Stochastic optimal budget decision for advertising considering uncertain sales responses

Firms are faced with uncertain sales responses even though they advertise appropriately. To help marketing managers make optimal budget decisions in this situation, we develop a stochastic model, depicting the problem of advertising budget decision as a special Markov decision process where a new objective, maximizing expected market utility, is proposed. In the model we introduce a two-dimension state variable including accumulative sales, which vary randomly with advertising budget, and the predicted probability that an advertising campaign obtains a full sales response. We make an analysis of the model on the premise of growing infinite market potential, deriving the property of optimal policies and that of optimal value function. These results are successfully used to make advertising budget decisions for a private university in Xi’an, China.     

The cost of false alarms in Hellman and rainbow tradeoffs

Cryptanalytic time memory tradeoff algorithms are generic one-way function inversion techniques that utilize pre-computation. Even though the online time complexity is known up to a small multiplicative factor for any tradeoff algorithm, false alarms pose a major obstacle in its accurate assessment. In this work, we study the expected pre-image size for an iteration of functions and use the result to analyze the cost incurred by false alarms. We are able to present the expected online time complexities for the Hellman tradeoff and the rainbow table method in a manner that takes false alarms into account. We also analyze the effects of the checkpoint method in reducing false alarm costs. The ability to accurately compute the online time complexities will allow one to choose their tradeoff parameters more optimally, before starting the expensive pre-computation process.     

Supplier switching decisions

In order to maximize profit, a buying firm should continuously search for and access sources which offer more favorable prices. While the literature is replete with works on the formation and development of buyer–supplier relationships, there is surprisingly only scarce research on the termination of such relationships and supplier switching. Using the concept of switching costs in a principal-agent framework, we at first analyze whether a firm switches the entire or a partial quantity to an alternative supplier when there is either symmetric or asymmetric information about the alternative supplier’s cost structure. Information asymmetry results in inert supplier switching decisions. Subsequently, we extend our model and take competitive reactions of the incumbent supplier and economies of scale effects into consideration. We find conditions under which ‘no’, ‘partial’ and ‘complete’ switching occurs, which depend on the buying firm’s beliefs about the alternative supplier’s unit costs, switching costs, the price offered by the incumbent supplier, and refinements of the price offered by the incumbent supplier due to competitive reactions and economies of scale. Broader implications for supplier relationship management and sourcing strategy decisions are also provided.     

一种智能入侵检测系统设计与模拟实现

针对目前大多数的入侵检测系统存在的局限性,提出一种较完善的入侵检测模型,将专家系统和神经网络技术同时应用于入侵检测系统中.设计专家系统模块检测已知攻击,设计神经网络模块实现未知攻击的检测,提高了检测准确性.同时在神经网络模块应用PCA方法降低入侵数据维数,提高检测效率.仿真实验验证,该设计能有效降低入侵检测系统的漏报率和误报率.     

Cost-based decision-making in middleware virtualization environments

Middleware virtualization refers to the process of running applications on a set of resources (e.g., databases, application servers, other transactional service resources) such that the resource-to-application binding can be changed dynamically on the basis of applications’ resource requirements. Although virtualization is a rapidly growing area, little formal academic or industrial research provides guidelines for cost-optimal allocation strategies. In this work, we study this problem formally. We identify the problem and describe why existing schemes cannot be applied directly. We then formulate a mathematical model describing the business costs of virtualization. We develop runtime models of virtualization decision-making paradigms. We describe the cost implications of various runtime models and consider the cost effects of different managerial decisions and business factors, such as budget changes and changes in demand. Our results yield useful insights for managers in making virtualization decisions.     

Predicting the false alarm rate in multi-institution mortality monitoring

Statistical process control is increasingly used by single hospitals or centres to monitor their performance, but national monitoring across multiple centres, measures and groups incurs higher false alarm rates unless the method is modified. We consider setting the threshold for cumulative sum charts to produce the desired false alarm rate, taking into account the centre volume and expected outcome rate. We used simulation to estimate the false alarm and successful detection rates for a variety of chart thresholds. We thereby calculated the ‘cost’ of a higher threshold compared with one set to give a false alarm rate of 5% for three clinical groups of common interest. The false alarm rate often showed non-linear relations with the threshold, volume and expected mortality rate but an equation was found with good approximation to the simulated values. The relation between these factors and the ‘cost’ of a higher threshold was not straightforward. The ‘cost’ (difference in number of deaths) incurred by raising the chart threshold provides an intuitive measure and is applicable to other settings.     

