考虑不同回收质量等级的再制造系统回收生产决策

废旧产品的回收再制造过程往往在回收质量、再制造成本、再制造产出率、再制造产品需求等方面存在不确定性因素,极大地增加了再制造生产管理决策的复杂性。本文在一个回收再制造系统中研究了存在多种回收质量等级时的两阶段回收—再制造联合优化决策问题,并扩展到需求与价格相关和再制造产出率随机两种情形。在最大化再制造商期望利润的决策目标下,基于每种回收质量等级的单位回收和再制造成本构造出再制造系统的有效生产前沿面,给出了不同决策情形下再制造商的最优回收数量、销售定价的解析解,并且分析了一些主要的参数对再制造商最优决策的影响。本文的研究结果表明：（1）含有多种回收质量等级的再制造系统中存在一个下凸的有效生产前沿面,不在该前沿面上的任何质量等级的回收产品都将不会被用于再制造;（2）在同等的政府补贴额度下,回收补贴方式对再制造商决策的影响程度大于再制造补贴方式;（3）当再制造品的市场需求与价格相关时,最优销售价格至少大于第一种被使用的回收质量等级的边际回收和再制造成本;（4）任意两种回收质量等级之间存在着替代或互补效应,由其成本差异决定,并且这种效应随着需求不确定性的增大而增大;（5）再制造产出率的不确定性和再制造品需求的不确定性之间存在"对冲"效应,这种效应随着再制造产出率不确定性的降低而减弱。本文的研究可为不确定性环境下再制造企业的回收、生产管理决策提供有益的管理启示。     

The Effect of Cognitive Load on Decision Making with Graphically Displayed Uncertainty Information

An experiment examined the ability of five graphical displays to communicate uncertainty information when end users were under cognitive load (i.e., remembering an eight‐digit number). The extent to which people could accurately derive information from the graphs and the adequacy of decisions about optimal behaviors based on the graphs were assessed across eight scenarios in which probabilistic outcomes were described. Results indicated that the load manipulation did not have an overall effect on derivation of information from the graphs (i.e., mean and probability estimation) but did suppress the ability to optimize behavioral choices based on the graph. Cognitive load affected people's use of some graphical displays (basic probability distribution function) more than others. Overall, the research suggests that interpreting basic characteristics of uncertainty data is unharmed under conditions of limited cognitive resources, whereas more deliberative processing is negatively affected.     

碳交易政策下多式联运路径选择问题的鲁棒优化研究

针对碳交易政策下的多式联运路径选择问题,考虑运输时间和单位运费率不确定且其概率分布未知的情况,引入鲁棒优化建模方法对其进行研究。首先利用box不确定集合刻画分布未知的运输时间和运费率,然后在碳交易政策下确定模型的基础上,构建鲁棒性可调节的多式联运路径选择模型,并通过对偶转化得到相对易求解的鲁棒等价模型。实例分析表明,鲁棒模型能较好地处理参数概率分布未知的多式联运路径选择问题,方便决策者根据偏好调整不确定预算水平进行决策。运输时间和单位运费率的不确定性都会影响多式联运路径决策,但是作用机理有所不同。将上述碳交易政策下的模型拓展到其他低碳政策,结果表明多种低碳政策的组合能更好实现多式联运减排。     

广义ATO下基于多种团购模型的供应链协调

针对由多个供应商、单个制造商和零售商所组成的广义按订单装配式供应链，在上游多个供应商不确定的零部件（或商品）供应以及下游客户不确定的需求的环境下，首先分析基本模型中团购前后的零售商和制造商的利润变化，然后提出了团购的标准模型和协调模型，并比较三种不同的团购模型对各成员以及全局供应链绩效的影响。通过数学推导证明和仿真数据算例分析，结果表明：零售商自发的团购基本模型总是对制造商有利，而对零售商的好处是有限的；由制造商主导的团购标准模型在满足一定条件时是优于团购基本模型的，但有使得零售商甚至制造商自身受到损失的可能；而集中决策下的团购协调模型能够提升全局供应链的期望利润，实现渠道的帕累托改善，但协调的效果会受到团购客户组成结构的限制。     

