Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning

This paper addresses a multi-period, multi-product sawmill production planning problem where the yields of processes are random variables due to non-homogeneous quality of raw materials (logs). In order to determine the production plans with robust customer service level, robust optimization approach is applied. Two robust optimization models with different variability measures are proposed, which can be selected based on the tradeoff between the expected backorder/inventory cost and the decision maker risk aversion level about the variability of customer service level. The implementation results of the proposed approach for a realistic-scale sawmill example highlights the significance of using robust optimization in generating more robust production plans in the uncertain environments compared with stochastic programming.     

考虑交货期的生产运输联合优化问题研究

结合企业实际场景研究了考虑交货期的多个工厂、多条生产线、单一产品的生产与运输联合优化问题.已知客户订单需求量和交货时间窗,考虑了各条生产线在不同时段的生产能力约束,在满足交货时间窗约束的前提下,以生产、存储、运输费用之和极小化为目标建立了生产与运输联合优化问题的混合整数规划模型,通过分析模型结构证明了在不考虑固定生产成...     

A heuristic approach for capacity expansion of packet networks

We address the problem of expanding transmission capacity of an existing packet network over a multiperiod planning horizon, the objective being low total cost of expansion. Discrete capacity choices, interaction with routing decisions, and economy of scale in the cost of capacity make it extremely difficult to decide when, where and how much capacity to add. A fast heuristic solution method is developed based on the well established Flow Deviation routing algorithm. The heuristic begins by making myopic expansion decisions, which are then subsequently adjusted to account for economies of scale in the cost of capacity. Heuristic solutions are compared to a benchmark which approximates the real cost function by its linear lower envelope. Since the number of possible expansion plans is an exponential function of the number of edges, capacity choices, and periods in the planning horizon, a fast heuristic allows one to look beyond small problems at more realistically sized ones.     

安全质量费用工期在网络计划中的系统优化研究

施工网络计划中安全、质量、费用、工期达到综合优化是工程管理决策追求的目标,在工程项目管理中对这4个指标进行全面系统分析,并且进行综合均衡优化,对于提高工程项目的综合效益至关重要.采取在网络计划所有可行的施工方案中寻求最优的方案组合,从而达到总目标的优化.并且通过一个实例来说明本方法的应用.     

Bayesian single and double variable sampling plans for the Weibull distribution with censoring

The sampling inspection problem is one of the main research topics in quality control. In this paper, we employ Bayesian decision theory to study single and double variable sampling plans, for the Weibull distribution, with Type II censoring. A general loss function which includes the sampling cost, the time-consuming cost, the salvage value, and the after-sales cost is proposed to determine the Bayes risk and the corresponding optimal sampling plan. Explicit expressions for the Bayes risks for both single and double sampling plans are derived, respectively. Numerical examples are given to illustrate the effectiveness of the proposed method. Comparisons between single and double sampling plans are made, and sensitivity analysis is performed.     

钢厂生产的模糊作业时间及其在管理中的应用

结合抚顺钢厂的实际,利用模糊数学理论对生产实际中的模糊作业时间进行处理,将模糊作业时间转换为非模糊精确问题,利用禁忌搜索智能优化方法对问题进行求解,使调度计划具有一定的柔性,让管理者能够掌握调度时间范围,从而使计划调度更接近现实,大大缩短了总完工时间,对于钢厂一体化管理,使连铸生产的高温铸坯能够在允许时间范围内到达热轧厂,降低了能源消耗,缩短了生产周期。     

A model of R&D valuation and the design of research incentives

We develop a real options model of R&D valuation that takes into account the uncertainty in the quality (or efficacy) of the research output, the time and cost to completion, and the market demand for the R&D output. The model is then applied to study the problem of pharmaceutical under-investment in R&D for vaccines to treat diseases affecting the developing regions of the world. To address this issue, world organizations and private foundations are willing to sponsor vaccine R&D, but there is no consensus on how to administer the sponsorship effectively. Different research incentive contracts are examined using our valuation model. Their effectiveness is measured in the following five dimensions: expected cost to the sponsor, probability of development success, consumer surplus generated, expected number of successful vaccinations and expected cost per person successfully vaccinated. We find that, in general, purchase commitment plans (pull subsidies) are more effective than cost subsidy plans (push subsidies). Moreover, we find that a hybrid subsidy plan constructed from a purchase commitment combined with a sponsor research cost-sharing subsidy is the most effective.     

