基于遗传算法的水泵机组优化调度研究

从供水系统整体优化和能量损耗最小的角度出发,结合供水厂生产的实际特点,建立了基于遗传算法的水泵机组优化组合的数学模型,并对该数学模型进行求解,得到较为满意的最优解或次优解.实际生产表明,利用遗传算法求解水泵机组优化组合的数学模型,对水泵机组进行优化调度,取得了良好的经济效益.     

基于多目标进化算法的供水系统优化运行研究

将多目标进化算法与启发式算法相接合,对供水管网微观模型进行优化调度研究.目标函数为供水系统的运行费用和维护费用最小化,以及水压服务水平的最大化(保证安全供水),以各泵站各型号水泵的开启和调速泵的转数比为决策变量,进行二进制-实数混合编码,并采用新型的交叉算子.运用NSGA-Ⅱ、epsilon-MOEA、SPEA2三种多目标进化方法求解优化运行模型,并通过工程算例进行比较.应用表明,多目标进化算法能为供水系统的优化决策提供支持.     

基于遗传算法的浸染生产排缸策略

针对目前印染企业在浸染生产过程中产品种类和加工设备多、调度复杂的特性，建立了一种用于浸染生产调度的数学模型，并应用遗传算法进行排缸调度求解。以生产任务的分配优先级顺序作为染色体的编码来求解多个生产任务在多个染缸上的调度分配命题，从而得出了最优排缸策略，适用于快速、高效地解决实际生产中大量生产任务调度问题。仿真结果表明了该策略的有效性和实用性。     

油田供水管网管道内径反演方法研究

在对油田供水管网进行系统仿真计算时候,其数学模型都涉及到管网中各管段的内径、粗糙度等参数,这类参数的选取直接影响管网平差计算的精度.在管网运行多年后,其上述参数大都和原始设计参数相差较大,由于实际生产过程中不允许也不可能对管网中的管道管径进行测量,导致管网仿真计算数学模型与实际不符,使得仿真计算失去本来意义.为解决此问题,提出了一种运用部分测试数据对管网管道内径进行反演求解的方法.从数学方面证明了其求解方法可行性,实际算例计算结果表明,运用该方法可以使管网平差计算结果与测试结果很好的吻合.     

煤炭码头卸车调度问题多目标优化模型及算法

本文针对煤炭码头卸车调度问题,提出了相应的多约束多目标优化模型,并设计了采用仿真推演策略解码的遗传算法求解。首先,本文考虑列车、煤种、场存、设备、翻堆线和卸车作业过程等约束条件,以卸车效率最大和列车在港时间最短为目标,构建了煤炭码头卸车调度问题多目标数学模型。然后,综合运筹学、遗传算法以及仿真技术,给出了煤炭码头卸车调度问题遗传算法详细设计,包括组合式编码和仿真推演解码方法,染色体生成算法,适应度函数设计,以及采用多种策略的遗传操作及修正等,并列出了算法步骤。实例测试表明,本算法的执行效率高而且优化效果好,结果适用。     

基于管网测压点的变频恒压供水优化方法研究

本文根据管网和水泵的运行曲线,阐明了供水系统的变频调速节能原理,研究了基于城市供水管网测压点的分时变频恒压供水优化方法与模型,并对该方法在某自来水公司的应用节能效果及经济效益进行了分析.     

多车场多车型车辆调度问题及其遗传算法

研究多车场多车型车辆调度问题,建立了一种基于最小配送费用的数学模型,模型的配送费用在考虑基本运输费的基础上又引入了司机的工资支出,包括基本工资和加班费.在多车场多车型车辆调度模型中,一辆车可以为多个客户服务,但一个客户只能由一辆车提供服务.根据模型的这些特点,提出了一种新的染色体混合编码方案和遗传操作策略,从而借助遗传算法成功实现了模型的求解.数值仿真结果验证了算法的可行性.     

基于虚拟变形法的供水管网漏损定位方法

针对供水管网的漏损探测与定位问题,采用虚拟变形法对管网各参数进行计算,通过漏损影响矩阵和流量影响矩阵来表征管网的状态,并提出了漏损探测定位的优化计算模型.由于管网漏损点数量事先难以预先获取,提出了基于贝叶斯理论的管网漏损探测、定位模型,并结合Matlab和Epanet编制了相应的优化计算程序,数值仿真结果验证了方法的有效性和可行性.     

基于遗传算法的车间作业计划仿真研究

将仿真技术和遗传算法相结合,根据生产车间的资源情况、优化目标等建立了生产调度仿真模型,然后对仿真输出结果进行统计,针对统计结果应用遗传算法对调度决策进行优化.仿真优化结果说明了该集成优化方法是有效性的.     

具有学习效应的置换流水车间调度问题布谷鸟搜索算法求解

对于以最小化最大完工时间为目标的置换流水车间调度问题,现有研究较少考虑学习效应对生产调度的影响,构建了具有学习效应的PFSP问题数学模型.采用ROV的编码方式,应用布谷鸟搜索算法进行离散优化问题求解.通过对Car类问题的大量仿真测试,表明了布谷鸟搜索算法求解该类问题的可行性和有效性.同时,证明了学习效应能够降低最大完工时间,从而提高生产效率.     

