基于自适应蚁群算法的作业车间模糊调度研究

在研究不确定生产调度问题的基础上,针对具有模糊加工时间和模糊交货期的调度问题给出了作业车间模糊调度模型,用三角模糊数表示模糊加工时间,梯形模糊数表示模糊交货期,以交货期平均满意度最大作为调度目标.针对模糊调度问题对基本蚁群算法作了改进,并给出了新的状态转移规则,同时采用自适应信息素更新策略使算法能快速跳出局部收敛,进行仿真结果验证了自适应蚁群算法求解作业车间模糊调度的有效性.     

模糊环境下的差异作业单机批调度优化问题的研究*

针对现实生产制造系统中存在的时间参数模糊化问题,本文用梯形模糊数表征时间参数,给出了一种具有模糊加工时间和模糊批次间隔的、以最小化制造跨度为目标的模糊差异作业单机批调度问题模型。在对模糊差异作业单机批调度问题进行有效求解方面,针对基本粒子群算法容易陷入局部最优的问题,本文给出了一种基于遗传操作的混合粒子群算法,利用遗传算法思想对粒子进行交叉、变异操作,增强了算法跳出局部最优的能力。仿真实验验证了该算法具有可行性和有效性。     

基于双种群模糊引力搜索算法的舰载机甲板作业调度

舰载机甲板作业调度问题是一类具有NP-hard特性的资源受限多项目调度问题.首先,分析舰载机甲板作业调度问题的工序流程约束和各类资源约束,构建舰载机甲板作业调度混合整数规划模型.然后,基于基本引力搜索算法,提出双种群模糊引力搜索算法用于模型求解.算法采用基于作业时序修正的优先数编码,并采用双种群交替迭代结构,将基于个体的双向对齐技术扩展到种群层面,基于串行调度生成机制产生调度方案.为了提高算法性能,采用边界修正策略修正越界粒子编码,在引力计算阶段,采用模糊逻辑控制策略进行参数自适应控制.最后,通过案例仿真和算法对比验证了双种群模糊引力搜索算法的有效性,所提出的算法适合求解大规模的舰载机甲板作业调度问题.     

模糊环境下多目标差异作业单机批调度问题研究

针对现实生产制造系统中存在的时间参数模糊化问题,采用梯形模糊数表征时间参数,给出一种具有模糊交货期和模糊加工时间,以最小化提前/拖期惩罚、制造跨度以及加工费用为目标的多目标差异作业单机批调度问题模型.在对该问题进行求解方面,针对基本粒子群算法容易陷入局部最优的问题,引入混沌局部搜索策略,给出了一种基于混沌优化技术的混合粒子群算法.仿真实验验证了所提出算法的可行性和有效性.     

具有不精确活动时间的项目调度算法

针对以最小化项目工期为目标的资源受限项目调度问题,提出对不精确活动时间项目调度的求解方法。对现实项目调度中存在的不精确活动时间及模糊资源分配进行分析,在模糊集理论基础上建立了数学模型,提出一种基于蚁群算法的对不精确活动周期下的项目调度问题求解方法。人工蚂蚁的初始节点采用概率优先约束原则选择,以避免单一概率选择可能导致的过快收敛的局限性,提高解的质量;对算法所使用的重要参数的选择进行分析说明,给出计算方法。进行模拟实例并与其它实验结果进行对比,对比结果表明了该算法的有效性和可行性。     

基于不确定作业时间的FSM P 调度问题研究

针对一类加工时间不确定的FSMP调度问题，建立了基于扩展期望区间数近似不确定参数的优化模型．提出了利用扩展期望区间数构造参数近似边界的取极大运算方法，给出了包含不确定度控制指标的多目标优化模型．基于算例，讨论了决策因子和不确定度控制指标对调度性能的影响，仿真结果及分析表明了该模型和算法的有效性与鲁棒性．     

基于模糊理论的软件项目调度算法

针对软件项目调度问题中信息的不确定性及资源分配的特殊性需要,提出了一种基于模糊理论的软件项目调度算法FSSA。该算法采用模糊数表示任务的工期并应用遗传算法产生任务的调度序列。实验结果表明,FSSA算法能在很短的时间内得到理想的结果,有一定的应用价值。     

