bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge Acquisition from Multiple Experts: An Empirical Study
Authors: O'Leary Daniel E.
Published: November 2008

Expert systems often employ a weight on rules to capture conditional probabilities. For example, in classic rule-based settings, Pr(h ∣ e) = x is used to mean “If e is known to be true then conclude h is true with probability x.” Further, other probability-based approaches, such as influence diagrams and Bayes' Nets are increasingly being used to support decision making through decision support systems. Although algorithms for these systems have received substantial attention, less attention has been given to knowledge acquisition of probabilities used in these systems. However, the underlying probabilities are critical because they lead the user to particular solutions. Accordingly, the purpose of this paper is to investigate the quality of probability knowledge when it is acquired from groups or individuals.

This paper summarizes the results of an empirical cognitive study on the ability of individuals and groups to provide consistent sets of probabilities Pr(A), Pr(B), Pr(A ∣ B) and Pr(B ∣ A). The analysis of these probabilities allowed the study of the ability of subjects to account for Bayes' theorem reversals, a basic assumption made by virtually all algorithms. It was found that knowledge acquisition from groups provided more correct orderings to the probabilities than knowledge acquisition from individuals. This suggests that knowledge acquisition from groups is more likely to obtain correct probability knowledge.
An Optimization Based Heuristic for Political Districting
Authors: Mehrotra Anuj , Johnson Ellis L. , Nemhauser George L.
Published: November 2008

Redistricting, the redrawing of congressional district boundaries within the states, may occur every 10 years on the basis of the population census. Many redistricting plans are designed with partisan politics in mind, resulting in disputes and forcing judges to intervene. We address this problem from a nonpolitical viewpoint and present an optimization based heuristic incorporating universally agreed upon characteristics. We model the problem as a constrained graph partitioning problem and develop a specialized branch-and-price based solution methodology. We demonstrate the feasibility of our methodology by showing how to satisfy the one-person, one-vote principle with compact and contiguous districts for the state of South Carolina.
Computationally Manageable Combinational Auctions
Authors: Rothkopf Michael H. , Pekeč Aleksandar , Harstad Ronald M.
Published: November 2008

There is interest in designing simultaneous auctions for situations such as the recent FCC radio spectrum auctions, in which the value of assets to a bidder depends on which other assets he or she wins. In such auctions, bidders may wish to submit bids for combinations of assets. When this is allowed, the problem of determining the revenue maximizing set of nonconflicting bids can be difficult. We analyze this problem, identifying several different structures of permitted combinational bids for which computational tractability is constructively demonstrated and some structures for which computational tractability cannot be guaranteed.
How Decision Makers Evaluate Alternatives and the Influence of Complexity
Authors: Nutt Paul C.
Published: November 2008

The evaluation of alternatives during organizational decision making was investigated to uncover evaluation tactics used by decision makers and how these tactics and complexity influenced success. Evaluation tactics that relied upon subjective, judgmental, bargaining, and analytical inferences were uncovered from 317 strategic decisions. The complexity of these decisions was measured by the numbers of alternatives considered, number of criteria used, and perceived difficulty of the evaluation task to identify conditions under which the evaluation tactics were successful. The managerial implications of evaluating alternatives with the tactics that used judgmental, bargaining, analytical, and subjective inferences under different levels of complexity are discussed.
The Effect of Property Rights and Audit Information Quality on Team Incentives for Inventory Reduction
Authors: Balakrishnan Ramji , Nagarajan Nandu J. , Sivaramakrishnan K.
Published: November 2008

We analyze how limited contractibility and the informational quality of audits affect inventory levels and the optimality of individual versus team-based production. We use a two-period agency model in which contractibility is limited and agents meet a fixed delivery quota each period. A costly audit is triggered in any period if the delivery quota of output for the period is not met. We show that the informativeness of the audit plays a crucial role in resolving coordination problems between agents when they are organized as a team. When the audit is perfectly informative about agent productivity and inventory levels, team-based production is optimal. The team meets its quota even though, in equilibrium, the audit never takes place. If the audit is not perfectly informative about inventory levels, we show that team-based production typically induces agents to endogenously reduce inventory levels and could even result in agents adopting a zero-inventory policy. When the audit is completely uninformative, individual production is superior to team-based production.
Not Only the Tragedy of the Commons: Misperceptions of Bioeconomics
Authors: Moxnes Erling
Published: November 2008

