Scheduling Algorithms for the Unbalanced Production Line: An Analysis and Comparison

This paper presents an analysis and comparison of scheduling algorithms for the unbalanced production line. A new heuristic algorithm is presented accompanied by an index for classifying the configuration of a production line. A factorial experiment was conducted in order to determine those factors which were significant with respect to the performance measure under consideration, the average cost of holding in-process inventory. Additional analyses were then performed on the line factors and scheduling algorithms with appropriate conclusions drawn. The best performing algorithm was then compared to the performance of Single Period integer programming over a multiple time frame. The heuristic algorithm was found to yield better average performance than the integer programming solution, although the differences were not statistically significant. Recommendations concerning the implementation of the heuristic algorithm are provided.     

带时间窗口的自动化混流生产线调度方法

研究了一类带时间窗口的自动化混流生产线调度问题,其中包括工件排序和机器人搬运作业排序两类问题.在对问题进行系统分析的基础上,考虑处理时间窗口约束、机器人搬运能力约束和工作站能力约束,使用混合整数规划方法建立了此类问题的通用数学模型,并采用CPLEX软件求解模型,以印刷电路板电镀生产线为实例并通过随机算例验证该方法的有效性.     

钢管冷区生产调度的一种启发式算法

基于国内大型钢铁公司的实际操作,运用离散化时间的方法把钢管冷区生产调度抽象为可中断Job-Shop问题.同时考虑前置库存限制、生产尽可能连续、部分人工调度等约束条件,以最小化中断次数和尽可能满足人工调度为目标,建立相应的数学模型.再依据约束条件的主次性设计生产调度规则(赋值规则和生产规则),根据实际生产流程设计启发式算法.运用该算法对该问题的大规模算例进行求解,其结果在满足了约束条件情况下达到比较理想的中断次数.     

针对项目式生产调度的模拟退火算法

分析了某航空航天企业生产现状,考虑到工时不确定性,建立了数学模型。提出了模拟退火启发式算法,以此制定主动调度计划,并且结合企业生产的实际情况,进行了验证。应用实践表明,相较于单纯求解工期最短的调度计划,该算法的解能很大程度上提高计划的鲁棒性,不需要过多延长工期,对于工时可变范围大的生产项目尤其适用。     

一种面向卷烟混合生产线的优化调度模型研究

目的 基于多品种、小批量的生产制造模式,在规定的8 h工作之内,快速有效地生产出多品种、多批量的卷烟,并对所需物料进行准确调度,达到经济效益最大化的目的。方法 针对某烟草生产企业订单需要生产6种型号的品牌香烟,通过分析卷烟生产线的工艺流程,提出一种解决卷烟厂车间资源优化调度的CSS模型,该模型可以根据产品之间的销售需求情况来匹配生产线资源配置,按需求比采用粒子群优化算法计算出单次投料后混合生产香烟所需的最小时间与最大收益。结果 将所得方案进行综合对比后,计算得出在规定工期内,生产香烟获取利益的最优分配方案,在迭代在10次以内时已完成了优化过程,最大获利为3.65万元。结论 该优化模型通过改变相关工艺参数能够实现对不同混合生产线的资源调度优化,并对其他制造行业提供借鉴价值。     

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。     

基于变量分离的生产调度空间划分算法

静态生产调度大多形成MILP或MINLP模型，由于调度规模大及混合整数规划的组合优化特性，造成调度求解困难。通过对混合整数规划模型空间的分析，提出依据整数变量和连续变量的分离策略进行空间的自然划分，从而将模型的求解转化为多个较小规模连续子空间的寻优。对典型间歇调度模型的分析表明，将空间划分后进行连续寻优的策略较大地降低了实际运算的规模，降低了求解难度，可以提高问题的求解速度和效率。     

面向节能无关联平行机调度模型及分支定界法

工业企业,特别是高耗能行业,不仅要满足交货期和缩小生产周期的要求,而且不断优化能源配置,降低能耗。研究一类新的以延迟和能源消耗的加权最小为目标的生产调度问题。首先,描述问题并分析问题的复杂性。其次,建立混合整数线性规划模型。进一步,我们提出求解该问题的分支定界算法。最后,通过数值实验和数值试验,验证算法的有效性和高效性。     

基于多代理和遗传算法的协同生产调度研究

针对经典作业车间调度(Job-Shop Scheduling)问题的局限性,结合实际生产情况,提出了基于多代理机制的供应链环境下的企业间的协同生产调度(Cooperated production scheduling)系统的体系结构,结合多代理的灵活性和遗传算法的全局优化性,阐述了一种基于多代理和遗传算法的协同调度系统,并寻求适合于这种供应链环境的优化调度方案.     

A Simple Algorithm for A Workforce Scheduling Model

This paper presents a simple solution algorithm to the problem of scheduling workforce for an organization operated seven days a week. For a given number of full-time workers, the objective is to maximize the total number of workers who get two consecutive days off. It can be shown that due to the special structure of the derived mathematical model, an optimal schedule may be found by a few arithmetic steps without the computer assistance which other existing methods require.     