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 1—Decision Support System

This article introduces a new integrated scenario-based evacuation (ISE) framework to support hurricane evacuation decision making. It explicitly captures the dynamics, uncertainty, and human–natural system interactions that are fundamental to the challenge of hurricane evacuation, but have not been fully captured in previous formal evacuation models. The hazard is represented with an ensemble of probabilistic scenarios, population behavior with a dynamic decision model, and traffic with a dynamic user equilibrium model. The components are integrated in a multistage stochastic programming model that minimizes risk and travel times to provide a tree of evacuation order recommendations and an evaluation of the risk and travel time performance for that solution. The ISE framework recommendations offer an advance in the state of the art because they: (1) are based on an integrated hazard assessment (designed to ultimately include inland flooding), (2) explicitly balance the sometimes competing objectives of minimizing risk and minimizing travel time, (3) offer a well-hedged solution that is robust under the range of ways the hurricane might evolve, and (4) leverage the substantial value of increasing information (or decreasing degree of uncertainty) over the course of a hurricane event. A case study for Hurricane Isabel (2003) in eastern North Carolina is presented to demonstrate how the framework is applied, the type of results it can provide, and how it compares to available methods of a single scenario deterministic analysis and a two-stage stochastic program.     

Presenting Results from Model Based Studies to Decision-Makers: Can Sensitivity Analysis be a Defogging Agent?

The motivation of the present work is to provide an auxiliary tool for the decision-maker (DM) faced with predictive model uncertainty. The tool is especially suited for the allocation of R&Dresources. When taking decisions under uncertainties, making use of the output from mathematical or computational models, the DM might be helped if the uncertainty in model predictions be decomposed in a quantitative-rather than qualitativefashion, apportioning uncertainty according to source. This would allow optimal use of resources to reduce the imprecision in the prediction. For complex models, such a decomposition of the uncertainty into constituent elements could be impractical as such, due to the large number of parameters involved. If instead parameters could be grouped into logical subsets, then the analysis could be more useful, also because the decision maker might likely have different perceptions (and degrees of acceptance) for different kinds of uncertainty. For instance, the decomposition in groups could involve one subset of factors for each constituent module of the model; or one set for the weights, and one for the factors in a multicriteria analysis; or phenomenological parameters of the model vs. factors driving the model configuratiodstructure aggregation level, etc.); finally, one might imagine that a partition of the uncertainty could be sought between stochastic (or aleatory) and subjective (or epistemic) uncertainty. The present note shows how to compute rigorous decomposition of the output's variance with grouped parameters, and how this approach may be beneficial for the efficiency and transparency of the analysis.     

A Note on the Relationship Among Capacity,Pricing, and Inventory in a Make‐to‐Stock System

We address the simultaneous determination of pricing, production, and capacity investment decisions by a monopolistic firm in a multi‐period setting under demand uncertainty. We analyze the optimal decision with particular emphasis on the relationship between price and capacity. We consider models that allow for either bi‐directional price changes or models with markdowns only, and in the latter case we prove that capacity and price are strategic substitutes.     

考虑交易成本的投资组合效率估计方法

投资组合绩效评价是学术界研究的热点问题。本文在经典的经济学框架下,基于真实前沿面,给出了投资组合效率的明确定义。由于实际投资环境的影响,投资组合优化模型非常复杂,难以获得真实前沿面的解析解,这给投资组合效率的应用带来了很大的困难。本文基于投资组合理论,在投资组合模型所对应的前沿面为凹函数的情况下,采用基于数据的投资组合DEA评价模型构造前沿面来逼近真实的前沿面,从而估计一般情形下投资组合的效率。在此基础上研究了考虑交易成本的投资组合效率评价问题,并用实例说明了本文方法的合理性与可行性。     

考虑供应风险和生产成本不确定性的供应链动态决策研究

考虑到供应风险和生产成本的不确定性,本文研究了买方或卖方实施努力既可能改善供应可靠性,同时又可能降低生产成本的供应链最优决策问题。分别构建了制造商努力和供应商努力的两阶段动态决策模型。分析了提升供应可靠性和降低成本的概率对两种模型中的最优采购数量、最优努力程度和上下游期望收益以及供应链期望收益的影响。同时在两种模型的比较中发现,制造商的最优努力与供应商的最优努力之间的差异与收益分享系数有关。相同努力效果下,制造商努力时的最优采购数量总是不小于供应商努力时的最优采购数量。相比供应商努力,制造商努力更有利于增加其自身的期望收益。然而,供应商以及整个供应链的期望收益却可能在供应商努力时更高。     

Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation

Sequential resource allocation decision-making for the military medical evacuation of wartime casualties consists of identifying which available aeromedical evacuation (MEDEVAC) assets to dispatch in response to each casualty event. These sequential decisions are complicated due to uncertainty in casualty demand (i.e., severity, number, and location) and service times. In this research, we present a Markov decision process model solved using a hierarchical aggregation value function approximation scheme within an approximate policy iteration algorithmic framework. The model seeks to optimize this sequential resource allocation decision under uncertainty of how to best dispatch MEDEVAC assets to calls for service. The policies determined via our approximate dynamic programming (ADP) approach are compared to optimal military MEDEVAC dispatching policies for two small-scale problem instances and are compared to a closest-available MEDEVAC dispatching policy that is typically implemented in practice for a large-scale problem instance. Results indicate that our proposed approximation scheme provides high-quality, scalable dispatching policies that are more easily employed by military medical planners in the field. The identified ADP policies attain 99.8% and 99.5% optimal for the 6- and 12-zone problem instances investigated, as well as 9.6%, 9.2%, and 12.4% improvement over the closest-MEDEVAC policy for the 6-, 12-, and 34-zone problem instances investigated.     

TECHNOLOGICAL PROGRESS AND TECHNOLOGY ACQUISITION: STRATEGIC DECISION UNDER UNCERTAINTY

We develop a stochastic programming model to aid manufacturing firms in making strategic decisions in technology acquisition. The proposed model maximizes the firm's expected profit under the condition of the uncertainty in technological progress and development. To solve this large‐scale problem, we decompose future uncertainties through scenarios and then develop an algorithm to solve the resulting non‐linear subproblems efficiently. Finally, we develop a heuristic to eliminate the infeasibility in the master problem and obtain best solutions. Numerical results show that our heuristic solutions are very close to the optimal solutions and meaningful insights are derived.     

基于工业互联网平台的增值服务收费模式研究

工业互联网平台的增值服务可以给制造商提供数字化转型服务赋能，实现提质降本增效的作用。本文构建包含一个工业互联网平台、一个制造商和一个零售商组成的三级供应链博弈模型，求解基于使用量收费和订阅收费模式下的最优决策。分析不同定价模式下企业数字化转型决策和利润受到增值服务因子、成本降低因子的影响，以及对两种定价模式下的最优决策进行比较。研究显示：存在不同的平台增值服务效果阈值，当服务效果位于不同阈值范围时，平台和制造商将选择订阅收费模式或按使用量收费模式。当平台增值服务效果适中时，平台和制造商都选择订阅收费模式。当平台增值服务效果较低(高)时，平台(制造商)选择基于使用量收费模式，而制造商(平台)选择订阅收费模式。     

Centralized resource allocation based on efficiency analysis for step-by-step improvement paths

The existing centralized resource allocation models often assume that all of the DMUs are efficient after resource allocation. For the DMU with a very low efficiency score, it means the dramatic reduction of the resources, which can cause the organizational resistance. In addition, in reality, it is particularly difficult for the DMUs to achieve their target efficiencies in a single step, especially when they are far from the efficient frontier. Thus, gradual progress towards benchmarking targets is gaining importance. In this paper, we present a new approach for resource allocation based on efficiency analysis under a centralized decision-making environment. Through our approach, the central decision-maker can obtain a sequence of intermediate benchmark targets based on efficiency analysis, which provide a level-wise improvement path to direct the DMUs to reach their ultimate targets on the efficient frontier in an implementable and realistic manner. Numerical examples are presented to illustrate the application procedure of the proposed approach.     

Management of Energy Technology for Sustainability: How to Fund Energy Technology Research and Development

Operations management methods have been applied profitably to a wide range of technology portfolio management problems, but have been slow to be adopted by governments and policy makers. We develop a framework that allows us to apply such techniques to a large and important public policy problem: energy technology R&D portfolio management under climate change. We apply a multi‐model approach, implementing probabilistic data derived from expert elicitations into a novel stochastic programming version of a dynamic integrated assessment model. We note that while the unifying framework we present can be applied to a range of models and data sets, the specific results depend on the data and assumptions used and therefore may not be generalizable. Nevertheless, the results are suggestive, and we find that the optimal technology portfolio for the set of projects considered is fairly robust to different specifications of climate uncertainty, to different policy environments, and to assumptions about the opportunity cost of investing. We also conclude that policy makers would do better to over‐invest in R&D rather than under‐invest. Finally, we show that R&D can play different roles in different types of policy environments, sometimes leading primarily to cost reduction, other times leading to better environmental outcomes.     

