影响,且考虑行为效应的指派能优化员工安排,缩短任务总完工时间。说明人的行为效应对企业实际利益有重要影响。     

面向生产调度的订货量分配问题研究

为研究多品种批量制造环境下由于供应商交货数量不确定造成物料不齐套进而导致生产计划不可行的问题,以多个供应商和单个制造商组成的二级供应链为背景,提出面向生产过程的供应商选择与订货量分配模型。以包含订货、采购、库存以及拖期惩罚成本的期望总成本最小化为目标,在传统供应商能力限制、订货数量区间要求以及产品生产调度约束的基础上,考虑允许供应商延期交货且拖期时间依赖供应商可靠度的情形,建立了相应的混合整数随机规划模型。针对所研究问题的复杂性及模型特点,采用基于局部搜索和变异机制的改进离散粒子群优化算法对模型进行求解,结合具体交货情景下的工程实例对模型可行性进行了验证,通过与其他方法进行比较,表明所提算法的有效性。     

考虑电量消耗的车辆调度优化研究

基于电动汽车电量消耗特性,考虑电动车里程、载重、顾客服务时间窗等约束,建立以配送总成本最小为目标的电动车调度优化问题模型;利用自然数编码的遗传算法,求解出电动车的配送路线以及车辆的充电计划,再结合枚举法,在配送中心运营时间内以10 min为时间间隔,计算出配送车辆惩罚成本最小时的最优发车时刻。最后结合算例,验证该模型和方法的有效性、正确性。     

加工时间具有似然关系的单机调度北大核心CSSCI

对加工时间不确定的单机调度问题,构建了工件基本加工时间具有似然关系的工件调度模型。该模型中工件基本加工时间可按照似然比排序,并考虑了工件加工时间同时存在学习效应和恶化效应,以最小化所有工件期望完成时间为目标函数。研究表明,每个工件按照其期望基本加工时间的非减序列排序时,完成所有工件的时间最短。最后通过数值算例模拟工件调度过程,验证了该模型能有效地缩短工件完工时间。     

加工时间具有似然关系的单机调度

对加工时间不确定的单机调度问题,构建了工件基本加工时间具有似然关系的工件调度模型。该模型中工件基本加工时间可按照似然比排序,并考虑了工件加工时间同时存在学习效应和恶化效应,以最小化所有工件期望完成时间为目标函数。研究表明,每个工件按照其期望基本加工时间的非减序列排序时,完成所有工件的时间最短。最后通过数值算例模拟工件调度过程,验证了该模型能有效地缩短工件完工时间。     

生产调度干扰管理模型构建及智能算法研究

在现代企业生产经营过程中,生产调度的作用日益突出。它是生产管理领域内的关键生产环节。干扰管理是近些年来学者提出的一种新的处理生产过程中突发事件的思想和方法,已在供应链、物流以及某些特定领域的调度方面有所应用。流水车间调度问题(Flow-shopScheduling Problem,FSP)是一类复杂且极有代表性的流水线生产调度问题的简化模型,它无论是在离散制造工业还是在流程工业中都具有广泛的应用,具有一定的代表性。构建了流水车间调度问题以及干扰为工件到达的流水车间调度干扰管理模型,其经典目标函数为最大完工时间和干扰目标函数为干扰时间差相混合。     

Algorithms for multiprocessor scheduling with machine release times

In this paper we present algorithms for the problem of scheduling n independent jobs on m identical machines. As a generalization of the classical multiprocessor scheduling problem each machine is available only at a machine dependent release time. Two objective functions are considered. To minimize the makespan, we develop a dual approximation algorithm with a worst case bound of 5/4. For the problem of maximizing the minimum completion time, we develop an algorithm, such that the minimum completion time in the schedule produced by this algorithm is at least 2/3 times the minimum completion time in the optimum schedule. The paper closes with some numerical results.     

A decomposition-based approach to flexible flow shop scheduling under machine breakdown

Manufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling under machine breakdown.     

装配作业车间分批调度问题的算法对比

建立最小化makespan的装配作业车间分批调度模型,利用遗传算法构建基于整体集成优化策略、分层迭代优化策略和双层进化策略的求解算法。它们的区别在于处理批量划分问题和子批调度问题的联合优化机制不同。最后通过仿真实验对比了几类算法的求解效果和收敛速度,并分析了它们的适应性特点。     

具有成组约束的柔性同序加工车间的排序算法

针对轮胎模具的生产调度特点,研究了具有成组约束的两阶段柔性同序加工车间(FF2)的调度问题,建立了目标函数为最小化最大完成时间的调度数学模型,基于Johnson准则提出一种新的启发式算法,并应用企业的实际算例,说明了数学模型和求解方法的可靠性和有效性。     

Scheduling in a Semi-Ordered Flow-shop Without Intermediate Queues

The problem of finding a minimum makespan permutation schedule in a deterministic flow-shop without intermediate queues is equivalent to the shortest distance routing traveling salesman problem. If the task system is semi-ordered, the associated distance matrix of the corresponding traveling salesman problem is found to exhibit some peculiar characteristics. Consequently, we are able to derive some important results which help us to eliminate permutations in the search of the minimum makespan permutation schedule. The most important result is that the optimal permutation schedule is pyramidal. An algorithm, having quadratic worst-case complexity in terms of the number of partial schedules explicitly enumerated, has been presented. Some particular cases of the semi-ordered flow-shop are also discussed.     

Jobshop lot streaming with routing flexibility,sequence-dependent setups,machine release dates and lag time

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage manufacturing system so that operations of a given lot can overlap. This technique can reduce the manufacturing makespan and is an effective tool in time-based manufacturing. Research on lot streaming models and solution procedures for flexible jobshops has been limited. The flexible jobshop scheduling problem is an extension of the classical jobshop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. In this paper we develop a lot streaming model for a flexible jobshop environment. The model considers several pragmatic issues such as sequence-dependent setup times, the attached or detached nature of the setups, the machine release date and the lag time. In order to solve the developed model efficiently, an island-model parallel genetic algorithm is proposed. Numerical examples are presented to demonstrate the features of the proposed model and compare the computational performance of the parallel genetic algorithm with the sequential algorithm. The results are very encouraging.     

含不一致任务重量的同型熔炼炉批调度优化

为求解含不一致任务重量的同型熔炼炉批调度问题,建立了最小化最大任务完工时间优化模型,设计了一种混合粒子群算法（HPSO）。算法使用随机生成的任务序列作为粒子,采用批首次匹配（BFF）规则对任务序列分批,最长加工时间（LPT）规则将批分配到批处理机,并提出了一种最小完工时间差（MCD）规则对LPT调度结果进行优化;为避免早熟,算法引入交叉和变异操作搜索最优解。通过仿真实验与SA、GA算法对比,实验结果表明算法具有良好的性能。     

Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm

The integration of process planning and scheduling is considered as a critical component in manufacturing systems. In this paper, a multi-objective approach is used to solve the planning and scheduling problem. Three different objectives considered in this work are minimisation of makespan, machining cost and idle time of machines. To solve this integration problem, we propose an improved controlled elitist non-dominated sorting genetic algorithm (NSGA) to take into account the computational intractability of the problem. An illustrative example and five test cases have been taken to demonstrate the capability of the proposed model. The results confirm that the proposed multi-objective optimisation model gives optimal and robust solutions. A comparative study between proposed algorithm, controlled elitist NSGA and NSGA-II show that proposed algorithm significantly reduces scheduling objectives like makespan, cost and idle time, and is computationally more efficient.