Optimality of the threshold dividend strategy for the compound Poisson model

In this paper, we consider the optimal dividend problem for the compound Poisson risk model. We assume that dividends are paid to the shareholders according to an admissible strategy with dividend rate bounded by a constant. Our objective is to find a dividend policy so as to maximize the expected discounted value of dividends until ruin. We give sufficient conditions under which the optimal strategy is of threshold type.     

Optimal dividend payments in the stochastic Ramsey model

We consider the dividend payments of a self-financing firm in the stochastic Ramsey model. The firm invests in capital stock and its production technology is given by the Cobb–Douglas function. Our objective is to maximize the expected present value of future real dividends subject to a positive constraint on the capital stock. We use the penalization method to obtain a solution for the variational inequality associated with the optimal growth problem and give a synthesis of the optimal dividend policy.     

A tactical model for planning the production and distribution of fresh produce

We present an integrated tactical planning model for the production and distribution of fresh produce. The main objective of the model is to maximize the revenues of a producer that has some control over the logistics decisions associated with the distribution of the crop. The model is used for making planning decisions for a large fresh produce grower in Northwestern Mexico. The decisions obtained are based on traditional factors such as price estimation and resource availability, but also on factors that are usually neglected in traditional planning models such as price dynamics, product decay, transportation and inventory costs. The model considers the perishability of the crops in two different ways, as a loss function in its objective function, and as a constraint for the storage of products. The paper presents a mixed integer programming model used to implement the problem as wells as the computational results obtained from it.     

On the optimality of threshold control in queues with model uncertainty

We consider a single-stage queuing system where arrivals and departures are modeled by point processes with stochastic intensities. An arrival incurs a cost, while a departure earns a revenue. The objective is to maximize the profit by controlling the intensities subject to capacity limits and holding costs. When the stochastic model for arrival and departure processes are completely known, then a threshold policy is known to be optimal. Many times arrival and departure processes can not be accurately modeled and controlled due to lack of sufficient calibration data or inaccurate assumptions. We prove that a threshold policy is optimal under a max–min robust model when the uncertainty in the processes is characterized by relative entropy. Our model generalizes the standard notion of relative entropy to account for different levels of model uncertainty in arrival and departure processes. We also study the impact of uncertainty levels on the optimal threshold control.     

带注资的二维复合泊松模型的最优分红

研究建立两类理赔关系的二维复合泊松模型的最优分红与注资问题,目标为最大化分红减注资的折现. 该问题由随机控制问题刻画, 通过解相应的哈密尔顿-雅克比-贝尔曼(HJB)方程,得到了最优分红策略,并在指数理赔时明确地解决该问题.     

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??In this paper, we investigate a robust optimal portfolio and reinsurance problem under inflation risk for an ambiguity-averse insurer (AAI), who worries about uncertainty in model parameters. We assume that the AAI is allowed to purchase proportional reinsurance and invest his/her wealth in a financial market which consists of a risk-free asset and a risky asset. The objective of the AAI is to maximize the minimal expected power utility of terminal wealth. By using techniques of stochastic control theory, closed-form expressions for the value function and optimal strategies are obtained.     

Optimal dividend-financing strategies in a dual risk model with time-inconsistent preferences

In this paper, we consider an optimal dividend-financing problem for a company whose capital reserve is described by the dual of classical risk model. We assume that the manager of the company has time-inconsistent preferences, which are described by a quasi-hyperbolic discount function, and that financing is permitted to prevent the company from going bankrupt. The manager’s objective is to maximize the expected cumulative dividend payments minus financing costs. We solve the optimization problems for a naive manager and a sophisticated manager, and obtain explicit solutions for both managers. Our results show that the manager with time-inconsistent preferences tends to pay out dividends earlier. We also present some economic implications and sensitivity analysis for our results.     

