On the maximum principle for deterministic impulse control problems

This paper is devoted to a simple and direct proof of a version of the Blaquiere's maximum principle for deterministic impulse control problems.     

Generalized Nonlinear Variational-Like Inequalities

This study investigates how to dynamically allocate resources with a given budget for advertising through Web portals using keyword-activated banner ads on the Internet. Identifying the factors that affect the potential number of banner ad clickthroughs in each portal, we show that the process of budget allocation between the two types of portals (generic vs specialized) that leads to the largest banner clicksthrough in the long run is an optimal control problem. Using techniques of dynamic programming, we find analytical solutions for the optimal budgeting decisions. Our analysis shows that an advertisers optimal portal budgeting depends nonlinearly on the number of visitors who type the same trigger keyword and the average clicksthrough rates, as well as on the advertiser and ad effectiveness. Further, we find that the maximal number of banner clickthroughs from both portals, at time t, depends on the remaining budget until the end of the planning period. The analytical results have useful managerial insight. One of the interesting features of our solution shows that, while a large visitor base may favor the generic portal, other parameters may affect it unfavorably: e.g., lower clickthrough rates of keyword banners from a more heterogeneous audience. Using a specificaction that is consistent with empirical observations, we show that, in the long run, an advertiser must always spend more ad money at the specialized portal.     

Lavrentiev's regularization method for nonlinear ill-posed equations in Banach spaces

In this paper, we deal with nonlinear ill-posed problems involving m-accretive mappings in Banach spaces. We consider a derivative and inverse free method for the implementation of Lavrentiev regularization method. Using general H¨older type source condition we obtain an optimal order error estimate. Also we consider the adaptive parameter choice strategy proposed by Pereverzev and Schock(2005) for choosing the regularization parameter.     

Improving biorefinery planning: Integration of spatial data using exact optimization nested in an evolutionary strategy

Biorefineries can provide a product portfolio from renewable biomass similar to that of crude oil refineries. To operate biorefineries of any kind, however, the availability of biomass inputs is crucial and must be considered during planning. Here, we develop a planning approach that uses Geographic Information Systems (GIS) to account for spatially scattered biomass when optimizing a biorefinery’s location, capacity, and configuration. To deal with the challenges of a non-smooth objective function arising from the geographic data, higher dimensionality, and strict constraints, the planning problem is repeatedly decomposed by nesting an exact nonlinear program (NLP) inside an evolutionary strategy (ES) heuristic, which handles the spatial data from the GIS. We demonstrate the functionality of the algorithm and show how including spatial data improves the planning process by optimizing a synthesis gas biorefinery using this new planning approach.     

Dividend optimization for jump–diffusion model with solvency constraints

Belhaj (2010) established that a barrier strategy is optimal for the dividend problem under jump–diffusion model. However, if the optimal dividend barrier level is set too low, then the bankruptcy probability may be too high to be acceptable. This paper aims to address this issue by taking the solvency constrain into consideration. Precisely, we consider a dividend payment problem with solvency constraint under a jump–diffusion model. Using stochastic control and PIDE, we derive the optimal dividend strategy of the problem.     

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??In this paper, we consider the optimal investment strategy which maximizes the utility of the terminal wealth of an insurer with SAHARA utility functions. This class of utility functions has non-monotone absolute risk aversion, which is more flexible than the CARA and CRRA utility functions. In the case that the risk process is modeled as a Brownian motion and the stock process is modeled as a geometric Brownian motion, we get the closed-form solutions for our problem by the martingale method for both the constant threshold and when the threshold evolves dynamically according to a specific process. Finally, we show that the optimal strategy is state-dependent.     

污染产业转移下府际间合作治理大气污染的演化博弈分析

大气污染具有跨区域性,府际间合作治理是大气污染防治的必由之路,合作治理中存在“搭治污便车”的困境。由于府际间大气污染治理博弈的具有长期性和动态性,由此建立了多人两策略的演化博弈模型。分别研究,无约束机制和有约束机制两种情况下地方政府演化稳定策略及其影响因素。考虑到政府间污染产业转移,建立了污染产业转移政府对承接方政府治污补偿机制下的演化博弈模型,分别研究无约束和有约束两种情形下(补偿,治理)策略成为演化稳定均衡状态的条件。研究表明:府际间的合作治理策略选择不会受到外部效应的影响,大气污染治理成本、治理时污染物减排量、不治理时污染物排放增加量、中央政府对治理的奖励和对不治理的惩罚力度是影响地方政府决策的重要因素。将大气质量作为政绩考核的重要指标,提高其权重系数,可有效促使地方政府策略向参与合作治理演化。无约束下自主选择时,最优策略(补偿,治理)不会成为稳定均衡策略,引入中央政府约束后,当经济惩罚在一定范围内,(补偿,治理)成为稳定均衡策略。     

基于银行下侧风险规避的第三方B2B平台激励策略研究

以电子仓单融资为例, 基于银行下侧风险规避角度, 研究联合授信和委托授信下当第三方B2B平台存在行为隐匿的道德风险时, 银行对B2B平台的激励策略设计问题。研究发现:B2B平台的最优努力水平随收益分配比例、回购比例的增大而减小, 随质押率、贷款利率、产品采购量、损失补偿比例的增大而增大;同时银行为规避违约风险, 需设置质押率、贷款利率和贷款额上限及回购比例下限, 并且银行最优收益分配比例与损失补偿比例、最优损失补偿比例与贷款损失率均成正相关关系。此外, 随着B2B平台工作效率的提高, 联合授信下最优收益分配比例将减小, 最优损失补偿比例将增大, 最终近似于委托授信下的最优损失补偿比例。最后给出数值分析。     

快速路交通微分系统的脉冲迭代学习策略研究

针对快速路交通微分系统,结合该交通系统具有明显的重复性和周期性等特点,设计了一种脉冲迭代学习策略,并建立了迭代学习误差的收敛性条件.其次,利用λ范数和脉冲Gronwall不等式等方法证明了算法的收敛性.结合脉冲控制与学习控制的特点,该方法的优点在于容易实现和经济环保,且能有效地达到控制目的.最后,通过数值仿真实证进一步说明所设计控制方法的有效性.     

顾客参与研发过程的价值共创机理与策略研究

顾客参与企业研发是产品价值创造的最初始过程,不仅提高产品接受度、满足顾客需求,而且为企业增加收入,但同时也产生相应成本。文章探究顾客参与研发的系统中,顾客、企业和产品的价值共创行为以及企业决策如何影响三者价值;立足多目标视角,充分考虑企业与顾客的收益、成本,采用系统动力学方法建立顾客参与研发过程的动态价值共创模型,全面探究系统内部共创机理。在模型基础上,重点就企业产品价格、交互次数以及激励资金比例等策略进行仿真,使企业、顾客及产品实现价值共创。结论如下:(1)随着顾客、企业交互次数的增多,顾客参与研发过程同样具备一般产品生命周期导入期、成长期、成熟期以及衰退期的特点。(2)系统存在最优交互次数;且最优交互次数随着研发投入比例减少而增加。(3)在不考虑参与顾客获取激励资金的情况下,企业针对不同顾客群(参与研发顾客与普通购买顾客)进行定价,在最优交互次数状态下,统一定价模式优于差别定价模式,将为企业带来更大的收益。