不确定周期约束下项目调度算法

为了能让企业通过良好的项目调度的方法缩短产品的项目周期,提出了对不确定周期约束项目调度的求解方法。对现实生产生活中存在的不确定因素进行分析,指出不确定项目周期和不确定资源这2个不确定因素。在此基础上建立了总体模型。提出了一种基于模糊理论的对不确定周期约束下项目调度问题的求解方法。该方法以遗传算法为基础,通过与模拟退火算法的结合可以很好地克服遗传算法自身的缺点,从而得到能更好地反应现实需求的结果。对算法进行了实例验证,结果表明了该方法的准确性、有效性和可行性。     

Joint replenishment model under fuzzy costs and resource constraints——differential evolution algorithm approach

针对复杂不确定环境下的联合采购决策难题,用三角模糊数表示不确定的次要订货费用、库存持有费用和资金约束条件,用梯形模糊数表示不确定的存储空间约束,构建了模糊联合采购模型,并采用两种方法对模糊总成本进行去模糊化处理.进而在对差分进化(DE)算法改进并借助典型函数测试性能的基础上,给出了基于改进DE的模糊联合采购模型求解流程,算例证明所设计的DE算法能较好地解决模糊联合采购问题.     

不确定条件下的中间存储时间有限的Flow Shop提前/拖期调度问题

生产调度是企业生产过程中一个重要的部分,在实际生产调度过程中,会存在各种各样的不确定性.本文针对不确定条件下的具有不同交货期窗口的F low Shop提前/拖期调度问题,考虑了产品中间存储时间的有限性,用模糊数学的方法来表示产品的不确定处理时间,在模糊规划理论的基础上建立了相应的调度模型.并借鉴生物免疫系统的概念和机理,提出了解决此类特殊问题的模糊免疫调度算法.仿真结果表明了该模型的有效性和算法的较好的收敛效率.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

Solution of fuzzy multi-objective project crashing problem

Project management is a very important field employed for scheduling activities and monitoring the progress, in competitive and fluctuating environments. The feasible duration time required to perform a specific project is determined using critical path method. However, because of competitive priorities, time is important and the completion time of a project determined using critical path method should be reduced to meet a deadline requested. In this situation, project crashing problem arises. Project crashing analysis is concerned with shortening the project duration time by accelerating some of its activities at an additional cost. In general, the parameters of the problem are accepted as certain and the project crashing problems are solved using deterministic solution techniques. In reality, because of uncertain environment conditions, incomplete or unobtainable information, there can be ambiguity in the parameters of the problem. The uncertainty in the parameters can be modeled via fuzzy set theory. Using fuzzy models gives the chance of better project management decisions with more stability under uncertain environmental factors. In the literature, various authors solved different fuzzy versions of project management problems via transforming them into their crisp equivalents. In this study, a fuzzy multi-objective project crashing problem with fuzzy parameters is handled. The fuzzy project crashing problem is solved with a direct solution approach based on fuzzy ranking methods and the tabu search algorithm.     

多特征参数的任务模糊调度算法

针对任务具有特征参数多和特征参数不确定性的特点,提出了一种基于模糊理论的任务调度算法。利用模糊集合来描述任务的不确定性特征;使用多层模糊综合评判和最大隶属度原理来综合考虑任务的多个特征参数并确定任务的优先级;采用动态构建多层评判模型的调度策略来减小任务优先级评判的失效率。仿真表明,该算法提高了任务调度的成功率,降低了任务截止期的错失率和任务优先级评判的失效率。该方法可应用于优先等级有限的实时系统任务动态调度中。     

模糊反馈控制实时调度算法

为了解决模糊不确定任务集在不可预测环境下的动态抢占调度问题,应用模糊规则和模糊调度理论,提出一个基于模糊反馈控制的调度算法,并建立相应的调度架构.该架构由基本调度器和模糊反馈控制两部分组成.用模糊调度算法作为基本调度器的调度算法,将任务集按不同优先级等级进行划分,优先级等级高的任务优先调度,从而使得更多的重要任务得到调度;模糊控制器与任务流调节策略一起构成模糊反馈控制部分.仿真结果表明,模糊反     