Multilevel,threshold-based policies for cargo container security screening systems

To mitigate the threat of nuclear terrorism within the US using nuclear material that has been smuggled into the country, the US Bureau of Customs and Border Protection has expanded its cargo container detection capabilities at ports of entry into the US This paper formulates a risk-based screening framework for determining how to define a primary screening alarm for screening cargo containers given a set of dependent primary screening devices. To do so, this paper proposes two linear programming models for screening cargo containers for nuclear material at port security stations using knapsack problem models. All cargo containers undergo primary screening, where they are screened by a given number of security devices. The objective is to identifying the primary security outcomes that warrant a system alarm for each container risk group such that the system detection probability is maximized, subject to a screening budget. The base model is compared to a second model that explicitly requires a threshold-based policy. The structural properties of the two models are compared, which indicates that all risk groups except at most one have deterministic screening policies. A computational example suggests that the detection probability is not significantly altered by enforcing a threshold policy.     

Mean–range based distribution-free procedures to minimize “overage” and “underage” costs

We discuss several mean–range based distribution-free decision procedures to minimize several “overage” and “underage” cost functions. For a general cost function, we identify the most favorable distribution and the least favorable distribution associated with the random variable of interest and determine the upper and lower bounds for the cost function. For the quadratic cost function, we recommend the min–max distribution-free decision. For the linear cost function, we identify the range of potential optimal decisions and recommend a hybrid distribution-free decision that has several desirable properties. We provide several numerical examples to demonstrate the robustness of the proposed distribution-free decisions.     

Interdicting nuclear material on cargo containers using knapsack problem models

This paper introduces a framework for screening cargo containers for nuclear material at security stations throughout the United States using knapsack problem, reliability, and Bayesian probability models. The approach investigates how to define a system alarm given a set of screening devices, and hence, designs and analyzes next-generation security system architectures. Containers that yield a system alarm undergo secondary screening, where more effective and intrusive screening devices are used to further examine containers for nuclear and radiological material. It is assumed that there is a budget for performing secondary screening on containers that yield a system alarm. This paper explores the relationships and tradeoffs between prescreening, secondary screening costs, and the efficacy of radiation detectors. The key contribution of this analysis is that it provides a risk-based framework for determining how to define a system alarm for screening cargo containers given limited screening resources. The analysis suggests that highly accurate prescreening is the most important factor for effective screening, particularly when screening tests are highly dependent, and that moderately accurate prescreening may not be an improvement over treating all cargo containers the same. Moreover, it suggests that screening tests with high true alarm rates may mitigate some of the risk associated with low prescreening intelligence.     

Strategies for detecting fraudulent claims in the automobile insurance industry

Some property and casualty insurers use automated detection systems to help to decide whether or not to investigate claims suspected of fraud. Claim screening systems benefit from the coded experience of previously investigated claims. The embedded detection models typically consist of scoring devices relating fraud indicators to some measure of suspicion of fraud. In practice these scoring models often focus on minimizing the error rate rather than on the cost of (mis)classification. We show that focusing on cost is a profitable approach. We analyse the effects of taking into account information on damages and audit costs early on in the screening process. We discuss several scenarios using real-life data. The findings suggest that with claim amount information available at screening time detection rules can be accommodated to increase expected profits. Our results show the value of cost-sensitive claim fraud screening and provide guidance on how to render this strategy operational.     

Online retailers’ promotional pricing,free-shipping threshold,and inventory decisions: A simulation-based analysis

We consider a two-stage decision problem, in which an online retailer first makes optimal decisions on his profit margin and free-shipping threshold, and then determines his inventory level. We start by developing the retailer’s expected profit function. Then, we use publicly-available statistics to find the best-fitting distribution for consumers’ purchase amounts and the best-fitting function for conversion rate (i.e., probability that an arriving visitor places an online order with the retailer). We show that: (i) a reduction of the profit margin does not significantly affect the standard deviation of consumers’ order sizes (purchase amounts) but increases the average order size; whereas, (ii) variations in a positive finite free-shipping threshold affect both the average value and the standard deviation of the order sizes. We then use Arena to simulate the online retailing system and OptQuest to find the retailer’s optimal decisions and maximum profit. Next, we perform a sensitivity analysis to examine the impact of the ratio of the unit holding and salvage cost to the unit shipping cost on the retailer’s optimal decisions. We also draw some important managerial insights.     