An exploratory search for explanations of mismanagement of renewable resources, other than the theory of the commons, was performed by an experiment. Eighty three subjects, mostly recruited from the fisheries sector in Norway, were asked to manage the same simulated virgin fish stock, one subject at a time. Exclusive property rights were granted to rule out the commons problem. Despite perfect property rights, subjects consistently overinvested, leading to an average overcapacity of 60%. The resource was reduced by an average of 15% below its optimal level. Overcapacity and tough “quotas” resemble the situation in Norwegian and other fisheries during the past few decades. The likely explanation of the observed behaviour is misperception of feedback, a phenomenon that occurs in many experimental studies of dynamically complex systems. Such misperceptions add a new and important dimension to the problem of renewable resource management, beyond the commons problem.
Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models
Authors: Gönül Füsun , Shi Meng Ze
Published: November 2008

We investigate the key determinants of the optimal direct mail policy in a dynamic environment where customers maximize utility and the direct mailer maximizes profits. We measure the sensitivity of the customers to receiving a catalog in the mail, while controlling for customer characteristics such as elapsed time in responses and number of purchases. We apply our model to a database from a national cataloger that markets nonseasonal products. We summarize the results of our model that are valid for these types of products. We find that the dynamic model significantly outperforms its single-period counterpart. We find that it is not optimal to mail to individuals at low recency levels because they are likely to buy anyway. It is better to save the mailing dollars for customers at higher recency levels. We find that it is optimal to mail to customers who have purchased only a small or a medium number of times to induce them to continue to buy from this catalog and not switch to others. It is not necessary to mail often to customers who have purchased many times before from the company unless they have high recency values. We find that under the optimal mailing policy the cataloguer enjoys higher profits than under the current mailing policy.
Optimal Production Policies for Multi-Stage Systems with Setup Costs and Uncertain Capacities
Authors: Hwang Juhwen , Singh Medini R.
Published: November 2008

The increased complexity of modern manufacturing has led to uncertainties in production processes. Factors such as unplanned machine maintenance, tool unavailability, and complex process adjustments make it difficult to maintain a predictable level of output. To be effective, an appropriate production model must incorporate these uncertainties into the representation of the production process. This paper considers a one-time production of an application-specific product which must follow a fixed routing through the manufacturing system. The flow of items can be modeled as a multi-stage serial production line. The productive capacity is uncertain at each stage and the decision to produce at any stage incurs a significant setup cost. Semifinished products have little value and inability to satisfy the demand incurs a penalty for each unit of unmet demand. We show that the optimal production policy for this system can be characterized by two critical numbers, which can be computed apriori based on the cost parameters and distributional information for all downstream stages. Sensitivity of the critical numbers is also explored.
A Practical Scheduling Method for Multiclass Production Systems with Setups
Authors: Olsen Tava Lennon
Published: November 2008

Consider a multiclass production system where many job classes share a single server and a setup time is incurred whenever the server changes class. This paper presents a simple method for scheduling these systems that performs well, not only with respect to mean waiting time, but also with respect to waiting-time variance and the outer percentiles of waiting time. The scheduling method is dynamic and uses the ages of items in each queue, as well as the queue statistics, to decide which queue to service next.
Are Tradeoffs Inherent in Diversification Moves? A Simultaneous Model for Type of Diversification and Mode of Expansion Decisions
Authors: Chatterjee Sayan , Singh Jagdip
Published: November 2008

Drawing on the premise that the diversification decisions are driven by antecedent factors such as a firm’s existing resources (Teece 1982) and industry structural conditions, this paper develops formal hypotheses for reciprocity between the type of diversification and mode of expansion decisions. We consider the specificity of antecedent resources that affect these two decisions and conceptually demonstrate that there is a contradictory tension in trying to optimize the decisions jointly implying that one or both diversification decisions have to be sub-optimized (i.e., there has to be a trade-off). We make a conceptual argument that this sub-optimization is likely to be in the form of subordination of the mode decision subject to constraints imposed by resources that are highly specific to the mode decision. Following this, we empirically investigate this contradictory tension by using a simultaneous equation model (SEM) on a large sample of firms between 1981 and 1989. The results suggest that one antecedent factor—internal funds—act as the key mediating influence in the joint optimization and leads to a subordination of the mode decision in the joint optimization process. However, the existence of time compression economies and market power benefits are the exceptions to this subordination and trade off process.
Nonlinear Decision Weights in Choice Under Uncertainty
Authors: Wu George , Gonzalez Richard
Published: November 2008