A model of R&D valuation and the design of research incentives

We develop a real options model of R&D valuation that takes into account the uncertainty in the quality (or efficacy) of the research output, the time and cost to completion, and the market demand for the R&D output. The model is then applied to study the problem of pharmaceutical under-investment in R&D for vaccines to treat diseases affecting the developing regions of the world. To address this issue, world organizations and private foundations are willing to sponsor vaccine R&D, but there is no consensus on how to administer the sponsorship effectively. Different research incentive contracts are examined using our valuation model. Their effectiveness is measured in the following five dimensions: expected cost to the sponsor, probability of development success, consumer surplus generated, expected number of successful vaccinations and expected cost per person successfully vaccinated. We find that, in general, purchase commitment plans (pull subsidies) are more effective than cost subsidy plans (push subsidies). Moreover, we find that a hybrid subsidy plan constructed from a purchase commitment combined with a sponsor research cost-sharing subsidy is the most effective.     

Decision-theoretic troubleshooting: Hardness of approximation

Decision-theoretic troubleshooting is one of the areas to which Bayesian networks can be applied. Given a probabilistic model of a malfunctioning man-made device, the task is to construct a repair strategy with minimal expected cost. The problem has received considerable attention over the past two decades. Efficient solution algorithms have been found for simple cases, whereas other variants have been proven NP-complete. We study several variants of the problem found in literature, and prove that computing approximate troubleshooting strategies is NP-hard. In the proofs, we exploit a close connection to set-covering problems.     

Vendor managed inventory for environments with stochastic product usage

We develop technology to plan delivery routes for the supply of blood products to hospitals by a blood bank. The technology produces low cost, robust plans that hedge against the natural uncertainty associated with blood product usage at hospitals. The technology relies on sampling-based approaches involving integer programming and variable neighborhood search. An extensive computational study shows the efficacy of the two approaches and highlights the impact of product usage uncertainty on the resulting delivery plans.     

Determining Economic Maintenance Frequency for a Family of Machines

This paper deals with the problem of determining economic maintenance frequency for a group of machines. The operating cost for each machine is assumed to increase with time since the last maintenance work carried out on the machine. The cost of carrying out maintenance work is known and assumed constant. A fixed cost is incurred whenever maintenance work is carried out, and this fixed cost is assumed to be independent of the number of machines on which maintenance work is carried out. The total cost of the system is given by the sum of maintenance and operating costs for all the machines. A heuristic method is proposed for determining the economic maintenance frequency of each machine. An example is given to illustrate the method.     

An efficient sampling method for stochastic inverse problems

A general framework is developed to treat inverse problems with parameters that are random fields. It involves a sampling method that exploits the sensitivity derivatives of the control variable with respect to the random parameters. As the sensitivity derivatives are computed only at the mean values of the relevant parameters, the related extra cost of the present method is a fraction of the total cost of the Monte Carlo method. The effectiveness of the method is demonstrated on an example problem governed by the Burgers equation with random viscosity. It is specifically shown that this method is two orders of magnitude more efficient compared to the conventional Monte Carlo method. In other words, for a given number of samples, the present method yields two orders of magnitude higher accuracy than its conventional counterpart.     

A simple method to compute economic order quantities

In this note, a simple method by using the arithmetic–geometric-mean-inequality theorem is proposed to computer the global minimum economic order quantities without taking complex differential calculus or using tedious algebraic manipulations. In contrast to (Minner, S., 2007. A note on how to compute economic order quantity without derivatives by cost comparisons. International Journal of Production Economics 105, 293–296; Wee, H.M., Wang, W.T., Chung, C.J., 2009. A modified method to computer economic order quantities without derivatives by cost-difference comparisons. European Journal of Operational Research) based on a local cost minimum initially to derive the solution and then proven it’s the global minimum, the proposed method yields the global minimum cost immediately and explicitly without using the cost comparisons and letting the time horizon to infinity.     

