Quality improvement and backorder price discount under controllable lead time in an inventory model

The proposed study investigates a continuous review inventory model with order quantity, reorder point, backorder price discount, process quality, and lead time as decision variables. An investment function is used to improve the process quality. Two models are developed based on the probability distribution of lead time demand. The lead time demand follows a normal distribution in the first model and in the second model it does not follow any specific distribution but mean and standard deviation are known. We prove two lemmas to obtain optimal solutions for the normal distribution model and distribution free model. Finally, some numerical examples are given to illustrate the model.     

基于双重权约束期望改进策略的多目标并行代理优化方法

针对多目标仿真优化的高昂成本及黑箱函数难以获取问题,提出基于双重权约束期望改进策略的多目标并行代理优化方法.首先,建立Kriging模型获取未试验点的预测不确定性;其次,构建双重权约束期望改进策略,并利用填充策略矩阵及距离聚合方法实现新改进策略的聚合;然后,最大化聚合双重权约束期望改进策略实现多目标并行优化;最后,达到终止条件,获得Pareto最优解集.选取测试函数及铰接夹芯梁设计案例进行优化验证.验证对比结果表明:所提方法可有效提升多目标问题优化效率,减少昂贵仿真成本;与同类方法相比,低维问题中获取Pareto最优解集的收敛性、多样性及分布性更优.     

Optimal production run length and capital investment in quality improvement with an imperfect production process

The paper studies the effects of an imperfect production process on the optimal production run length when capital investment in process quality improvement is adopted. The mathematical model is derived to determine the optimal production run length and capital investment under the assumption of logarithmic investment function such that the total annual cost is minimized. In addition, an efficient algorithm is provided to find both the optimal production run length and the optimal process quality. Therefore, the optimal lot sizing and capital investment are appropriately determined. A numerical example is provided to illustrate the results and to assess the cost savings realized by adopting capital investment. Furthermore, some managerial implications are also included.     

Process sequence optimization based on a new cost–tolerance model

Identification of the optimal production sequence and allocation of machining tolerance are important activities in process planning for reducing production cost. This paper presents an approach that is capable of determining the optimal production sequence and its optimal process tolerance to achieve the minimum production costs. A new mathematical model, representing the cost–tolerance relationships and showing significant accuracy improvement over existing models, is introduced to allocate process tolerance. Stock allowance constraints on the selected switching tolerance are also considered to yield practical solutions. A prototype system for hole-making components is used to verify and evaluate the effectiveness of the new approach.     

Real-time energy management for electric arc furnace operation

Electric arc furnaces are used extensively in the steel industry for steel production. Development of energy savings strategies for the highly energy-intensive batch process is extremely challenging due to the complexity of the process and lack of measurements due to the harsh operating conditions. Here we introduce a new energy management approach that effectively curtails the energy cost in real-time through the implementation of economically optimal operating decisions. An economics- oriented shrinking horizon nonlinear model predictive control (NMPC) algorithm that exploits time-varying electricity prices is coupled with a multi-rate moving horizon estimator (MHE) to form an integrated decision- making framework. With a detailed first-principles dynamic model functioning at the core, the multi-variable interactions and plant variations are successfully incorporated into the control strategy to achieve reliable performance. We also present a novel initialization scheme for obtaining fast on-line solutions of the economic NMPC and multi-rate MHE dynamic optimization problems. Using this initialization algorithm, we show that the optimal input decisions are obtained with sufficient computational speed for real-time implementation. The energy usage optimization results indicate a significant reduction in the operating cost and peak electricity demand compared to the case where the electricity price profile is not updated.     

一种解决蕴含不确定性信息的氧化铝配料问题的智能优化方法

针对原料质量不稳定和成分检测大滞后带来的信息不确定性,提出了一种两级智能优化方法实现氧化铝配料过程中生料浆质量的优化控制.该方法通过引入中间优化同标,将优化问题分解为原料配比优化和料浆调配优化,逐步弱化不确定信息对生料浆质量的影响.配比优化基于入槽生料浆质量预测模型,设计了专家分级推理机制,实现多质量指标约束条件下的配比优化设定:调配优化将不确定的生料浆质量信息引入调配优化模型约束中,采用改进遗传算法求解最优渊配方案,配制高质量的生料浆.将提出的方法应用于国内某厂氧化铝配料过程,实现了生料浆质量指标的优化控制,简化了工艺流程,为存在信息不确定的长流程工业过程的优化控制提供了范例.     