In most real-world decisions, consequences are tied explicitly to the outcome of events. Previous studies of decision making under uncertainty have indicated that the psychological weight attached to an event, called a decision weight, usually differs from the probability of that event. We investigate two sources of nonlinearity of decision weights: subadditivity of probability judgments, and the overweighting of small probabilities and underweighting of medium and large probabilities. These two sources of nonlinearity are combined into a two-stage model of choice under uncertainty. In the first stage, events are taken into subjective probability judgments, and the second stage takes probability judgments into decision weights. We then characterize the curvature of the decision weights by extending a condition employed by Wu and Gonzalez (1996) in the domain of risk to the domain of uncertainty and show that the nonlinearity of decision weights can be decomposed into subadditivity of probability judgments and the curvature of the probability weighting function. Empirical tests support the proposed two-stage model and indicate that decision weights are concave then convex. More specifically, our results lend support for a new property of subjective probability judgments, interior additivity (subadditive at the boundaries, but additive away from the boundaries), and show that the probability weighting function is inverse S-shaped as in Wu and Gonzalez (1996).
Planning Electric Power Systems Under Demand Uncertainty with Different Technology Lead Times
Authors: Gardner Douglas T. , Rogers J. Scott
Published: November 2008

Demand uncertainty is a key concern of electric utility planners. While the greater use of short lead time technologies provides one possible way to deal with this problem, it is not clear how they are best deployed. The approach taken in this paper is to examine a capacity mix model that explicitly accounts for differences in technology lead times. Key results that are obtained include the characterization of the optimal solution and the development of a new set of technology screening criteria. In practice, the “lead time order” (i.e., the set of available technologies ordered by ascending length of lead time) is typically the inverse of the so-called merit order (i.e., the set of available technologies ordered by ascending operating cost). We show that for this case, the optimal solution may be determined with relative ease. A numerical example demonstrates that some short lead time technologies screened out by standard planning methods may enter the optimal solution when differences in lead time are considered, while some long lead time technologies may leave. In addition, the optimal expected level of reliability may be greater.
The Quantity Flexibility Contract and Supplier-Customer Incentives
Authors: Tsay Andy A.
Published: November 2008

Consider a supply chain consisting of two independent agents, a supplier (e.g., a manufacturer) and its customer (e.g., a retailer), the latter in turn serving an uncertain market demand. To reconcile manufacturing/procurement time lags with a need for timely response to the market, such supply chains often must commit resources to production quantities based on forecasted rather than realized demand.

The customer typically provides a planning forecast of its intended purchase, which does not entail commitment. Benefiting from overproduction while not bearing the immediate costs, the customer has incentive to initially overforecast before eventually purchasing a lesser quantity. The supplier must in turn anticipate such behavior in its production quantity decision. This individually rational behavior results in an inefficient supply chain.

This paper models the incentives of the two parties, identifying causes of inefficiency and suggesting remedies. Particular attention is given to the Quantity Flexibility (QF) contract, which couples the customer's commitment to purchase no less than a certain percentage below the forecast with the supplier's guarantee to deliver up to a certain percentage above. Under certain conditions, this method can allocate the costs of market demand uncertainty so as to lead the individually motivated supplier and customer to the systemwide optimal outcome. We characterize the implications of QF contracts for the behavior and performance of both parties, and the supply chain as a whole.
Dynamic R&D Investment Policies
Authors: Childs Paul D. , Triantis Alexander J.
Published: November 2008

This paper examines dynamic R&D investment policies and the valuation of R&D programs in a contingent claims framework. We incorporate the following characteristics of R&D programs into our model: learning-by-doing, collateral learning between different projects in the program, interaction between project cash flows, periodic reevaluations of the program, different intensities of investment, capital rationing constraints, and competition. We show that a firm may invest in multiple projects even if only one can be implemented after development is complete. Furthermore, the firm may significantly alter its funding policy over time. For example, it may simultaneously develop multiple projects for a period of time, then focus on a lead project, and potentially resume funding of a “backup” project if the lead project fails to deliver on its early promise. We show how a firm can forecast expected R&D spending through time for an optimally executed R&D program. While project volatility plays an important role in determining R&D program value, we find that for high volatility projects the optimal investment policy is not very sensitive to changes in (or misestimation of) volatility. In considering whether to accelerate development of a project, a firm should balance the adverse effects of increased costs and the loss of investment flexibility against the positive effects of rapid uncertainty resolution and accelerated cash flows. In the presence of a budget constraint that prevents the firm from simultaneously accelerating projects and developing projects in parallel, we find that, if one project significantly dominates another early in the development stage, the option to accelerate the lead project is likely to be more valuable than the option to exchange projects. Thus, the backup project would be shelved in order to commit extra resources to development of the lead project. Finally, competition from other firms leads to more parallel investment in the early development stages of projects, less parallel investment in the latter stages of development, and lower overall investment.
Flexible and Risk-Sharing Supply Contracts Under Price Uncertainty
Authors: Li Chung-Lun , Kouvelis Panos
Published: November 2008