仿生群智能算法在生产调度中的应用综述

群智能算法是在观察和研究群居生物群体行为的基础上,提出的人工智能模拟模式,通过模拟群体中多个个体之间的简单协作实现问题的求解。在介绍群智能算法原理的基础上,综述并讨论了其在生产调度中的应用,为未来的研究提供了有意义的借鉴和参考。     

A Branch-and-Bound Algorithm for the Continuous-Process Job-Shop Scheduling Problem

The continuous-process job-shop scheduling problem (CPJS) arises typically in the following way: (1) a set of M machines or production facilities are available; (2) a set of N jobs are to be processed through these machines in accordance with a technological matrix; (3) the machines associated with a given job must all be used simultaneously for the completion of this job; (4) a predetermined production time is required for each job; (5) the objective is to determine a production schedule which minimizes the total completion time (makespan) of all jobs. A branch-and-bound type algorithm for the solution of the (CPJS) problem is presented.     

A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem

The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem. To overcome these limitations, this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany. Through the introduction of a grafted population and crossover probability matrix, this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence. Finally, the approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.     

A Branch-and-Bound Algorithm for Flow Shop Scheduling Problems

This article is concerned with the solution of the flow shop scheduling problem in which all jobs have the same machine ordering. A branch-and-bound algorithm is developed for finding the sequence of J jobs to be processed on M machines which minimizes the schedule time. Thib algorithm consists of branching and bounding processes, but without the backtracking process which guarantees optimality. The procedure employed is that in constructing a subset of feasible sequences, a node representing a partial sequence is branched. Selection of the node depends on the lower-bound concept as a decision rule. This lower bound is based on resolving the conflict of jobs on the last machine. By using this algorithm, the number of explored nodes is considerably reduced and, hence, the computational effort involved in obtaining an optimal or near-optimal solution is decreased. High quality of solutions is obtained. Computationally, this algorithm extends the size of problems that can reasonably be solved.     

A Multi-Resource Project Scheduling Algorithm

A heuristic algorithm for scheduling a multi-resource project is described where the objective is to minimize the project cost for any project duration between some specified time range. Input conditions are realistic and include alternative resource combinations (ARCs) for each activity, resource cost functions and hiring and dismissal costs for changes in resource quantities. The algorithm selects that combination of activity ARCs that gives the minimum cost solution for any feasible project duration, and eventually generates the Variable Cost-Time trade-off relationship from which the minimum cost conditions for the project can be found.     

适合柔性生产的调度技术

具有竞争力的生产组织要求精益化和柔性的数据处理方法，用于订单计划的制订以及订单的执行。现代生产调度技术严格遵循企业流程，并有效支持所有生产后勤工作部门的应用者。其结果是低成本、经改善的供货能力和员工更高的积极性。     

A Heuristic Production Lot Scheduling Model

This article describes a model which seeks to schedule the production of m products over the next n production planning periods in a manner which minimizes total setup, production, and inventory costs while observing all constraints imposed by the capacities of the productive resources. The model formulates this problem as a fixed charge problem, and then uses a modified version of the simplex method to locate optimal or near optimal solutions of this nonlinear programming problem. A discrete optimizing approach is used to estimate the effectiveness of the model.     

面向柔性作业调度问题的启发性规则改进遗传算法

对柔性作业调度问题,提出了一种启发性规则的改进遗传求解方法,此方法从启发性规则出发产生初始调度解。通过对初始调度解进行比较而产生初始种群。对初始种群通过启发规则的改进遗传算法进行优化计算,对染色体进行交叉、变异、交换和选择操作,应用启发式规则搜索关键工序并提高关键工序的交换、变异操作概率,在变异操作中利用启发式规则对变异过程加以引导,从而得到优化解。将此方法运用于一系列典型柔性调度问题进行了实验求解,并将求解结果与其他的计算方法进行了比较,表明此方法能提高求解效率,适合复杂的柔性作业调度问题求解。     

混合流程型企业主生产计划的启发式算法

提出了一个新的启发式算法,该启发式算法称为多目标主生产计划算法(MOMPS),用于解决混合流水线车间的主生产计划安排,该启发式算法主要有以下目标:最小化拖期惩罚,最小化完工时间,最小化装设和库存成本等.该算法先对所有的定单进行排序,然后根据最小生产成本树及其该树的最大生产能力进行定单的分配,如果定单数量超出了最大生产能力,对生产网络进行调整,通过比较次优生产成本树和拖期以后的最小生产成本决定定单是否该拖期.最后通过和一般的线性规划进行比较,得出该算法在解决混合流程型企业的多目标主生产计划的制定中十分有效,有时得到的结果和线性规划模型解出的解是一致的.     

生产调度与维护集成的多目标Lorenz非劣遗传优化

研究了一种单机环境下集成生产和维护的双目标优化调度问题。机床的故障间隔时间和平均维修时间服从指数分布,同时结合加工序列相关准备时间。预防性维护活动不能与作业加工同时进行,但与准备时间不相冲突。调度目标是同时最小化作业总计完成时间和机床不可得性。在问题建模的基础上,构造了一种基于Lorenz非劣关系的分类遗传算法(表示为L-NSGA-Ⅱ),详细设计了算法的核心部分。最后,通过大量计算实验,将L-NSGA-II算法与NSGA-II算法进行了比较分析,说明了L-NSGA-II算法的有效性。