Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfil demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses.     

海外区域技术平台投资的时机选择模型及实证研究

基于后发企业海外区域技术平台(RTP)投资中“成熟技术产品推广”和“新技术产品开发”两个阶段,本文构建了两阶段实物期权模型。进一步,利用中国制造业对外直接投资(OFDI)的上市公司样本,考察了RTP投资时机选择的决定因素及影响效果。研究发现,第一阶段外生不确定性越低、第二阶段内生不确定性越高、新技术产品项目的增长期权越大,企业越倾向于尽早地RTP投资于海外市场;在两阶段内外生不确定性的不同条件下,RTP投资时机相应地对企业创新增长产生了显著的差异影响。     

An Inhomogeneous Uncertainty Principle for Digital Low-Pass Filters

This article introduces an inhomogeneous uncertainty principle for digital lowpass filters. The measure for uncertainty is a product of two factors evaluating the frequency selectivity in comparison with the ideal filter and the effective length of the filter in the digital domain, respectively. We derive a sharp lower bound for this product in the class of filters with so-called finite effective length and show the absence of minimizers. We find necessary and certain sufficient conditions to identify minimizing sequences. When the class of filters is restricted to a given maximal length, we show the existence of an uncertainty minimizer. The uncertainty product of such minimizing filters approaches the unrestricted infimum as the filter length increases. We examine the asymptotics and explicitly construct a sequence of finite-length filters with the same asymptotics as the sequence of finite-length minimizers.     

技术不确定环境下新产品策略的设计应用

技术的动态发展和企业间的竞争对企业新产品策略有很大影响,直接决定新产品的引进周期。本文在产业技术动态变化的随机环境下构建随机动态规划模型,关注产业技术进步、投资成本和产品市场竞争等影响因素,探讨企业进行新产品引进的周期选择,对新产品引进的周期和质量决策进行方法设计和应用举例。利用随机动态规划模型得出新产品引进的最优时间周期,用算例分析技术进步和产品研发成本对企业引进周期策略的影响,采取策略迭代的方法进行求解,发现技术进步较快时企业的新产品引进步伐也较快,研发成本的提高使企业的新产品引入步伐降低。     

Treating measurement uncertainty in industrial conformity control

Every conformity control method based on measurements is subject to uncertainty, which distorts the decision. In the traditional conformity control approaches, this uncertainty is an inherent part of the deviation of the observed characteristic; however, the distribution of the real product characteristic may differ from the distribution of measurement uncertainty, which obscures the real conformity or nonconformity. The specification and consideration of this uncertainty are particularly necessary if it is high and/or the consequences associated with the decision errors are severe. This paper studies the effects of the cost structure associated with the decision outcomes and the skewness and kurtosis of the measurement uncertainty distribution. The proposed method can specify when and how the measurement uncertainty should be taken into account to increase the expected profit associated with the decision.     

A Chance-Constrained Integer Goal Programming Model for Capital Budgeting in the Production Area

The selection of capital projects in a production environment is complicated by the existence of multiple and conflicting goals. Typical production objectives for cost minimization often conflict with goals for quality, environmental standards, labor relations, etc. This problem of project selection is further complicated by the uncertainty inherent in product demand, the key factor in production management. This paper approaches these complications by employing an integer goal programming (to compensate for multiple conflicting objectives) with chance-constrained capabilities (to reflect uncertainty in product demand). The approach is demonstrated via an in-depth case example of a production problem.     

Biologically guided intensity modulated radiation therapy planning optimization with fraction-size dose constraints

Although intensity modulated radiation therapy plans are optimized as a single overall treatment plan, they are delivered over 30–50 treatment sessions (fractions) and both cumulative and per-fraction dose constraints apply. Recent advances in imaging technology provide more insight on tumour biology that has been traditionally disregarded in planning. The current practice of delivering physical dose distributions across the tumour may potentially be improved by dose distributions guided by the biological responses of the tumour points. The biological optimization models developed and tested in this paper generate treatment plans reacting to the tumour biology prior to the treatment as well as the changing tumour biology throughout the treatment while satisfying both cumulative and fraction-size dose limits. Complete computational testing of the proposed methods would require an array of clinical data sets with tumour biology information. Finding no open source ones in the literature, the authors sought proof of concept by testing on a simulated head-and-neck case adapted from a more standard one in the CERR library by blending it with available tumour biology data from a published study. The results show computed biologically optimized plans improve on tumour control obtained by traditional plans ignoring biology, and that such improvements persist under likely uncertainty in sensitivity values. Furthermore, adaptive plans using biological information improve on non-adaptive methods.     