We study supply contracts for deterministic demand but in an environment of uncertain prices. We develop valuation methodologies for different types of supply contracts. A “time-inflexible contract” requires the firm to specify not only how many units it will purchase, but also the timing of the purchase. A “time-flexible contract” allows the firm to specify the purchase amount over a given period of time without specifying the exact time of purchase. Other than time flexibility, the suppliers may offer “quantity flexibility” to the firm as well, i.e., purchase quantities could be within a prespecified quantity window. Finally, “risk-sharing” features can be incorporated in the contract in terms of the purchase price that the firm eventually pays to a supplier. Within a prespecified price window the firm pays the realized price, but outside of it the firm shares, in an agreed way, added costs or benefits.

Given the structure of a supply contract, we study the firm's decision when to purchase and how many units in each purchase such that the expected net present value of the purchase cost plus inventory holding cost is minimized. We discuss optimal purchasing strategies for both time-flexible and time-inflexible contracts with risk-sharing features. Other interesting results include the analysis of two-supplier sourcing environments and the exploitation of quantity flexibility in such contracts. Our discussion illustrates how time flexibility, quantity flexibility, supplier selection, and risk sharing, when carefully exercised can effectively reduce the sourcing cost in environments of price uncertainty.
Modeling Emergency Supply Flexibility in a Two-Echelon Inventory System
Authors: Alfredsson Patrik , Verrijdt Jos
Published: November 2008

We consider a two-echelon inventory system for service parts. To obtain high service levels at a low cost we allow not only for normal supply of parts but also for emergency supply options in terms of lateral transshipments and direct deliveries. After presenting the strategy we use for satisfying customer demand, we construct an analytical model that we use to calculate relevant performance measures. Simulation shows that our model produces accurate estimates, and that the performance of the inventory system is insensitive to the lead-time distribution. After introducing a cost structure we show that the strategy we propose can result in considerable savings when compared to using only normal supply. Comparison of the results of our model with the results of other models indicates that the combined use of lateral transshipments and direct deliveries can lead to significant cost savings.
Note: Optimal Ordering Decisions with Uncertain Cost and Demand Forecast Updating
Authors: Gurnani Haresh , Tang Christopher S.
Published: November 2008

We determine the optimal ordering policy for a retailer who has two instants to order a seasonal product from a manufacturer prior to a single selling season. While the demand is uncertain, the retailer can improve the forecast by utilizing the market signals observed between the first and second instants. However, because of the nature of the manufacturing environment, the unit cost at the second instant is uncertain and could be higher (or lower) than the unit cost at the first instant. To determine the profit-maximizing ordering strategies at both instants, the retailer has to evaluate the trade-off between a more accurate forecast and a potentially higher unit cost at the second instant. We present a nested newsvendor model for determining the optimal order quantity at each instant and characterize the conditions under which it is optimal for the retailer to delay its order until the second instant.
The Internalization of Exports: Firm- and Location-Specific Factors in a Middle-Income Country
Authors: Campa José , Guillén Mauro F.
Published: November 2008

Firms make strategic choices about foreign market access on the basis of location factors in the home and export countries, as well as on their ownership advantages. The empirical analysis is based on a sample of 837 manufacturing companies in a typical middle-income country (Spain), in which firms are starting to internationalize through investments or alliances in distribution. Following theoretical expectations, the greater the level of such ownership factors as intangible technological assets, product variability, and resource availability, the higher the likelihood of internalization, and in particular internalization by proprietary distribution instead of by commercial alliance. But most importantly, location factors in the home country and in the export market have an independent effect on the likelihood and mode of internalization. Proprietary distribution channels are preferred when the firm's competitors are based in richer countries than the home country, and when the export market is well known to the firm.
A Periodic Review Inventory Model with Demand Influenced by Promotion Decisions
Authors: Cheng Feng , Sethi Suresh P.
Published: November 2008

In this paper, we use a Markov decision process (MDP) to model the joint inventory-promotion decision problem. The state variable of the MDP represents the demand state brought about by changing environmental factors as well as promotion decisions. The demand state in a period determines the distribution of the random demand in that period. Optimal inventory and promotion decision policies in the finite horizon problem are obtained via dynamic programming. Under certain conditions, we show that there is a threshold inventory level P for each demand state such that if the threshold is exceeded, then it is desirable to promote the product. For the proportional ordering cost case, the optimal inventory replenishment policy is a base-stock type policy with the optimal base-stock level dependent on the promotion decision.
The Use of a Neural Factory to Investigate the Effect of Product Line Width on Manufacturing Performance
Authors: Swamidass Paul M. , Nair Satish S. , Mistry Sanjay I.
Published: November 2008

The dual goals of this study are: (1) to develop an empirically valid neural model of U.S. factories in a range of industries producing discrete products, and (2) to use the model to test the effect of changes in product line width on plant performance variables. Accordingly, a neural factory was developed using 59 input and 5 output/performance variables, and was trained using field data collected from 385 U.S. manufacturing plants. The model was validated using a holdout sample before conducting sensitivity tests. The study demonstrates that, through the use of parametric sensitivity analysis, the neural factory could be used to investigate the relationship between inputs and performance of a factory.

