基于蚁群算法的车间生产调度模型的研究与应用

针对面向订单的车间生产调度问题的局限性,结合实际生产情况,建立了订单车间生产调度管理系统,并对调度算法模型进行了深入研究,提出了一种综合运用理想点求模与蚁群算法的订单多目标优化调度方法。以最小化设备资源利用总时间、最小化产品延迟交货总时间为目标,利用蚁群算法对企业生产能力范围内的订单进行多目标优化调度,调度报表为企业实际生产提供指导。该系统和方法在某重型机械集团公司进行应用,体现了减少劳动力投入、缩短生产周期的效果。     

基于Agent和蚁群算法的混合流水车间调度

混合流水车间调度问题是经典调度问题的推广,但是,由于计算的复杂性,很难求得其最优解,文章提出了利用基于Agent技术的蚁群算法来实现混合流水车间调度优化。描述了算法实现的步骤,以及在JADE平台上人工蚁Agent的创建。最后,结合某生产企业的实际例子,利用蚁群算法来实现其生产调度,其生产调度结果显示,蚁群算法能很好的解决混合流水车间调度问题。     

基于MTO的车间生产运作的优化

文章分析MTO生产企业在车间生产运作中存在的问题，并根据MTO生产企业的特点，研究运用工业工程亏业及相关知识对其车间生产运作进行分析与优化，并提出优化措施。     

SDN供需流的两阶段及其优化分析——基于木地板生产企业的视角

在传统企业向供需网企业转变过程中,作为SDN的一个供需流,其核心集中在对车间生产调度及物流配送的优化上。针对这两个问题,基于木地板生产企业的现状,本文引入了量子粒子群与模拟退火相结合的混合算法,以及一种求解物流系统的整合优化模型与求解的启发式算法分别对其进行研究,具有一定的实用价值。     

关于流水车间调度问题的综述

流水车间调度问题,也被称为同序作业调度问题,是许多实际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.因此,对其进行研究具有重要的理论意义和工程价值.本文介绍了流水车间调度问题的研究现状和几种解决方法.     

关于流水车间调度问题的综述

流水车间调度问题，也被称为同序作业调度问题，是许多实际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。因此，对其进行研究具有重要的理论意义和工程价值。本文介绍了流水车间调度问题的研究现状和几种解决方法。     

基于知识管理的车间调度规划

在以销定产的生产模式下,为了充分利用制造资源,提升制造企业核心竞争力,根据制造车间生产特点,提出了制造车间生产抽象化模型。将车间资源进行了分类,根据制造车间特点归纳出调度时应服从的原则,指出调度应服从的整体经济原则。对制造车间agent建立与调度优化方法,构建调度数据采集和网络做了分析。为功能各异、数据结构不同的车间资源调度的建模提供参考。     

一种车间生产调度仿真的优化方法

文章以制造企业的生成调度为研究背景，提出了一种针对车间生成调度的离散型仿真方法。通过讨论车间调度仿真的主要原理、方法和运用表现，给出了车间生成仿真调度的业务流程和实现方法，并在某企业中试用运行。     

基于改进BBO算法的多目标柔性作业车间调度研究

为了满足市场的快速变化、降低生产成本,车间调度成为生产的关键环节。为解决作业车间分批调度问题,本文在企业资金收益率最大化、物料周转速度最快的情况下,建立以生产周期、交货期满意度为指标的数学模型,并提出一种将DE算法与BBO算法相结合的多目标优化算法模型。最后经过实例仿真,求得的结果证明了算法的有效性。     

基于Plant Simulation的车间调度作业优化研究

针对某变压器生产车间内的作业调度及布局优化问题,采用优化仿真软件Plant Simulation进行分析研究,并且加入了仅仅使用优化方法无法实现的随机因子,使模型更加符合现实。根据仿真实验所得瓶颈分析图及工作站状态图,对现有方案进行优化改进,得到一个较满意的方案。说明利用优化仿真可有效实现对于车间作业调度问题的研究。     

Diverse task scheduling for individualized requirements in cloud manufacturing

Cloud manufacturing (CMfg) has emerged as a new manufacturing paradigm that provides ubiquitous, on-demand manufacturing services to customers through network and CMfg platforms. In CMfg system, task scheduling as an important means of finding suitable services for specific manufacturing tasks plays a key role in enhancing the system performance. Customers’ requirements in CMfg are highly individualized, which leads to diverse manufacturing tasks in terms of execution flows and users’ preferences. We focus on diverse manufacturing tasks and aim to address their scheduling issue in CMfg. First of all, a mathematical model of task scheduling is built based on analysis of the scheduling process in CMfg. To solve this scheduling problem, we propose a scheduling method aiming for diverse tasks, which enables each service demander to obtain desired manufacturing services. The candidate service sets are generated according to subtask directed graphs. An improved genetic algorithm is applied to searching for optimal task scheduling solutions. The effectiveness of the scheduling method proposed is verified by a case study with individualized customers’ requirements. The results indicate that the proposed task scheduling method is able to achieve better performance than some usual algorithms such as simulated annealing and pattern search.     