基于似然估计的零售商库存鲁棒均值-风险模型

针对具有风险厌恶的零售商,建立了权衡期望利润和条件风险值（CVaR）的均值-风险库存优化模型,给出了离散需求分布不确定条件下能实现帕累托最优但具有较高保守性和非帕累托最优但具有较低保守性的两种鲁棒对应。针对不确定需求分布,在仅知历史需求样本数据情况下,应用统计推断理论构建了满足一定置信水平的基于似然估计的需求概率分布不确定集。在此基础上,运用拉格朗日对偶理论,将上述两种鲁棒对应模型转化为易于求解的凹优化问题,并证明了其与原问题的等价性。最后,针对实际案例进行了数值计算,分析了不同系统参数和样本规模对零售商最优库存决策及其运作绩效的影响,并给出了零售商期望利润和条件风险值两个目标权衡的帕累托有效前沿。结果表明,采用基于似然估计的鲁棒优化方法得到的零售商库存策略具有良好鲁棒性,能够有效抑制需求分布不确定性对零售商库存绩效的影响。而且,历史需求样本规模越大,鲁棒库存策略下的零售商运作绩效越接近最优情况。进一步,通过对比发现,两种鲁棒对应模型虽然保守性不同,但在最终库存策略上保持一致。     

THE EFFECTS OF VARIABLE AND FIXED TRANSACTION COSTS ON OPTIMAL INVESTMENT DECISIONS

This paper studies the impact of variable and fixed transaction costs on investment decisions under conditions of risk. The decision model is first formulated as a mixed-integer nonlinear program. The following subjects are then examined: the structure of the investment frontier facing the investor and the effects of transaction costs on this frontier, the impact of transaction costs on the investor's optimal investment strategy, and the conditions for the equilibrium structure of risky asset prices and risk-return relationships. The main finding is that the relaxation of the assumption of the absence of transaction costs eliminates some of the most unattractive implications of the classic capital asset pricing model (CAPM) while preserving the more attractive implications of this model. Also, our model provides explanations for some discrepancies between the theoretical CAPM and empirical findings and, therefore, is a step toward narrowing the gap between theory and practice.     

Simultaneous Determination of Joint Product Cost Allocations and Cost-Plus Prices*

Use of the net realizable value approach for joint manufacturing cost allocations requires knowledge of selling prices of joint products. However, joint product selling prices themselves are functions of the allocated costs under a cost-plus pricing policy. In this case, it is necessary to determine joint cost allocations and joint product prices simultaneously. This paper applies a nonlinear programming (NLP) approach to simultaneously determine the optimal joint production decision, joint product cost-plus prices, and joint cost allocations using the net realizable value method. The NLP solution provides not only the optimal joint production and pricing decisions, but also the necessary conditions for such optimal decisions.     

灾后不确定需求下应急医疗移动医院鲁棒选址问题研究

我国灾害医学救援主要采用"现场救治"模式，应急医疗移动医院的选址是否合理直接影响救援效率，但各受灾点伤员数量的不确定性增加了决策的困难。本文引入多面体不确定集合刻画伤员数量的不确定性，同时考虑伤员分类及移动医院分型，构建一个以伤员总生存概率最大化为目标的鲁棒选址模型。利用鲁棒优化理论，将模型转化为等价的混合整数规划问题，通过GAMS软件编程并调用CPLEX求解器求解。最后，以四川芦山地震应急医疗救援为例，验证模型和求解方法的可行性和鲁棒性。结果表明，扰动比例和不确定水平对移动医院的选址和伤员的分配方案有显著影响，决策者可根据自己对不确定性风险的偏好程度选择最佳的扰动比例和不确定水平组合，以获得最优的选址分配方案。     

需求不确定条件下的制造商订单分配模型

制造商的订单分配作为供应链模型微观层面的重要组成部分,对提升整个供应链效率有很大影响,但需求层面的不确定因素加大了订单分配的难度。以按比例分配为原则,讨论在需求不确定条件下完全信息与不完全信息两类多供应商-单制造商的订单分配模型。重点研究完全信息条件下各方的分散决策和集中决策,由于后者能避免各参与方对其他决策方的边际影响,所以能够实现供应链总利润的最大化;其次又将不完全信息引入模型,讨论制造商如何通过折算因子结合已有信息对供应商的私人信息进行估计,进而做出决策。最后以需求服从正态分布为例对两类模型进行验证。