While the focused factory principle would favor a smaller product line, economies of scope theory would favor a larger product line for the good of the factory; this implies a rather complex relationship between product line width (PLW) and plant performance. The neural factory was used to study the sensitivity of output/performance variables when product line width was varied over a range extending from 10% to 200% of the average values. The sensitivity analysis of the neural factory shows that, as the product line increases, it (1) does not affect cost-of-goods-sold (COGS), (2) decreases return on investment, (3) has a negative effect on the top management's perception of manufacturing performance, (4) increases inventory turns, and (5) increases sales per employee.

The explanations for these findings show how complex and intertwined the relationships between PLW and performance variables are. They enhance our understanding of PLW and provide some new directions for future empirical research.
DEA Duality on Returns to Scale (RTS) in Production and Cost Analyses: An Occurrence of Multiple Solutions and Differences Between Production-Based and Cost-Based RTS Estimates
Authors: Sueyoshi Toshiyuki
Published: November 2008

This article discusses the concept of RTS (Returns To Scale) in the framework of DEA (Data Envelopment Analysis) production and cost analyses, focusing on an occurrence of multiple solutions and how to deal with such a difficulty. Dual relationships between production-based and cost-based RTS estimates are also theoretically discussed in this study.
Price Versus Production Postponement: Capacity and Competition
Authors: Van Mieghem Jan A. , Dada Maqbool
Published: November 2008

This article presents a comparative analysis of possible postponement strategies in a two-stage decision model where firms make three decisions: capacity investment, production (inventory) quantity, and price. Typically, investments are made while the demand curve is uncertain. The strategies differ in the timing of the operational decisions relative to the realization of uncertainty.

We show how competition, uncertainty, and the timing of operational decisions influence the strategic investment decision of the firm and its value. In contrast to production postponement, price postponement makes the investment and production (inventory) decisions relatively insensitive to uncertainty. This suggests that managers can make optimal capacity decisions by deterministic reasoning if they have some price flexibility. Under price postponement, additional postponement of production has relatively small incremental value. Therefore, it may be worthwhile to consider flexible ex-post pricing before production postponement reengineering. While more postponement increases firm value, it is counterintuitive that this also makes the optimal capacity decision more sensitive to uncertainty. We highlight the different impact of more timely information, which leads to higher investment and inventories, and of reduced demand uncertainty, which decreases investment and inventories. Our analysis suggests appropriateness conditions for simple make-to-stock and make-to-order strategies. We also present technical sufficiency and uniqueness conditions. Under price postponement, these results extend to oligopolistic and perfect competition for which pure equilibria are derived. Interestingly, the relative value of operational postponement techniques seems to increase as the industry becomes more competitive.
94%-Effective Policies for a Two-Stage Serial Inventory System with Stochastic Demand
Authors: Chen Fangruo
Published: November 2008

A two-stage inventory system is considered where Poisson demand occurs at Stage 1, and Stage 1 replenishes its inventory from Stage 2, which in turn orders from an outside supplier with unlimited stock. Each shipment, either to Stage 2 or to Stage 1, incurs a fixed setup cost. Under the assumption that the supply leadtime at Stage 2 is zero, we characterize a simple heuristic policy whose long-run average cost is guaranteed to be within 6% of optimality, i.e., a 94%-effective policy. The paper also provides heuristic policies for more general inventory systems and reports computational results.
A Punctuated-Equilibrium Model of Technology Diffusion
Authors: Loch Christoph H. , Huberman Bernardo A.
Published: November 2008

We present an evolutionary model of technology diffusion in which an old and a new technology are available, both of which improve their performance incrementally over time. Technology adopters make repeated choices between the established and the new technology based on their perceived performance, which is subject to uncertainty. Both technologies exhibit positive externalities, or performance benefits from others using the same technology.