Balancing congestion and security in the presence of strategic applicants with private information

Concerns on security and congestion appear in security screening which is used to identify and deter potential threats (e.g., attackers, terrorists, smugglers, spies) among normal applicants wishing to enter an organization, location, or facility. Generally, in-depth screening reduces the risk of being attacked, but creates delays that may deter normal applicants and thus, decrease the welfare of the approver (authority, manager, screener). In this paper, we develop a model to determine the optimal screening policy to maximize the reward from admitting normal applicants net of the penalty from admitting bad applicants. We use an M/M/1 queueing system to capture the impact of security screening policies on system congestion and use game theory to model strategic behavior, in which potential applicants with private information can decide whether to apply based on the observed approver’s screening policy and the submission behavior of other potential applicants. We provide analytical solutions for the optimal non-discriminatory screening policy and numerical illustrations for both the discriminatory and non-discriminatory policies. In addition, we discuss more complex scenarios including imperfect screening, abandonment behavior of normal applicants, and non-zero waiting costs of attackers.     

Complexity results for the linear time–cost tradeoff problem with multiple milestones and completely ordered jobs

We consider two linear project time–cost tradeoff problems with multiple milestones. Unless a milestone is completed on time, penalty costs for tardiness may be imposed. However, these penalty costs can be avoided by compressing the processing times of certain jobs that require additional resources or costs. Our model describes these penalty costs as the total weighted number of tardy milestone. The first problem tries to minimize the total weighted number of tardy milestones within the budget for total compression costs, while the second problem tries to minimize the total weighted number of tardy milestones plus total compression costs. We develop a linear programming formulation for the case with a fixed number of milestones. For the case with an arbitrary number of milestones, we show that under completely ordered jobs, the first problem is NP-hard in the ordinary sense while the second problem is polynomially solvable.     

Optimal expansion of capacitated transshipment networks

In this paper, we address the problem of allocating a given budget to increase the capacities of arcs in a transshipment network to minimize the cost of flow in the network. The capacity expansion costs of arcs are assumed to be piecewise linear convex functions. We use properties of the optimum solution to convert this problem into a parametric network flow problem. The concept of optimum basis structure is used which allows us to consider piecewise linear convex functions without introducing additional arcs. The resulting algorithm yields an optimum solution of the capacity expansion problem for all budget levels less than or equal to the given budget. For integer data, the algorithm performs almost all computations in integers. Detailed computational results are also presented.     

Contracting with an urgent supplier under cost information asymmetry

We investigate a contract setting problem faced by a manufacturer who can procure major modules from an overseas supplier, as well as a local supplier. The overseas supplier is prime and offers quality products, whereas the local supplier is viewed only as a backup, and its products are inferior in quality. As the local supplier needs to put in additional effort to fulfill the urgent orders, it is difficult for the manufacturer to estimate this urgent supplier’s production cost. This asymmetric cost information becomes an obstacle for the manufacturer in managing the urgent supplier. In this paper, we study two types of contingent contracts. One is the common price-only contract, and the other is a contract menu consisting of a transfer payment and a lead time quotation. We construct a Stackelberg game model and evaluate how the involvement of an urgent supplier with private cost information affects performances of the prime supplier and the manufacturer in different scenarios (with or without the urgent supplier, under different contingent contracts). We also conduct numerical experiments to show how the parameters of the contracts affect profits of the manufacturer.     