一种改进的蚁群算法在工艺规划与车间调度集成优化中的应用

改进标准蚁群算法的执行策略,可提高工艺规划和调度集成问题的求解 质量和效率。通过节点集、有向弧/无向弧集、AND/OR 关系,建立了基于AND/OR 图的工 艺规划和调度集成优化模型。提出一种求解工艺规划与车间调度集成问题的改进蚁群优化算 法,采用了信息素动态更新策略避免收敛过慢和局部收敛,利用多目标优化策略提高求解质 量。仿真结果证明了该算法的有效性。     

一种配电网投资项目的综合评价与优选排序方法

配网投资项目具有项目众多、规模相对小、项目繁杂、时间紧迫等配电网建设和运维自身的特点,在投资需求远大于投资能力的情况,基于项目综合评价实现优选排序是实现配电网精准投资的关键手段.为此,提出了一种计及指标约束同时兼顾投资效益的中压配电网项目优选方法.以配电网投资效果为指标构建评价体系,并利用建设目标对项目属性进行分类,基于灰色关联度法深入挖掘项目属性与评价指标间的关联关系,探明指标改善所对应的投资概算.为了同时兼顾规划指标与投资成效,提出了计及指标约束的配网项目两阶段优选排序法,将优选过程分为针对关键规划指标的项目优选和针对普通指标的项目优选两个阶段.通过对某地市电力公司中压配电网项目进行优选评估,验证了所提方法的有效性与合理性.     

An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost

The single-vendor single-buyer integrated production inventory system has been an object of study for a long time, but little is known about the effect of investing in reducing ordering cost on the integrated inventory models with backorder price discount and variable lead time. The purpose of this article is to investigate in the continuous review model with backorder price discount and variable lead time to effectively increase investment and to reduce the joint expected annual total cost. The integrated strategy discussed here is one in which the buyer orders a quantity, then the vendor produces n times order quantity in each production cycle, in order to reduce setup cost. In addition, the buyer offers backorder price discounts to the customers that may motivate the customers’ desire for backorders, and buyer ordering cost can be reduced through effective investment. An integrated inventory model is established to find the optimal solutions of order quantity, ordering cost, backorder price discount, lead time, and the number of shipments from the vendor to the buyer in one production run, so that the joint expected annual total cost incurred has the minimum value. Furthermore, numerical examples are used to demonstrate the benefits of the model.     

数据网格中基于MAS的复制管理研究

数据网格是网格环境下的一种数据管理和存储架构,通常使用数据复制技术来获得更好的数据访问效率和容错性能,提出了一种基于MAS的复制管理模型,解决数据网格中数据高度自治和动态带来的管理难题,探讨了基于MAS的实现架构,给出了Agent的结构和协作过程,将复制管理和复制优化策略封装于Agent智能模块中,结合实际应用使用Optorsim仿真器对模型和复制优化策略进行分析,并对经济模型的基于二项分布的估价函数进行了改进,仿真结果表明模型能够提供高效的复制管理服务.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

竖炉焙烧过程综合自动化系统

针对竖炉焙烧过程的工艺特点及技术要求，基于智能技术提出了实现综合生产指标优化的竖炉焙烧过程综合自动化系统，讨论了由智能优化、过程控制和过程管理三层结构组成的综合自动化系统的结构、功能和控制策略。用智能优化设定模型、炉况诊断模型、智能预报模型及回路控制，实现了优化综合生产指标的目标。所提出的系统成功应用于桌选矿厂竖炉焙烧生产过程，实现了竖炉焙烧生产过程的优化控制、优化运行和优化管理，取得了明显的应用成效。     

A neural network applied to estimate process capability of non-normal processes

It is always crucial to estimate process capability index (PCI) when the quality characteristic does not follow normal distribution, however skewed distributions come about in many processes. The classical method to estimate process capability is not applicable for non-normal processes. In the existing methods for non-normal processes, probability density function (pdf) of the process or an estimate of it is required. Estimating pdf of the process is a hard work and resulted PCI by estimated pdf may be far from real value of it. In this paper an artificial neural network is proposed to estimate PCI for right skewed distributions without appeal to pdf of the process. The proposed neural network estimates PCI using skewness, kurtosis and upper specification limit as input variables. Performance of proposed method is validated by simulation study for different non-normal distributions. Finally, a case study using the actual data from a manufacturing process is presented.     

Fuzzy opportunity cost for EOQ model with quality improvement investment

The cost of capital (i.e. opportunity cost) is one of the key factors that will influence the inventory and investment decisions. Previously, the classical EOQ model has been extended to include an imperfect production process and quality improvement investment, where the opportunity cost rate (interest rate or discounted rate) for evaluating the cost of capital investment is known with certainty. However, in some practical situations, the opportunity cost rate probably incurs disturbance due to the unstable environments. To capture this reality, this paper attempts to combine the statistical technique and fuzzy sets concept to deal with the unstable opportunity cost rate, so as to modify the aforementioned inventory/investment model. We derive the optimal lot size and the optimal process quality level in the fuzzy sense utilizing the logarithmic investment cost function. A numerical example is provided, and the results are compared with those obtained from a crisp opportunity cost rate model.     