We find that the superior technology will not necessarily be broadly adopted by the population. Externalities cause two stable usage equilibria to exist, one with the old technology being the standard and the other with the new technology the standard. Punctuations, or sudden shifts, in these equilibria determine the patterns of technology diffusion. The time for an equilibrium punctuation depends on the rate of incremental improvement of both technologies, and on the system’s resistance to switching between equilibria. If the new technology has a higher rate of incremental improvement, it is adopted faster, and adoption may precede performance parity if the system’s resistance to switching is low. Adoption of the new technology may trail performance parity if the system’s resistance to switching is high.
Improving Service by Informing Customers About Anticipated Delays
Authors: Whitt Ward
Published: November 2008

This paper investigates the effect upon performance in a service system, such as a telephone call center, of giving waiting customers state information. In particular, the paper studies two M/M/s/r queueing models with balking and reneging. For simplicity, it is assumed that each customer is willing to wait a fixed time before beginning service. However, customers differ, so the delay tolerances for successive customers are random. In particular, it is assumed that the delay tolerance of each customer is zero with probability β, and is exponentially distributed with mean α−1 conditional on the delay tolerance being positive. Let N be the number of customers found by an arrival. In Model 1, no state information is provided, so that if N ≥ s, the customer balks with probability β; if the customer enters the system, he reneges after an exponentially distributed time with mean α−1 if he has not begun service by that time. In Model 2, if N = s + k ≥ s, then the customer is told the system state k and the remaining service times of all customers in the system, so that he balks with probability β + (1 − β)(1 − qk), where qk = P(T > Sk), T is exponentially distributed with mean α−1, Sk is the sum of k + 1 independent exponential random variables each with mean (sμ)−1, and μ−1 is the mean service time. In Model 2, all reneging is replaced by balking. The number of customers in the system for Model 1 is shown to be larger than that for Model 2 in the likelihood-ratio stochastic ordering. Thus, customers are more likely to be blocked in Model 1 and are more likely to be served without waiting in Model 2. Algorithms are also developed for computing important performance measures in these, and more general, birth-and-death models.
Correlations and Copulas for Decision and Risk Analysis
Authors: Clemen Robert T. , Reilly Terence
Published: November 2008

The construction of a probabilistic model is a key step in most decision and risk analyses. Typically this is done by defining a joint distribution in terms of marginal and conditional distributions for the model's random variables. We describe an alternative approach that uses a copula to construct joint distributions and pairwise correlations to incorporate dependence among the variables. The approach is designed specifically to permit the use of an expert's subjective judgments of marginal distributions and correlations. The copula that underlies the multivariate normal distribution provides the basis for modeling dependence, but arbitrary marginals are allowed. We discuss how correlations can be assessed using techniques that are familiar to decision analysts, and we report the results of an empirical study of the accuracy of the assessment methods. The approach is demonstrated in the context of a simple example, including a study of the sensitivity of the results to the assessed correlations.
Optimal Lead Time Policies
Authors: Spearman Mark L. , Zhang Rachel Q.
Published: November 2008

This paper examines two due-date setting problems first studied by Wein (1991). The first problem seeks to minimize the average due-date lead time (due-date minus arrival date) of jobs subject to a constraint on the fraction of tardy jobs (Problem I) while the second uses the same objective subject to a constraint on average job tardiness (Problem II). We show that under very general conditions, Problem I leads to unethical practice (i.e., quote lead times for which there is no hope to achieve when the system is highly congested) while Problem II results in policies that quote lead times that are monotonically increasing with the congestion level. Furthermore, we prove that Problem II is equivalent to a policy that is widely used and is easy to compute. This policy quotes lead times that guarantee the same serviceability level (the fraction of tardy jobs) to all jobs.
Stalking Information: Bayesian Inventory Management with Unobserved Lost Sales
Authors: Lariviere Martin A. , Porteus Evan L.
Published: November 2008

Retailers are frequently uncertain about the underlying demand distribution of a new product. When taking the empirical Bayesian approach of Scarf (1959), they simultaneously stock the product over time and learn about the distribution. Assuming that unmet demand is lost and unobserved, this learning must be based on observing sales rather than demand, which differs from sales in the event of a stockout. Using the framework and results of Braden and Freimer (1991), the cumulative learning about the underlying demand distribution is captured by two parameters, a scale parameter that reflects the predicted size of the underlying market, and a shape parameter that indicates both the size of the market and the precision with which the underlying distribution is known. An important simplification result of Scarf (1960) and Azoury (1985), which allows the scale parameter to be removed from the optimization, is shown to extend to this setting. We present examples that reveal two interesting phenomena: (1) A retailer may hope that, compared to stocking out, realized demand will be strictly less than the stock level, even though stocking out would signal a stochastically larger demand distribution, and (2) it can be optimal to drop a product after a period of successful sales. We also present specific conditions under which the following results hold: (1) Investment in excess stocks to enhance learning will occur in every dynamic problem, and (2) a product is never dropped after a period of poor sales. The model is extended to multiple independent markets whose distributions depend proportionately on a single unknown parameter. We argue that smaller markets should be given better service as an effective means of acquiring information.
Risk Aversion or Myopia? Choices in Repeated Gambles and Retirement Investments
Authors: Benartzi Shlomo , Thaler Richard H.
Published: November 2008