A fuzzy random resource-constrained scheduling model with?multiple projects and its application to a working procedure in?a?large-scale water conservancy and hydropower construction project

The aim of this paper is to deal with resource-constrained multiple project scheduling problems (rc-mPSP) under a fuzzy random environment by a hybrid genetic algorithm with fuzzy logic controller (flc-hGA), to a large-scale water conservancy and hydropower construction project in the southwest region of China, whose main project is a dam embankment. The objective functions in this paper are to minimize the total project time (that is the sum of the completion time for all projects) and to minimize the total tardiness penalty of multiple projects, which is the sum of penalty costs for all the projects. After describing the problem of the working procedure in the project and presenting the mathematical formulation model of a resource-constrained project scheduling problem under a fuzzy random environment, we give some definitions and discuss some properties of fuzzy random variables. Then, a method of solving solution sets of fuzzy random multiple objective programming problems is proposed. Because traditional optimization techniques could not cope with the rc-mPSP under a fuzzy random environment effectively, we present a new approach based on the hybrid genetic algorithm (hGA). In order to improve its efficiency, the proposed method hybridized with the fuzzy logic controller (flc) concept for auto-tuning the GA parameters is presented. For the practical problems in this paper, flc-hGA is proved the most effective and most appropriate compared with other approaches. The computer generated results validate the effectiveness of the proposed model and algorithm in solving large-scale practical problems.     

Uncertain project scheduling problem with resource constraints

Project scheduling problem is to make a schedule for allocating the loans to a project such that the total cost and the completion time of the project are balanced under some constraints. This paper presents an uncertain project scheduling problem, of which both the duration times and the resources allocation times are uncertain variables. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize the total cost, and second objective is to minimize the overtime. Genetic algorithm is employed to solve the proposed uncertain project scheduling model, and its efficiency is illustrated by a numerical experiment.     

Integrated Fuzzy–HMH for project uncertainties in time–cost tradeoff problem

Time–cost tradeoff (TCT) problem in project scheduling studies how to schedule project activities to achieve a tradeoff between project cost and project completion time. It gives project planners both challenges and opportunities to work out the best plan that optimizes time and cost to complete a project. In this paper, we present a novel method which examines the effects of project uncertainties on both, the duration as well as the cost of the activities. This method integrates a fuzzy logic framework with Hybrid Meta-Heuristic. Hybrid Meta-Heuristic (HMH) is an innovative approach which hybridizes a multiobjective genetic algorithm and simulated annealing. Integration of HMH and fuzzy logic is referred to as ‘integrated Fuzzy–HMH’. A rule based fuzzy logic framework brings up changes in the duration and the cost of each activity for the input uncertainties and HMH searches for Pareto-optimal front (TCT profile) for a given set of time–cost pair of each project activity. Two standard test problems from the literature are attempted using HMH. A case study of TCT problem is solved using integrated Fuzzy–HMH. The method solves time–cost tradeoff problems within an uncertain environment and carries out its sensitivity analysis.     

基于区间值模糊集的柔性工作车间调度模型

论文首先描述了现实柔性工作车间调度中的时间参数不确定性问题,并阐述了模糊理论中α截集、区间值模糊集等相关概念;然后给出了以遗传算法为框架的基于区间值梯形模糊数的柔性工作车间调度模型,并且通过实例验证了模型的可行性和有效性。     

施工项目调度问题的一种智能优化算法

研究了施工项目进度调度问题,提出了一种基于启发式规则和遗传算法的综合智能优化算法,并在施工项目调度问题的描述、带资源约束的施工项目调度问题的分解方法、遗传算法的编码、交叉、变异方法和解码方法等方面进行了研究。不同规模的数值计算结果表明,该算法在解决复杂工程施工项目调度问题上具有良好的性能,并能较好地适用于带时序、资源约束的施工项目调度问题。