IMPACTS OF WATER SUPPLY UNCERTAINTY AND STORAGE ON EFFICIENT IRRIGATION TECHNOLOGY ADOPTION

In the framework of a stochastic dynamic programming model, the paper investigates the impact of water supply uncertainty and storage at farm level on adoption of efficient irrigation technologies under a flexible water price regime. We find that even a flexible water pricing cannot guarantee higher adoption of efficient irrigation technology in all cases. Results of the paper indicate that if a farmer invests in water storage capacity, then the value of efficient usage of water increases, and the rate of adoption of efficient irrigation technology will be higher. It establishes a complementarity relationship between investments in storage capacity and adoption of efficient irrigation technology. The relationship becomes stronger with increasing variance in water supply. In a situation without any option to store water at the farm level, we find a negative relationship between investment in efficient irrigation technology and water variability. However, numerical analysis results suggest that a risk averse farmer may invest more in efficient irrigation only if the variance in water supply is very high.     

Impact of product pricing and timing of investment decisions on supply chain co-opetition

In supply chain co-opetition, firms simultaneously compete and co-operate in order to maximize their profits. We consider the nature of co-opetition between two firms: The product supplier invests in the technology to improve quality, and the purchasing firm (buyer) invests in selling effort to develop the market for the product before uncertainty in demand is resolved. We consider three different decision making structures and discuss the optimal configuration from each firm’s perspective. In case 1, the supplier invests in product quality and sets the wholesale price for the product. The buyer then exerts selling effort to develop the market and following demand potential realization, sets the resale price. In case 2, the supplier invests in product quality followed by the buyer’s investment in selling effort. Then, after demand potential is observed, the supplier sets the wholesale price and the buyer sets the resale price. Finally, in case 3, both firms simultaneously invest in product quality and selling effort, respectively. Subsequently, observing the demand potential, the supplier sets the wholesale price and the buyer sets the resale price. We compare all configuration options from both the perspective of the supplier and the buyer, and show that the level of investment by the firms depends on the nature of competition between them and the level of uncertainty in demand. Our analysis reveals that although configuration 1 results in the highest profits for the integrated channel, there is no clear dominating preference on system configuration from the perspective of both parties. The incentives of the co-opetition partners and the investment levels are mainly governed by the cost structure and the level of uncertainty in demand. We examine and discuss the relation between system parameters and the incentives in desiging the supply contract structure.     

基于可拓变换的产品适应性设计研究

适应性设计是一种在面向产品全生命周期设计中解决矛盾问题的有效途径.然而,现有设计方法很难客观地反映定性定量相结合求解设计矛盾问题.为此,提出一种基于可拓变换的产品适应性设计方法.首先建立可拓适应性设计一般流程,定义可拓设计基元,建立适应性设计矛盾问题,然后进行可拓分析,建立起传导矛盾环系统,以及满足产品功能-结构的变换规则、适应度函数,再通过可拓变换实现产品设计基元之间冲突的消解,输出适应性设计方案.最后将该方法应用于某全自动LED分拣机振动盘的适应性设计.应用实例表明:该设计方法不仅能快速满足不同需求下的个性化要求,而且能为计算机参与解决产品设计矛盾问题提供一种可行的途径.     

R&D pipeline management: Task interdependencies and risk management

Maintaining a rich research and development (R&D) pipeline is the key to remaining competitive in many industrial sectors. Due to its nature, R&D activities are subject to multiple sources of uncertainty, the modeling of which is compounded by the ability of the decision maker to alter the underlying process. In this paper, we present a multi-stage stochastic programming framework for R&D pipeline management, which demonstrates how essential considerations can be modeled in an efficient manner including: (i) the selection and scheduling of R&D tasks with general precedence constraints under pass/fail uncertainty, and (ii) resource planning decisions (expansion/contraction and outsourcing) for multiple resource types. Furthermore, we study interdependencies between tasks in terms of probability of success, resource usage and market impact. Finally, we explore risk management approaches, including novel formulations for value at risk and conditional value at risk.     

Tool capacity planning for semiconductor fabrication facilities under demand uncertainty

This research is motivated by issues faced by a large manufacturer of semiconductor devices. Semiconductor manufacturing companies allocate millions of dollars every year for new types of machine tools for their facilities. Typically these are special purpose machine tools which are made to order. The rate of change in products and technology makes it difficult for manufacturers to have a good estimate of future tool requirements. Further, manufacturers experience a long lead time while procuring these tools. In this paper, we model the tool capacity planning problem under uncertainty in demand. The number of tools required in a facility is sufficiently large (nearly hundred or more tools) to make it nearly impossible to obtain efficient exact algorithms. We provide heuristics to find efficient tool procurement plans and test their quality using lower bounds on the formulation.     

Longevity risk in pension annuities with exchange options: The effect of product design

We consider defined benefit pension plans that, at retirement age, allow the participant to choose between a single life annuity and a joint and survivor annuity. We compare two plans that differ in terms of how pension rights are accrued. In one plan, the participant accrues the right to receive a single life annuity, and can exchange that annuity for an actuarially equivalent joint and survivor annuity at retirement date. The opposite holds in the other plan. We show that both plans are affected by longevity risk in two ways. First, the participants’ choices at retirement age affect the ratio of survivor benefits over single life benefits, and, therefore, affect the natural hedge potential that arises from combining single life and survivor annuities. Second, uncertainty in the rate at which the participant will be allowed to exchange one type of annuity for the other at retirement date induces uncertainty in the level of the nominal rights for single life and survivor annuities, respectively. We compare the two plans, and show that longevity risk is substantially lower in case rights are accrued in the form of a joint and survivor annuity.     