The optimal resource portfolio under consumer choice and demand risk for vertically differentiated products

We study the optimal resource portfolio of a firm that sells two vertically differentiated products and utilizes resource flexibility and responsive pricing. We model this decision problem as a two-stage stochastic programming problem with recourse: In the first stage, the firm determines its resource mix and capacities so as to maximize the expected profit under demand uncertainty; in the second stage, uncertainty is resolved and the firm determines its production and pricing decision, constrained by its investment decision. We show that the objective function of this decision problem is not well-behaved (ie, it may have multiple local maxima). Using the concept of Pareto dominance, we reduce the feasible investment region, without loss of optimality, to one in which the objective function is well-behaved everywhere. This reduction allows us to derive the necessary and sufficient conditions for the optimal capacity decision and to gain insights.     

Jointly pricing and ordering for a multi-product multi-constraint newsvendor problem with supplier quantity discounts

We present an extension to the multi-product newsvendor problem by incorporating the retailer’s pricing decision as well as considering supplier quantity discount. The objective is to maximize the expected profit of the retailer through jointly determining the ordering quantities and selling prices for the products, subject to multiple capacity constraints. We formulate the problem as a Generalized Disjunctive Programming (GDP) model and develop a Lagrangian heuristic approach for its solution. Randomly produced instances involving up to 1000 products are used to test the proposed approach. Computational results show that the Lagrangian heuristic approach can present very good solutions to all instances in reasonable time.     

A linear programming approach for determining optimal advertising policy

^** Email: wkc{at}maths.hku.hk In this paper, we propose a new advertising model which cancapture the advertising wear out phenomenon. The objective hereis to maximize overall sales. We show how to derive the optimalpulsation advertising strategy. The optimization problem canbe formulated as a linear programming problem. Closed-form optimalsolution can also be obtained under some conditions. We presentnumerical examples to illustrate the proposed model and applythe model to practical sales data.     

Optimal selection from distributions with unknown parameters: Robustness of Bayesian models

We consider the problem of making one choice from a known number of i.i.d. alternatives. It is assumed that the distribution of the alternatives has some unknown parameter. We follow a Bayesian approach to maximize the discounted expected value of the chosen alternative minus the costs for the observations. For the case of gamma and normal distribution we investigate the sensitivity of the solution with respect to the prior distributions. Our main objective is to derive monotonicity and continuity results for the dependence on parameters of the prior distributions. Thus we prove some sort of Bayesian robustness of the model.     

A Markov decision problem in a risk model with interest rate and Markovian environment

We consider the compound binomial model in a Markovian environment presented by Cossette et al.(2004). We modify the model via assuming that the company receives interest on the surplus and a positive real-valued premium per unit time, and introducing a control strategy of periodic dividend payments. A Markov decision problem arises and the control objective is to maximize the cumulative expected discounted dividends paid to the shareholders until ruin minus a discounted penalty for ruin. We show that under the absence of a ceiling of dividend rates the optimal strategy is a conditional band strategy given the current state of the environment process. Under the presence of a ceiling for dividend rates, the character of the optimal control strategy is given. In addition, we offer an algorithm for the optimal strategy and the optimal value function.Numerical results are provided to illustrate the algorithm and the impact of the penalty.     

A network location-allocation model trading off flow capturing andp-median objectives

The flow capturing and thep-median location—allocation models deal quite differently with demand for service in a network. Thep-median model assumes that demand is expressed at nodes and locates facilities to minimize the total distance between such demand nodes and the nearest facility. The flow-capturing model assumes that demand is expressed on links and locates facilities to maximize the one-time exposure of such traffic to facilities. Demand in a network is often of both types: it is expressed by passing flows and by consumers centred in residential areas, aggregated as nodes. We here present a hybrid model with the dual objective of serving both types of demand. We use this model to examine the tradeoff between serving the two types of demand in a small test network using synthetic demand data. A major result is the counter-intuitive finding that thep-median model is more susceptible to impairment by the flow capturing objective than is the flow capturing model to thep-median objective. The results encourage us to apply the model to a real-world network using actual traffic data.     