Joint spare parts inventory and reliability decisions under a service constraint

Reliability and inventory levels of spare parts are major factors that determine the service level for the maintenance of machines provided by original equipment manufacturers (OEMs). In general, decisions on reliability and stock levels are made separately in practice, and academic literature offers little guidance on how to jointly make these two decisions. In order to fill in the gap in the literature and provide guidance to OEMs, we jointly model reliability and inventory problems. We consider three different service measures: aggregate fill rate, average downtime per system per year and expected total number of long downs in a year. Our models minimize the sum of holding and emergency shipment costs subject to a limited reliability improvement budget and a target service level. We develop an algorithm that considers reliability and inventory decisions simultaneously, test our solution approach on real-life and randomly generated data sets and compare the results with an approach that considers reliability and inventory decisions sequentially. Numerical results show substantial benefits of integrating reliability and inventory decisions.     

Virtual queuing at airport security lanes

Airports continuously seek opportunities to reduce the security costs without negatively affecting passenger satisfaction. In this paper, we investigate the possibilities of implementing virtual queuing at airport security lanes, by offering some passengers a time window during which they can arrive to enter a priority queue. This process could result in a smoother distribution of arriving passengers, such that the required security personnel (costs) can be decreased. While this concept has received attention in a number of settings, such as theme parks, virtual queuing at airports bears an additional level of complexity related to the flight schedules, i.e., passengers can only be transferred forward in time to a limited extent, which we denote by the transfer time limit. We conducted a major simulation study in collaboration with a large international airport in Western Europe to determine the potential impact of virtual queuing and find that nearly one million Euro can be saved on security personnel cost without negatively impacting the passenger waiting time.     

Dynamic realization games in newsvendor inventory centralization

Consider a set N of n (> 1) stores with single-item and single-period nondeterministic demands like in a classic newsvendor setting with holding and penalty costs only. Assume a risk-pooling single-warehouse centralized inventory ordering option. Allocation of costs in the centralized inventory ordering corresponds to modelling it as a cooperative cost game whose players are the stores. It has been shown that when holding and penalty costs are identical for all subsets of stores, the game based on optimal expected costs has a non empty core (Hartman et al. 2000, Games Econ Behav 31:26–49; Muller et al. 2002, Games Econ Behav 38:118–126). In this paper we examine a related inventory centralization game based on demand realizations that has, in general, an empty core even with identical penalty and holding costs (Hartman and Dror 2005, IIE Trans Scheduling Logistics 37:93–107). We propose a repeated cost allocation scheme for dynamic realization games based on allocation processes introduced by Lehrer (2002a, Int J Game Theor 31:341–351). We prove that the cost subsequences of the dynamic realization game process, based on Lehrer’s rules, converge almost surely to either a least square value or the core of the expected game. We extend the above results to more general dynamic cost games and relax the independence hypothesis of the sequence of players’ demands at different stages.     

Allocating fixed costs or resources and setting targets via data envelopment analysis

In many applications of data envelopment analysis (DEA), there is often a fixed cost or input resource which should be imposed on all decision making units (DMUs). Cook and Zhu [W.D. Cook, J. Zhu, Allocation of shared costs among decision making units: A DEA approach, Computers and Operations Research 32 (2005) 2171-2178] propose a practical DEA approach for such allocation problems. In this paper, we prove that when some special constraints are added, Cook and Zhu’s approach probably has no feasible solution. The research of this paper focuses on two main aspects: to obtain a new fixed costs or resources allocation approach by improving Cook and Zhu’s approach, and to set fixed targets according to the amount of fixed resources shared by individual DMUs. When such special constraints are attached, our model is proved to be able to achieve a feasible costs or resources allocation. Numerical results for an example from the literature are presented to illustrate our approach.     

A multi-product continuous review inventory system with stochastic demand,backorders, and a budget constraint

Common characteristics of inventory systems include uncertain demand and restrictions such as budgetary or storage space constraints. Several authors have examined budget constrained multi-item stochastic inventory systems controlled by continuous review policies without considering marginal shortage costs. Existing models assume that purchasing costs are paid at the time an order is placed, which is not always the case since in some systems purchasing costs are paid when orders arrive. In the latter case the maximum investment in inventory is random since the inventory level when an order arrives is a random variable. Hence payment of purchasing costs on delivery yields a stochastic budget constraint for inventory. This paper models a multi-item stochastic inventory system with backordered shortages when estimation of marginal backorder cost is available, and payment is due upon order arrival. The budget constraint can easily be converted into a storage constraint.