基于遗传算法的大规模定制供应链调度优化研究

大规模定制(MC)模式下的供应链调度优化过程,是典型的随机需求与资源约束的动态问题。为合理解决这一问题,在前期研究成果基础上,提出了包括供应商选择及企业合作时序安排的优化调度模型,并提出了针对这种模型的求解思路,设计了基于遗传算法的求解过程;通过多组数据实验及结果比较分析,对模型算法的有效性、稳定性进行了验证。     

A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination Algorithm

With the growing complexity of customer requirements and the increasing scale of manufacturing services, how to select and combine the single services to meet the complex demand of the customer has become a growing concern. This paper presents a new manufacturing service composition method to solve the multi-objective optimization problem based on quality of service (QoS). The proposed model not only presents different methods for calculating the transportation time and transportation cost under various structures but also solves the three-dimensional composition optimization problem, including service aggregation, service selection, and service scheduling simultaneously. Further, an improved Flower Pollination Algorithm (IFPA) is proposed to solve the three-dimensional composition optimization problem using a matrix-based representation scheme. The mutation operator and crossover operator of the Differential Evolution (DE) algorithm are also used to extend the basic Flower Pollination Algorithm (FPA) to improve its performance. Compared to Genetic Algorithm, DE, and basic FPA, the experimental results confirm that the proposed method demonstrates superior performance than other meta heuristic algorithms and can obtain better manufacturing service composition solutions.     

基于遗传算法的高校排课系统的研究与实现

遗传算法借鉴生物界自然选择和遗传机制,使用群体搜索技术,处理传统搜索方法难以解决的复杂的非线性问题。排课问题是一个NP完全问题,除了穷举法没有绝对的求解方法。高校排课涉及到课程、教室、班级、教师和时间五方面相互关联的因素。按照遗传算法思想,对初始课表进行优化,并用Delphi进行编程,试验结果表明,遗传算法对课表的优化有着比较显著的作用。     

模拟退火算法在实验预约系统中的应用

实验预约系统的核心任务是生成实验教学课表,课表的生成是一个典型的组合优化问题,模拟退火算法是解决此类问题的优秀算法之一。本文主要研究了模拟退火算法的原理与特性,并将其应用于实验教学预约系统的排课模块中,更好地提高排课效率,改善排课效果。     

Service load balancing,task scheduling and transportation optimisation in cloud manufacturing by applying queuing system

Recently, there is a great deal of attention in Cloud Manufacturing (CMfg) as a new service-oriented manufacturing paradigm. To integrate the activities and services through a CMfg, both Service Load balancing and Transportation Optimisation (SLTO) are two major issues to ease the success of CMfg. Based on this motivation, this study presents a new queuing network for parallel scheduling of multiple processes and orders from customers to be supplied. Another main contribution of this paper is a new heuristic algorithm based on the process time of the tasks of the orders (LBPT) to solve the proposed problem. To formulate it, a novel multi-objective mathematical model as a Mixed Integer Linear Programming (MILP) is developed. Accordingly, this study employs the multi-choice multi-objective goal programming with a utility function to model the introduced SLTO problem. To better solve the problem, a Particle Swarm Optimisation (PSO) algorithm is developed to tackle this optimisation problem. Finally, a comparative study with different analyses through four scenarios demonstrates that there are some improvements on the sum of process and transportation costs by 6.1%, the sum of process and transportation times by 10.6%, and the service load disparity by 48.6% relative to the benchmark scenario.     

卷烟厂制丝车间自动排产问题研究

以卷烟厂为研究背景,对制丝车间生产特点和排产需求进行分析,提出了基于规则的启发式算法,以解决卷烟厂制丝车间排产问题。以生产均衡为目标,构建以工段为整体的倒序排产的制丝车间排产算法,并将该算法应用于某卷烟厂。经实际数据验证,该算法可很好地满足制丝排产的需求。     

城市多网点配送车辆调度模型与算法研究

通过对城市多网点车辆调度特点的深入分析和研究,建立了追求总体效益最优的多网点车辆调度多目标决策模型,并设计了求解该模型有效的启发式算法。     

基于ERP对离散型制造企业生产优化的研究

离散型制造企业由于其自身特点,其生产管理一直是企业的难题。文章主要从生产管理中的生产准备时间和生产排程两方面入手,以流程优化和约束理论为理论依据,运用ERP技术解决企业生产中在这两方面存在的问题,从而提升企业的生产效率和市场竞争力。     

生产企业合同交货期确定及合同排序

讨论了企业订货合同管理中面临的决策问题以及解决途径。应用机器工件调度理论求解包括提前和延期惩罚的合同排序问题，给出了启发式排序规则和求解搜索策略。采用了3种启发式方法：1）接排序规则排序；2）对初始排序施加成对交换技术改善；3）射束搜索技术。实验结果表明，第3种求解质量具有明显优势，能得到令人满意的结果。