基于虚拟化技术的仿真系统容错优化方法

节点崩溃或者仿真资源不足导致的分布式仿真系统故障,降低了仿真系统可靠性。为保证系统容错效果,降低容错开销,提出了一种基于虚拟化技术的仿真系统容错方法,按照系统故障发生的位置,对不同类型故障动态采用不同类型的容错策略。分析了检查点容错策略的优化方法,给出了最优设置间隔;结合虚拟化技术的优势,解决了副本容错策略的节点选择、副本数量以及位置分布问题;同时,引入基于虚拟机迁移的容错策略,并将其作为检查点容错策略和副本容错策略的补充,以降低容错开销。通过仿真实验数据对比,分析了动态容错策略与普通容错策略的性能,可知动态容错策略保证了系统容错性能,容错开销也保持在较低水平。     

基于专家知识的字典序优化决策方法及工业应用

为有效实现有色冶金配料过程的实时优化,首先根据氧化铝配料过程特点,建立一种字典序区间目标规划模型;然后通过整理长期积累的专家经验知识,构建了具有优先级的分类知识库;接着基于专家知识库的分类结构,提出一种字典序专家推理策略,以实现区间约束条件下的多质量指标的优化控制．工业应用结果表明,所提出的方法能够很好地实现生料浆质量的优化控制,为其他有色冶金工业配料过程的控制提供了一种优化模式．     

Stochastic optimization for real-time operation of alumina blending process

In this paper, the stochastic optimization blending operation is applied to the alumina production in this paper. A new binomial distribution based stochastic scenario optimization used together with the sample selection approach is utilized to design the optimal set point for control, under which the probability of quality indices of the raw slurry being within the tolerance region is high enough in the presence of uncertainties caused by fluctuation of the raw material and disturbances. Through practical industrial experiments, it is observed that the proposed stochastic optimization method is effective and the computational cost is low.     

A Tax Integrated Approach for Global Supply Chain Network Planning

Globalization is here to stay. Companies source, manufacture, and sell across borders. There are several destinations available for undertaking these activities offering varying degrees of incentives and at each destination the company incurs a different delivery cost. Multinational companies need to make more realistic decisions about where to make, source, locate, move, and store products to minimize the total cost of delivery keeping in mind the incentives offered by the governments and the logistics costs at and from the location. Current literature on supply chain optimization does not emphasize on tax. To attract foreign investment, many developing economies have included tax-holidays in their export-import (EXIM) policy for companies operating in free trade zones (FTZs). In this paper, we propose a tax integrated mixed integer model, for optimally deciding the foreign direct investment (FDI)-outsourcing (the choice of establishing captive production centers versus complete outsourcing) alternatives at the various stages of a global supply chain. For a general acyclic supply chain, this decision problem is NP-hard and obtaining analytical results on optimal FDI-outsourcing strategy may be difficult. We linearize the tax integrated model by introducing exactly one hub at each stage. In this case, termed hub-based sourcing-single hub case, we prove that the greedy strategy is an optimal FDI-outsourcing strategy. However, by associating multiple hubs at each stage the decision problem remains NP-hard. Finally, we empirically analyze the tax integrated model (for the general case) on a use-case scenario in which some locations in the choice have free trade zones offering tax incentives.     

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Collaborative two-echelon logistics joint distribution network can be organized through a negotiation process via logistics service providers or participants existing in the logistics system, which can effectively reduce the crisscross transportation phenomenon and improve the efficiency of the urban freight transportation system. This study establishes a linear optimization model to minimize the total cost of two-echelon logistics joint distribution network. An improved ant colony optimization algorithm integrated with genetic algorithm is presented to serve customer clustering units and resolve the model formulation by assigning logistics facilities. A two-dimensional colony encoding method is adopted to generate the initial ant colonies. Improved ant colony optimization combines the merits of ant colony optimization algorithm and genetic algorithm with both global and local search capabilities. Finally, an improved Shapley value model based on cooperative game theory and a cooperative mechanism strategy are presented to obtain the optimal profit allocation scheme and sequential coalitions respectively in two-echelon logistics joint distribution network. An empirical study in Guiyang City, China, reveals that the improved ant colony optimization algorithm is superior to the other three methods in terms of the total cost. The improved Shapley value model and monotonic path selection strategy are applied to calculate the best sequential coalition selection strategy. The proposed cooperation and profit allocation approaches provide an effective paradigm for logistics companies to share benefit, achieve win–win situations through the horizontal cooperation, and improve the negotiation power for logistics network optimization.     

Optimal Control of the Investment Portfolio with Respect to the Quantile Criterion

A two-step problem is considered for the optimal portfolio investment management (control) involving two kinds of securities with respect to the quantile criterion under the assumption of the uniform distribution of the return. The problem with the quantile criterion reduces to optimization of a probability functional, and for the analytical synthesis of an optimal strategy, use is made of a method of dynamic programming. The effectiveness of the suggested strategy in comparison with other known strategies of portfolio control is illustrated by an example.