Utility Maximization with Convex Constraints and Partial Information

We consider a market model where stock returns satisfy a stochastic differential equation with an unobservable, stochastic drift process. The investor’s objective is to maximize expected utility of terminal wealth, but investment decisions are based on the knowledge of the stock prices only. The performance of the resulting highly risky strategies can be improved considerably by imposing convex constraints covering e.g. short selling restrictions. Using filtering methods we transform the model to a model with full information. We provide a verification result and show how results on optimization under convex constraints can be used directly for a continuous time Markov chain model for the drift. In special cases we derive representations of the optimal trading strategies, including a stochastic volatility model. Supported by the Austrian Science Fund, FWF grant P17947-N12.     

Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain

We present a solution framework based on discrete-event simulation and enhanced robust design technique to address a multi-response optimization problem inherent in logistics management. The objective is to design a robust configuration for a cross-docking distribution center so that the system is insensitive to the disturbances of supply uncertainty, and provides steady parts supply to downstream assembly plants. In the proposed approach, we first construct a simulation model using factorial design and central composite design (CCD), and then identify the models that best describe the relationship between the simulation responses and system factors. We employ the response surface methodology (RSM) to identify factor levels that would maximize system potential.     

Assigning cross-trained workers to departments: A two-stage optimization model to maximize utility and skill improvement

We develop a general framework that is applicable in both manufacturing and service settings for assigning cross-trained workers across departments. The framework consists of a two-stage optimization model where two objective functions, departmental utility and skill improvement, are considered sequentially. Departmental utility is a function of departmental labor shortage and the first-stage optimization model maximizes total departmental utility subject to typical assignment constraints. The second stage model seeks to maximize total skill improvement, which is quantified by a hyperbolic learning curve, while trying not to deviate from the utility level obtained during the first stage optimization. Our computational experiments suggest that incorporating the skill improvement function explicitly in the model results in significant improvement in the total skill level of the workforce and thus leads to more effective worker assignments.     

占线订单排序C——收益函数下改进的随机策略

对于订单具有紧交货期限且以最大化完工总收益为目标的占线订单排序问题，Woeginger（1994）提出了完工收益与订单长度满足C——收益函数关系的一类模型，并给出了竞争比为4的最优确定性策略。本文针对该模型设计了一个简单的随机策略，并证明其具有竞争比2。该策略明显简单于已有的各种随机策略；同时，本文结论大大改进了Seiden（1998）所给出的当前最好竞争比3．732。     

Optimal investment policy of an insurance firm

In this paper we consider an investment problem by an insurance firm. As in the classical model of collective risk, it is assumed that premium payments are received deterministically from policyholders at a constant rate, while the claim process is determined by a compound Poisson process. We introduce a conversion mechanism of funds from cash into investments and vice versa. Contrary to the conventional collective risk model we do not assume a ruin barrier. Instead we introduce conversion costs to account for the problems implicit in reaching the zero boundary. The objective of the firm is to maximize its net profit by selecting an appropriate investment strategy. A diffusion approximation is suggested in order to obtain tractable results for a general claim size distribution.     

Multiple bottleneck assignment problem

We consider the multiple bottleneck assignment problem which subsumes the well known min-sum and bottleneck assignment problems. The problem arises in the context of flexible manufacturing systems, where the objective is to maximize the throughput of a production system with several flow shops, running in parallel, to produce a product. The problem is known to be strongly NP-hard. We propose a new algorithm for obtaining sharp lower bounds to the optimal objective value. Computational experiments are conducted to show the improvement over existing methods.     

基于战略导向的项目组合选择优化研究

本文研究了以项目组合的选择实现组织战略目标最大化的问题,将战略目标分解为收益、成本和风险目标,运用模糊集截集原理和目标标准法则把战略目标整合为单个的权衡目标。用梯形模糊数表示项目的不确定参数,并考虑项目间的相互影响关系,建立了基于战略目标的项目组合选择模型,来实现项目组合选择与战略目标的一致。并针对模型提出了遗传算法进行求解,用示例验证了方法的有效性。