Simultaneous determination of the optimal process mean and cutting speed for a manufacturing process

The relationship between machining speed and the process mean for a manufacturing process has received little attention. This paper presents an analytical model and its solution procedure to simultaneously determine the optimal machining speed and process mean when the objective is to minimize the total cost per part. The total cost of the model includes replacement (resetting), production and quality costs. A global optimization algorithm is used to solve the proposed unconstrained model. Numerical examples to demonstrate the effectiveness of the proposed model are given and an extensive sensitivity analysis of the proposed model parameters is also provided. It has been concluded that machining speed and process mean are highly related. Therefore, the machining conditions should be taken into account when solving process mean targeting problems.     

An application of the integrated AHP-PGP model for performance measurement of supply chain management

The performance measurement problem has gained great attention in business and operations management literature. The first objective of this study is to determine the required performance measures and to develop a model for performance evaluation, based on these selected measures using analytical hierarchy process (AHP) methodology. The second objective is to explain how an integrated AHP-PGP (pre-emptive goal programming) model can be used in performance measurement while optimising the overall performance. An integration of AHP and multi-objective PGP is used to consider both quantitative and qualitative performance measures in optimising the overall performance of the system. The integrated model is presented with a real-world application using source data provided by a survey conducted in India. Findings demonstrate that the integrated AHP-PGP model can be useful to all supply chain industries in their day-to-day performance measurement decisions.     

炼钢连铸生产调度问题的两阶段遗传算法

将炼钢连铸生产过程抽象为混合流水车间,建立了0-1型混合整数线性规划调度模型。模型将严格连续浇注作为等式约束,并通过分段惩罚来平衡炉次的驻留时间。在对模型进行Benders分解的基础上,提出了将GA与LP结合的两阶段遗传算法。在算法设计中,提出了一种新的染色体编码来表示炉次设备指派与排序方案,给出了相应的遗传操作方法。算法的第一阶段通过最小化设备析取冲突来寻找高质量的种群,第二阶段通过求解线性规划模型来指导遗传算法的迭代过程。基于生产实际数据的仿真实验表明,该算法能够有效求解炼钢连铸生产调度问题。     

多功能机床环境下的Job Shop问题研究

本文引入工序机的概念描述加工系统的资源,建立了面向多功能加工机床的Job Shop作业计划模型,用遗传算法对所建的模型进行优化。在遗传算法优化搜索的基础上,利用工件、工序机和实际机床之间的动态调度使作业计划更趋合理。最后给出数值试验结果。     

A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem

We develop a new genetic algorithm to solve an integrated Equipment-Workforce-Service Planning problem, which features extremely large scales and complex constraints. Compared with the canonical genetic algorithm, the new algorithm is innovative in four respects: (1) The new algorithm addresses epistasis of genes by decomposing the problem variables into evolutionary variables, which evolve with the genetic operators, and the optimization variables, which are derived by solving corresponding optimization problems. (2) The new algorithm introduces the concept of Capacity Threshold and calculates the Set of Efficient and Valid Equipment Assignments to preclude unpromising solution spaces, which allows the algorithm to search much narrowed but promising solution spaces in a more efficient way. (3) The new algorithm modifies the traditional genetic crossover and mutation operators to incorporate the gene dependency in the evolutionary procedure. (4) The new algorithm proposes a new genetic operator, self-evolution, to simulate the growth procedure of an individual in nature and use it for guided improvements of individuals. The new genetic algorithm design is proven very effective and robust in various numerical tests, compared to the integer programming algorithm and the canonical genetic algorithm. When the integer programming algorithm is unable to solve the large-scale problem instances or cannot provide good solutions in acceptable times, and the canonical genetic algorithm is incapable of handling the complex constraints of these instances, the new genetic algorithm obtains the optimal or close-to-optimal solutions within seconds for instances as large as 84 million integer variables and 82 thousand constraints.     

Scenario aggregation in single-resource production planning models with uncertain demand

In this paper we present an application of the scenario aggregation approach proposed by Rockafellar and Wets to a simple standard multi-product multi-period production planning problem with uncertain demand and setup cost modelled by logical zero-one variables. The uncertainty in demand is expressed by a number of demand scenarios. As compared with more traditional approaches that require distributional assumptions and/or estimates of parameters from historical demand data, the scenario approach offers greater flexibility and makes it possible to take subjective information into account. The scenario aggregation principle and the corresponding progressive hedging algorithm offer a theoretically sound basis for generating consistent solutions for production planning models with uncertain demand. Since the production planning problem studied in this paper is of mixed-integer type the original scenario aggregation approach cannot be applied directly. However, since the integer variables in the production planning model are indirectly coupled to the continuous production decisions an alternative method in which only the production quantities are used to couple the different realizations can be used. This paper is a first attempt to perform this form of coupling. We illustrate the ideas on a small example and use this example to demonstrate how the solution can be evaluated in terms of flexibility measures.     