Data uncertainty analysis in environmental management

Imprecision of input data is an important characteristic feature of environmental monitoring. When making evaluations, conclusions and the decisions from collected data, one has to be very careful not to make fatal mistake. The important task of computing and current information and communication technology is to deal with the primary data uncertainty and thus to reduce the risk of mistake. At present new approaches and methodologies to handle environmental data uncertainty are explored as opposite to standard. The paper presents comparison of such new approach against European Environment Agency and U.S. Environmental Protection Agency standards in the several cases of the assessment environmental management indicators in the Czech Republic. The new approach brings more modular way of dealing with uncertainty and is supposed to bring better results and less workload then standard approach. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Planning under Uncertainty: II. A Methodology for Robustness Analysis

A companion paper found that the principal planning methodologies embody procedures which tend to generate plans which are inflexible; and identified characteristics which a methodology for flexible planning should exhibit. In this paper an integrated methodology incorporating these features is proposed. Centred on robustness analysis it is non-optimising, being based on the establishment of feasible alternative plans; it accepts future uncertainty as a defining characteristic and attempts to keep options open; and it can facilitate participation. The potential advantages and disadvantages of the methodology are discussed; and some technical options in robustness analysis are presented in an Appendix.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

A robust data envelopment analysis model with different scenarios

Input and output data, under uncertainty, must be taken into account as an essential part of data envelopment analysis (DEA) models in practice. Many researchers have dealt with this kind of problem using fuzzy approaches, DEA models with interval data or probabilistic models. This paper presents an approach to scenario-based robust optimization for conventional DEA models. To consider the uncertainty in DEA models, different scenarios are formulated with a specified probability for input and output data instead of using point estimates. The robust DEA model proposed is aimed at ranking decision-making units (DMUs) based on their sensitivity analysis within the given set of scenarios, considering both feasibility and optimality factors in the objective function. The model is based on the technique proposed by Mulvey et al. (1995) for solving stochastic optimization problems. The effect of DMUs on the product possibility set is calculated using the Monte Carlo method in order to extract weights for feasibility and optimality factors in the goal programming model. The approach proposed is illustrated and verified by a case study of an engineering company.     

A surrogate based multi-fidelity approach for robust design optimization

Robust design optimization (RDO) is a field of optimization in which certain measure of robustness is sought against uncertainty. Unlike conventional optimization, the number of function evaluations in RDO is significantly more which often renders it time consuming and computationally cumbersome. This paper presents two new methods for solving the RDO problems. The proposed methods couple differential evolution algorithm (DEA) with polynomial correlated function expansion (PCFE). While DEA is utilized for solving the optimization problem, PCFE is utilized for calculating the statistical moments. Three examples have been presented to illustrate the performance of the proposed approaches. Results obtained indicate that the proposed approaches provide accurate and computationally efficient estimates of the RDO problems. Moreover, the proposed approaches outperforms popular RDO techniques such as tensor product quadrature, Taylor’s series and Kriging. Finally, the proposed approaches have been utilized for robust hydroelectric flow optimization, demonstrating its capability in solving large scale problems.     

企业技术投资决策模型及其模拟

何时投资购买技术是企业技术战略研究的一个关键问题.传统的技术投资的决策准则是使用净现值(NPV)方法,这种方法会导致企业过早的投资于一项技术,也不能解释企业技术投资时间滞后的现象.企业技术投资时间的滞后可以通过技术本身的不确定性来解释,技术的不确定性主要表现为在技术演化过程中新技术出现的速度和新技术技术效率改进程度的不确定.本文针对企业技术投资中的技术效率决策问题,假设技术出现服从泊松分布、技术改进程度服从均匀分布,构建了技术投资决策模型,并对模型进行模拟,结论表明决策模型比NPV方法能更好的解释企业技术投资决策的行为.     

Supply chain optimization of petroleum organization under uncertainty in market demands and prices

A multi-period stochastic planning model has been developed and implemented for a supply chain network of a petroleum organization operating in an oil producing country under uncertain market conditions. The proposed supply chain network consists of all activities related to crude oil production, processing and distribution. Uncertainties were introduced in market demands and prices. A deterministic optimization model was first developed and tested. The impact of uncertainty on the supply chain was studied by performing a sensitivity analysis in which ±20% deviations were introduced in market demands and prices of different commodities. A stochastic formulation was then proposed, which is based on the two-stage problem with finite number of realizations. The proposed stochastic programming approach proved to be quite effective in developing resilient production plans in light of high degree of uncertainty in market conditions. The anticipated production plans have a considerably lower expected value of perfect information (EVPI). The main conclusion of this study is that for an oil producing country with oil processing capabilities, the impact of economic uncertainties may be tolerated by an appropriate balance between crude exports and processing capacities.