春小麦水分敏感指数与有限水量生育期的最优分配

通过不同的受旱处理，确定了Jensen模型中的春小麦水分敏感指数。介绍了运用生态规划模型实现有限水量生育期最优分配的方法与步骤，并以辽西地区春小麦为例，确定了不同初始含水量和生育期可利用灌溉水量下的最优分配决策。     

Two optimization models of the optimum inspection problem

This paper deals with establishing an optimum inspection schedule for systems that are subject to random failure and where the failure can be detected only through an inspection. The paper reviews ‘classical’ optimum checking policies. Two new optimization models are proposed to find the optimum sequence of inspection times. Theoretical analysis and numerical examples show that the optimum inspection time sequence derived from the proposed models is relatively accurate, robust, and computationally simple.     

Control limit policies for early detection of failure

This paper considers a class of Markov Decision problems subjects to partial observation. We develop a methodology for early detection of failure of systems that do not display symptoms of failure. We assume that the inspections used for detection are error prone and there is a fixed probability of not detecting the failure. The problem is studied as a non-stationary, finite horizon Markovian Decision Process with two states. We demonstrate that the optimal policy has a two region structure that intuition suggests. This characterization can be used to reduce the computational burden of finding the optimum inspection schedule. We also show that the form of the optimum policy remains unchanged for the unbounded horizon problem. Finally, we discuss a potential application of the model to medical diagnosis.     

Stock Control with Random Opportunities for Replenishment

In most stock control systems, stock is replenished by re-ordering either at regular intervals or upon depletion below an agreed level. Situations sometimes arise, however, in which replenishment tends to be governed by the incidence of external opportunities which occur at irregular intervals, usually as a result of a complex schedule of production or distribution. Conventional stock control procedures can then become difficult to operate effectively; this article discusses an alternative procedure which can be used in such circumstances, and shows how optimum control levels may be calculated.     

基于信息熵的配送系统扰动度量及应用研究

物流配送系统的不确定性主要是指难以预测及控制的作业状态,干扰计划执行的时效性和稳定性.本文结合信息论给出对配送系统复杂度的定义及不确定性状态描述,并应用信息熵理论建立静态和动态复杂度模型.将上述复杂性理论应用到配送调度,提出调度最大可行周期和调度计划执行稳定度两个评价配送调度时效性和稳定性的量化指标.最后,应用实例证明该理论和方法对配送系统的有效性.     

Use of goal programming and integer programming for water quality management—A case study of Gaza Strip

This paper describes a project dealing with achieving an optimum mix of water from different underground wells, each having different amounts of nitrates and chlorides. The amounts of chlorides and nitrates in each of the wells may be higher or lower than the World Health Organization (WHO) standards. Therefore, the optimum mix would be the one that meets WHO standard which is 250 mg/l for chlorides and 50 mg/l for nitrates. A goal programming model was developed to identify the combination of wells along with the amounts of water from each well that upon mixing would result in minimizing the deviation of the amounts of chlorides and nitrates from the standards set by WHO. The output of the goal programming model along with the coordinates of the wells identified above was then used for a second model that determines the locations of the mixing points “reservoirs” in such a way that minimizes the total weighted distances from the corresponding wells. Finally, an easy-to-use pumping schedule was developed using integer programming. Results indicate that for the case study, there exist several optima which make it easier for the decision maker to consider other intangible factors if there are any.     

An analytically derived cooling schedule for simulated annealing

We present an analytically derived cooling schedule for a simulated annealing algorithm applicable to both continuous and discrete global optimization problems. An adaptive search algorithm is used to model an idealized version of simulated annealing which is viewed as consisting of a series of Boltzmann distributed sample points. Our choice of cooling schedule ensures linearity in the expected number of sample points needed to become arbitrarily close to a global optimum.     

Machine reliability and preventive maintenance planning for cellular manufacturing systems

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.     

Properties of certain zero column-sum matrices with applications to the optimization of chemical reactors

This work is motivated by linear chemical reactor systems. The mathematical model of these systems employs a finite dimensional concentration vector which yields the properties of a discrete probability distribution. Central in the response of the system is a rate matrix. The properties of these matrices are analyzed in terms of the theories of Markoff and M-matrices. A linear objective function is selected and the optimization of a cascade system relative to changes of the sizes of the tanks is pursued. This amounts to the optimization of the objective function on R₊^m. The global optimum is shown to lie on the diagonal of the domain. Hence, the search for optimum can be simplified to a single dimension. Other related topics such as the effect of the number of tanks in the cascade on the optimum, conditions for off-diagonal stationary points and the constrained optimization are also considered.     

Human resources scheduling to improve the product quality according to exhaustion limit

The role of human resources in manufacturing systems is very significant, and without efficient human resources we encounter high-price products with low quality. To improve the efficiency of human resources, we need to provide an optimal working schedule for each worker in production period. In this paper, we proposed a mixed-integer nonlinear model to find the best working schedule based on product quality cost and workers reliability. In this model, if the worker’s exhaustion level reaches a specific limit, the worker can rest to increase his reliability level and an accommodator should work instead of him. Since the proposed model is NP-hard, we used an artificial immune system to provide the best working schedule. The results indicate that this model can provide efficient and effective human resources schedule in manufacturing systems.