Estimating Dynamic Models of Imperfect Competition

We describe a two‐step algorithm for estimating dynamic games under the assumption that behavior is consistent with Markov perfect equilibrium. In the first step, the policy functions and the law of motion for the state variables are estimated. In the second step, the remaining structural parameters are estimated using the optimality conditions for equilibrium. The second step estimator is a simple simulated minimum distance estimator. The algorithm applies to a broad class of models, including industry competition models with both discrete and continuous controls such as the Ericson and Pakes (1995) model. We test the algorithm on a class of dynamic discrete choice models with normally distributed errors and a class of dynamic oligopoly models similar to that of Pakes and McGuire (1994).     

含有连续型随机参数的回收物流网络优化设计

考虑到设计参数的不确定性,建立了单一产品、有处理能力约束的回收物流网络优化设计的二阶段随机规划模型.该模型借助于抽样技术给出了连续型随机参数的有限离散数值,利用混合遗传算法计算并比较了不同网络的建设和运营费用,从而避免了网络设施数量和随机向量维数对模型求解效率的影响.为得到稳健的回收物流网络,利用大样本对计算获得的可行网络进行了评价.考虑到样本随机性的影响,给出了基于随机模型的回收物流网络优化设计步骤.另外,通过算例说明了随机模型的有效性,证实了确定性模型近似处理随机规划问题的不适用性.     

基于持续运营机会约束的竞争设施点选址研究——一种有效的实数编码遗传求解算法

竞争设施点选址是空间经济、区域发展、组合优化和系统工程的重要课题之一。本文以市场份额最大化为目标,研究了基于持续运营机会约束的竞争设施点选址问题,并给出了一种有效的实数编码遗传求解算法。在求解模型方面,首先假定运营成本是竞争设施点规模大小的函数,并对设施点持续运营概率进行机会约束,借鉴引力模型建立竞争设施点选址-设计问题的非线性混合整数规划模型。其次,考虑到选址变量和规模变量的数值类型,以及编码变换问题,设计了一种实数编码遗传求解算法。通过数值实验表明,对不同规模问题的实际计算结果,该算法可以在较短时间内获得最优解,可行解和精确解之间误差小于0.5%,相关比较分析也讨论了该算法的优越性和实用性,为竞争设施点选址问题的研究提供了不同的视角和实用求解算法。     

考虑异质性行为的灾后应急物资动态调度优化

在以人道主义为本的应急救援活动中，不可忽视被救灾民和施救决策者们异质性行为的影响。一方面，将各灾民因救援物资需求未被及时满足而呈现的差异化心理痛苦度量成经济损失，并纳入应急救援调度的社会成本这一决策目标中；另一方面，在灾后物资调度决策中，关注具有异质性偏好的各应急决策者所展现出的不同救援态度。结合面向联合机会约束规划的动态供需平衡限制，最终构建一个考虑灾民和决策者们异质性行为的多阶段灾后救援物资分配和应急路径优化模型。采用2008年汶川地震为案例背景，应用遗传算法对模型求解和参数分析，将仿真结果与不考虑异质性行为的传统救援调度方案进行比较，得出一些结论为构建高效的应急救援体系提供有益参考。     

基于多种不确定性参数分布的U-GERT网络模型及其应用研究

针对随机网络研究中活动参数的分布是不确定信息的情况,本文提出了不确定信息的GERT网络模型,并利用信号流图原理研究了不确定信息的GERT网络仿真算法;研究了不确定变量GERT网络的矩母函数的构造,并对其重要的性质做了详细的探讨;最后通过算例仿真,验证本文提出的不确定信息的GERT网络模型及其仿真算法能够有效的解决现实生活中不确定信息对GERT网络参数的影响,说明模型与算法正确可行。     

改进粒子群优化算法在电源规划中的应用

电源规划是一类复杂、非线性组合优化问题.传统的方法随着规划期的延长,考虑因素的增多,难以有效的进行优化,在实际应用中作用有限.首先,对电源规划优化问题进行了建模.然后,对于粒子群(PSO)的迭代策略进行改进,在此基础上,运用遗传粒子群(GPHA)混合优化算法进行了优化尝试.考虑到电源规划中相关参数众多,在优化过程中引入了虚拟变量对电源规划中的问题进行了简化描述;GHPA算法的适应度评价函数设计中,运用了罚函数的思想,以提高算法优化的效果.最后本文使用某省实际负荷预测和系统负荷实际数据,进行了电源规划方案优化,得到了优化后的电源规划方案,并与普通的遗传算法、粒子群算法以及传统的动态规划算法得到的结果进行了比较.比较的结果显示出了本文提出的算法在优化结果和速度方面具有明显效果.     