We study how decision makers choose when faced with multiple plays of a gamble or investment. When evaluating multiple plays of a simple mixed gamble, a chance to win x or lose y, subjects show a sensitivity to the amount to lose on a single trial, holding the distribution of returns for the portfolio constant; that is, they display “myopic loss aversion.” Many subjects who decline multiple plays of such a gamble will accept it when shown the resulting distribution. This analysis is applied to the problem of retirement investing. We show that workers invest more of their retirement savings in stocks if they are shown long-term (rather than one-year) rates of return.
Efficient Design and Sensitivity Analysis of Control Charts Using Monte Carlo Simulation
Authors: Fu Michael C. , Hu Jian-Qiang
Published: November 2008

The design of control charts in statistical quality control addresses the optimal selection of the design parameters (such as the sampling frequency and the control limits) and includes sensitivity analysis with respect to system parameters (such as the various process parameters and the economic costs of sampling). The advent of more complicated control chart schemes has necessitated the use of Monte Carlo simulation in the design process, especially in the evaluation of performance measures such as average run length. In this paper, we apply two gradient estimation procedures—perturbation analysis and the likelihood ratio/score function method—to derive estimators that can be used in gradient-based optimization algorithms and in sensitivity analysis when Monte Carlo simulation is employed. We illustrate the techniques on a general control chart that includes the Shewhart chart and the exponentially-weighted moving average chart as special cases. Simulation examples comparing the estimators with each other and with “brute force” finite differences demonstrate the possibility of significant variance reduction in settings of practical interest.
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Authors: Martello Silvano , Pisinger David , Toth Paolo
Published: November 2008

Two new algorithms recently proved to outperform all previous methods for the exact solution of the 0-1 Knapsack Problem. This paper presents a combination of such approaches, where, in addition, valid inequalities are generated and surrogate relaxed, and a new initial core problem is adopted. The algorithm is able to solve all classical test instances, with up to 10,000 variables, in less than 0.2 seconds on a HP9000-735/99 computer. The C language implementation of the algorithm is available on the internet.
A Multiserver Queueing System with Impatient Customers
Authors: Boots Nam Kyoo , Tijms Henk
Published: November 2008

Many real-world situations involve queueing systems in which customers wait for service for a limited time only and leave the system if service has not begun within that time. This paper considers a multiserver queueing system with impatient customers, where the customers arrive according to a Poisson process and the service requirements have a general distribution. A simple and insightful solution is presented for the loss probability. The solution is exact for exponential services and is an excellent heuristic for general service times.
Inventory Reduction and Productivity Growth: Linkages in the Japanese Automotive Industry
Authors: Lieberman Marvin B. , Demeester Lieven
Published: November 2008

The literature on JIT production suggests a causal link between work-in-process inventory and manufacturing productivity. Such a connection has been described in numerous case studies but never tested statistically. This paper uses historical data for 52 Japanese automotive companies to evaluate the inventory-productivity relationship. We find that firms increased their productivity rank during periods of substantial inventory reduction. More detailed tests suggest that inventory reductions stimulated gains in productivity: On average, each 10% reduction in inventory led to about a 1% gain in labor productivity, with a lag of about one year. Such effects were more immediate for Toyota affiliates, but undetectable for close suppliers of Nissan. These findings imply that inventory reduction served as an important driver of process improvement for many Japanese automotive companies, although some firms emphasized other methods.
Variety Seeking, Purchase Timing, and the “Lightning Bolt” Brand Choice Model
Authors: Chintagunta Pradeep K.
Published: November 2008

The “Lightning Bolt” (LB) model provides a comprehensive framework for accommodating the effects of habit persistence, unobserved heterogeneity, and state dependence on household brand choice behavior. This paper presents a discrete, dynamic brand-choice model that belongs to the LB class of models. We propose a method for incorporating the effects of variety seeking into the LB model formulation. The proposed formulation explicitly links brand choice and purchase timing behavior via the effect of state dependence. This state-dependence based linkage between brand choice and purchase timing comes about due to the attribute satiation notion associated with household variety seeking behavior. Empirical implementation of the model specification requires recognizing that interpurchase times, like brand choices, also depend upon marketing variables and household characteristics. A hazard model is specified to capture this relationship.