SAMPLING INSPECTION OF RELIABILITY IN （LOG）NORMAL CASE WITH TYPE I CENSORING

This article proposes a statistical method for working out reliability sampling plans under Type I censored sample for items whose failure times have either normal or lognormal distributions. The quality statistic is a method of moments estimator of a monotonous function of the unreliability. An approach of choosing a truncation time is recommended. The sample size and acceptability constant are approximately determined by using the Cornish-Fisher expansion for quantiles of distribution. Simulation results show that the method given in this article is feasible.     

诊断和修理航空继电器故障的优化系统

为了减少目前航空继电器用于发现和修理故障的费用 ,本文建立了基于规则、事实以及其自身经验的一个专家系统——适应性诊断系统 ADS( A daptive Diagnostic System) .该系统利用推理对航空继电器检测和替换顺序进行了优化 ,使节省的费用大约为 50 % .     

An Efficient Monte Carlo Method for Optimal Control Problems with Uncertainty

A general framework is proposed for what we call the sensitivity derivative Monte Carlo (SDMC) solution of optimal control problems with a stochastic parameter. This method employs the residual in the first-order Taylor series expansion of the cost functional in terms of the stochastic parameter rather than the cost functional itself. A rigorous estimate is derived for the variance of the residual, and it is verified by numerical experiments involving the generalized steady-state Burgers equation with a stochastic coefficient of viscosity. Specifically, the numerical results show that for a given number of samples, the present method yields an order of magnitude higher accuracy than a conventional Monte Carlo method. In other words, the proposed variance reduction method based on sensitivity derivatives is shown to accelerate convergence of the Monte Carlo method. As the sensitivity derivatives are computed only at the mean values of the relevant parameters, the related extra cost of the proposed method is a fraction of the total time of the Monte Carlo method.     

Exploration of pension funding in case of exact vesting

A pension plan is said to be exactly vested if it provides in addition to the benefit available upon retirement, a benefit, upon termination for any cause prior to retirement, which is exactly equivalent to the actuarial accured liability for the terminating participant.The concept of exact vesting has simple application in defined contribution plans such as those of the Teachers Insurance and Annuity Association. It is also feasible to develop the exact vesting concept for a defined benefit plan which uses an individual type of actuarial cost method. An exactly vested plan would have more individual equity than is available under customary vesting and early retirement provisions of defined benefit plans.In this paper, theory is developed for an exactly vested model plan in parallel to the theory for a pure pension model plan discussed in previous papers on pension funding dynamics.     

Upper and lower bounding procedures for the minimum caterpillar spanning problem

A spanning caterpillar in a graph is a tree composed by a path such that all vertices not in the path are leaves. In the Minimum Spanning Caterpillar Problem (MSCP) each edge has two costs: a path cost when it belongs to the path and a connection cost when it is incident to a leaf. The goal is to find a spanning caterpillar minimizing the sum of all path and connection costs. In this paper we formulate the as a minimum Steiner arborescence problem. This reduction is the basis for the development of an efficient branch-and-cut algorithm for the MSCP. We als developed a GRASP heuristic to generate primal bounds. Experiments carried out on instances adapted from TSPLIB 2.1 revealed that the exact algorithm is capable to solve to optimality instances with up to 300 vertices in reasonable time. They also showed that our heuristic yields very high quality solutions.     

Solving the Wire-Harness Design Problem at a European car manufacturer

In many industries, increasing competition and high customer expectations compel companies to widen their product offering. Car manufacturers face the same issue and tend to produce an increasingly large variety of models, with more and more electrical options. This leads to the design of a huge number of different electrical wire harnesses, but the use of component commonality can help reduce this number. This work, initiated in collaboration with a major European car manufacturer, provides a new mathematical formulation of the problem and several solution methods. Due to its size, the problem cannot be solved to optimality in reasonable time by exact methods. Hence, we introduce greedy heuristics, a descent method and a variable neighborhood search metaheuristic that provide high quality solutions in reasonable computing time. Results show that the use of these solution methods yields important cost savings with respect to the current practice of the company.     

定时截尾下指数分布产品可靠性抽样检验方案

本文给出了制订定时截尾下指数分布产品可靠性抽样检验方案的统计方法.检验统计量是平均寿命倒数的极大似然估计.提出了一种选择截尾时间的方法.利用分布分位数的Cornish-Fisher展开近似地确定了样本量和接收常数.模拟结果表明,本文给出的方法是可行的.