Equipment replacement under technological change

For infinite-horizon replacement economy problems it is common practice to truncate the problem at some finite horizon. We develop bounds on the error due to such a truncation. These bounds differ from previous results in that they include both revenues and costs. Bounds are illustrated through a numerical example from a real case in vehicle replacement. © 1994 John Wiley & Sons, Inc.     

Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001     

Dirty war: chemical weapon use and domestic repression

The utilization of chemical weapons to quash domestic rebellion is a drastic action for a regime facing domestic challengers to take, especially given the reputation costs and risk of international intervention. However, recent developments have illustrated that some regimes have contemplated and implemented extraordinary measures (including the use of chemical munitions) to quash rebellion. This study addresses the question of why some states utilize chemical weapons against domestic challengers while others refrain from this level of state repression. I argue that the utilization of chemical weapons has both domestic and international elements. Specifically, that ethnic cleavages that lead to secessionist challenges and factors associated with inter-state rivalry impact the likelihood that a state utilizes the employment of chemical munitions. I test my argument and other explanations regarding repression with a casestudy approach utilizing captured Iraqi Government documents comparing Iraq’s Al-Anfal campaigns with developments during the recent Syrian Civil War.     

Multiobjective flow-shop scheduling

Previous research on the scheduling of multimachine systems has generally focused on the optimization of individual performance measures. This article considers the sequencing of jobs through a multimachine flow shop, where the quality of the resulting schedule is evaluated according to the associated levels of two scheduling criteria, schedule makespan (C_max) and maximum job tardiness (T_max). We present constructive procedures that quantify the trade-off between C_max and T_max. The significance of this trade-off is that the optimal solution for any preference function involving only C_max and T_max must be contained among the set of efficient schedules that comprise the trade-off curve. For the special case of two-machine flow shops, we present an algorithm that identifies the exact set of efficient schedules. Heruistic procedures for approximating the efficient set are also provided for problems involving many jobs or larger flow shops. Computational results are reported for the procedures which indicate that both the number of efficient schedules and the error incurred by heuristically approximating the efficient set are quite small.     

Is human trafficking the dark side of economic freedom?

Economic freedom has increased living standards worldwide. Concurrent with such gains are rising concerns about potential human costs associated with free markets. This paper uses data on human trafficking and anti-trafficking policies, in conjunction with a measure of economic freedom, to examine whether free markets exacerbate or attenuate the incidence of human trafficking and policies designed to combat it. We do not find evidence suggesting that economic freedom is associated with human trafficking. In addition, our results suggest that economically free countries are more likely to enact and enforce policies to fight human trafficking.     

Stepwise inspection in Bayesian multiattribute acceptance sampling

Bayesian models for multiattribute acceptance sampling have been developed under the assumption that sampling inspection is carried to completion. A Bayesian multiattribute model for stepwise sampling inspection is proposed, whereby sampling inspection is terminated as soon as the disposition of the inspection lot is determined. An iterative solution procedure is developed for obtaining optimal or near-optimal multiattribute acceptance sampling plans under stepwise sampling inspection. The effect of stepwise sampling inspection on the characteristics of an optimal sampling plan is investigated. It is shown that stepwise sampling inspection achieves a sampling plan with lower total expected cost than complete sampling inspection. In addition, it is shown that the sequence of attributes in a stepwise sampling inspection substantially affects the sampling plan and resultant expected cost. The proposed methodology is used to evaluate various heuristics which may be used to determine the sequence of attributes in a stepwise inspection procedure.     

A note on optimal pricing for finite capacity queueing systems with multiple customer classes

This article investigates optimal static prices for a finite capacity queueing system serving customers from different classes. We first show that the original multi‐class formulation in which the price for each class is a decision variable can be reformulated as a single dimensional problem with the total load as the decision variable. Using this alternative formulation, we prove an upper bound for the optimal arrival rates for a fairly large class of queueing systems and provide sufficient conditions that ensure the existence of a unique optimal arrival rate vector. We show that these conditions hold for M/M/1/m and M/G/s/s systems and prove structural results on the relationships between the optimal arrival rates and system capacity. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Designing rewards-based crowdfunding campaigns for strategic (but distracted) contributors

In rewards-based crowdfunding, entrepreneurs solicit donations from a large number of individual contributors. If total donations exceed a prespecified funding target, the entrepreneur distributes nonmonetary rewards to contributors; otherwise, their donations are refunded. We study how to design such campaigns when contributors choose not just whether to contribute, but also when to contribute. We show that strategic contribution behavior—when contributors intentionally delay until campaign success is likely—can arise from the combination of nonrefundable (potentially very small) hassle costs and the risk of campaign failure, and can explain pledging patterns commonly observed in crowdfunding. Furthermore, such delays do not hurt the entrepreneur if contributors are perfectly rational, but they do if contributors are distracted, that is, if they might fail to return to the campaign after an intentional delay. To mitigate this, we find that an entrepreneur can use a simple menu of rewards with a fixed number of units sold at a low price, and an unlimited number sold at a higher price; this segments contributors over time based on the information they observe upon arrival. We show that, despite its simplicity, such a menu performs well compared to a theoretically optimal menu consisting of an infinite number of different rewards and price levels under many conditions.