Empirical results are presented using household-level data for two different products. Our results reveal the following. (1) State dependence effects decline over time. Hence, households' brand switching and repeat purchase probabilities vary over time, independent of any variation in marketing mix activities. (2) Accounting for the effects of marketing variables on interpurchase times may be important when empirically estimating the proposed model. While the nature of substantive implications is largely unchanged, the magnitudes of the different effects are nevertheless affected. (3) We find evidence for purchase reinforcement (and its effects declining over time), but no evidence for attribute satiation based variety seeking behavior. (4) A comparison of the model's predictive ability with those from two extant specifications reveals that the proposed model outperforms rival specifications.
Measures of Perceived Risk
Authors: Jia Jianmin , Dyer James S. , Butler John C.
Published: November 2008

Based on our previous work on the standard measure of risk, this paper presents two classes of measures for perceived risk by decomposing a lottery into its mean and standard risk. One of the classes of our risk measures presumes that there is no risk when there is no uncertainty involved, and the other allows different degenerate lotteries to be evaluated with different values of “risk.” The former has more prescriptive appeal in risky decision making, but the latter may have more descriptive power for subjective risk judgments. Our risk measures can also take into account the asymmetric effects of losses and gains on perceived risk based on an appropriate choice of the standard measure of risk. The perceived risk models we propose unify a large body of empirical evidence regarding risk judgments, and provide sufficient flexibility to better capture people's perceptions of risk than previously developed risk models. In particular, our risk measures provide clear ways to accommodate financial measures of risk and psychological measures of risk, and they can be incorporated into preference models in an appealing form based on mean-risk tradeoffs.
The Value of Internet Commerce to the Customer
Authors: Keeney Ralph L.
Published: November 2008

Internet commerce has the potential to offer customers a better deal compared to purchases by conventional methods in many situations. To make this potential a reality, businesses must focus on the values of their customers. We interviewed over one-hundred individuals about all the pros and cons of using Internet commerce that they experienced or envisioned. The results were organized into twenty-five categories of objectives that were influenced by Internet purchases. These categories were separated into means objectives and fundamental objectives used to describe the bottom line consequences of concern to customers. These results are applicable to designing an Internet commerce system for a business, creating and redesigning products, and increasing value to customers. The set of fundamental objectives also provides the foundation for developing a quantitative model of customer values.
Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning
Authors: Das Tapas K. , Gosavi Abhijit , Mahadevan Sridhar , Marchalleck Nicholas
Published: November 2008

A large class of problems of sequential decision making under uncertainty, of which the underlying probability structure is a Markov process, can be modeled as stochastic dynamic programs (referred to, in general, as Markov decision problems or MDPs). However, the computational complexity of the classical MDP algorithms, such as value iteration and policy iteration, is prohibitive and can grow intractably with the size of the problem and its related data. Furthermore, these techniques require for each action the one step transition probability and reward matrices, and obtaining these is often unrealistic for large and complex systems. Recently, there has been much interest in a simulation-based stochastic approximation framework called reinforcement learning (RL), for computing near optimal policies for MDPs. RL has been successfully applied to very large problems, such as elevator scheduling, and dynamic channel allocation of cellular telephone systems.

In this paper, we extend RL to a more general class of decision tasks that are referred to as semi-Markov decision problems (SMDPs). In particular, we focus on SMDPs under the average-reward criterion. We present a new model-free RL algorithm called SMART (Semi-Markov Average Reward Technique). We present a detailed study of this algorithm on a combinatorially large problem of determining the optimal preventive maintenance schedule of a production inventory system. Numerical results from both the theoretical model and the RL algorithm are presented and compared.
Batch Construction Heuristics and Storage Assignment Strategies for Walk/Ride and Pick Systems
Authors: Ruben Robert A. , Jacobs F. Robert
Published: November 2008

The retrieval of items from storage is a chore faced by virtually every business concern. Walk/ride and pick systems are a popular method for performing this chore in many applications. When retrieval requests consist of multiple items, order pickers must travel to numerous storage locations to complete each order. When these requests are made in sufficient volume, a method of order retrieval known as batch picking is often used. Batch picking is a popular approach in the mail-order industry and is employed in industrial settings such as tool cribs and in warehouses, which service assembly line operations.

Batch construction heuristics are developed and tested under three strategies for assigning storage space to individual items that have been suggested in the literature. Our results are derived from the simulation of a single hypothetical warehouse model. Specifically, we employ a model that is square in travel distance and assume the walk and pick method of order retrieval with sequential one-way travel. Sensitivity analysis is performed on workforce level and batch size. Our results indicate that the methods used for constructing batches of orders and for assigning storage space to individual items can significantly impact order retrieval efforts in warehouses of this type.