Stochastically Modeling Listeria Monocytogenes Growth in Farm Tank Milk

This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.     

SPECIFICATION AND ESTIMATION OF LOGICALLY CONSISTENT LINEAR MODELS

The implications of constrained dependent and independent variables for model parameters are examined. In the context of linear model systems, it is shown that polyhedral constraints on the dependent variables will hold over the domain of the independent variables when a set of polyhedral constraints is satisfied by the model parameters. This result may be used in parameter estimation, in which case all predicted values of the dependent variables are consistent with constraints on the actual values. Also, the implicit constraints that define the set of parameters for many commonly used linear stochastic models with an error term yield values of the dependent variables consistent with the explicit constraints. Models possessing these properties are termed “logically consistent”.     

GLOBAL JOB SHOP SCHEDULING WITH A GENETIC ALGORITHM

This paper describes a global job shop scheduling procedure that uses a genetic algorithm to find a good schedule. Unlike previously considered algorithms, this procedure has been implemented in the scheduling system for a manufacturing facility and has led to improved scheduling. This facility is a semiconductor test area. The test area is a job shop and has sequence-dependent setup times at some operations. The concern of management is to meet their customer due dates and to increase throughput. This requires the coordination of many resources, a task beyond the ability of simple dispatching rules. We discuss a centralized procedure that can find a good schedule through the use of a detailed scheduling model and a genetic algorithm that searches over combinations of dispatching rules. We discuss our effort in developing a system that models the shop, creates schedules for the test area personnel, and makes a number of contributions to test area management.     

分销网络优化模型及算法研究

为了优化网络结构,寻求最佳配送策略,最终找出成本最小的供应链,针对需求拖动式供应链中,多供应商、多产品、多客户分销配送网络的优化设计问题,在考虑需求分配的情况下,提出了分销配送网络的优化模型。为了求解优化模型,提出了基于混合遗传算法求解混合0-1整数规划问题的算法,它是用遗传算法搜索0-1变量的最优解,将其他算法融入遗传算法中,对非0-1变量进行求解的一种算法。最后通过两个算例进行了仿真实验,初步验证了优化模型和算法的有效性。     

A practical model for last train rescheduling with train delay in urban railway transit networks

Last train timetable rescheduling aims at coordinating the arrival and departure times of feeder trains with connecting trains at transfer stations to eliminate the effect of unexpected incidents in train operations. It has become a challenging problem in the operations and management of urban railway transit networks because of high complexities in coordination among lines. In this paper, we propose a rescheduling model for last trains with the consideration of train delays caused by incidents that occurred in train operations. In the model, two aspects are considered. On one hand, we try to minimize the running time and the dwell time, and to maximize the average transfer redundant time and the network accessibility. On the other hand, we expect to minimize the difference between the original timetable and the rescheduled one. A genetic algorithm is developed to solve this problem. The case study of Beijing railway transit network shows that once a delay occurs in a section, the most effective way to adjust the timetable consists of adjusting the running time of trains that have strong transfer relationships with the delay section. If the delay is not substantially long, the suggested model would neutralize the influence of the delay.     

Consistent Moment Selection Procedures for Generalized Method of Moments Estimation

This paper considers a generalized method of moments (GMM) estimation problem in which one has a vector of moment conditions, some of which are correct and some incorrect. The paper introduces several procedures for consistently selecting the correct moment conditions. The procedures also can consistently determine whether there is a sufficient number of correct moment conditions to identify the unknown parameters of interest. The paper specifies moment selection criteria that are GMM analogues of the widely used BIC and AIC model selection criteria. (The latter is not consistent.) The paper also considers downward and upward testing procedures. All of the moment selection procedures discussed in this paper are based on the minimized values of the GMM criterion function for different vectors of moment conditions. The procedures are applicable in time-series and cross-sectional contexts. Application of the results of the paper to instrumental variables estimation problems yields consistent procedures for selecting instrumental variables.     

具有遗憾值约束的鲁棒供应链网络设计模型研究

考虑不确定性环境,研究战略层次的供应链网络鲁棒设计问题,目标是设计参数发生摄动时,供应链性能能够保持稳健性。基于鲁棒解的定义,建立从上游供应商选择到下游设施选址-需求分配的供应链网络设计鲁棒优化模型;提出确定遗憾值限定系数上限和下限的方法,允许决策者调节鲁棒水平,选择多种供应链网络结构;通过模型分解与协调,设计了供应链节点配置的禁忌搜索算法。算例的计算结果表明了禁忌搜索算法具有良好的收敛特性,以及在处理大规模问题上的优越性;同时也反映了利用鲁棒优化模型进行供应链网络设计,可以有效规避投资风险。