Aggregating courier deliveries

We consider the problem of efficiently scheduling deliveries by an uncapacitated courier from a central location under online arrivals. We consider both adversary‐controlled and Poisson arrival processes. In the adversarial setting we provide a randomized (3βΔ/2δ ? 1) ‐competitive algorithm, where β is the approximation ratio of the traveling salesman problem, δ is the minimum distance between the central location and any customer, and Δ is the length of the optimal traveling salesman tour overall customer locations and the central location. We provide instances showing that this analysis is tight. We also prove a 1 + 0.271Δ/δ lower‐bound on the competitive ratio of any algorithm in this setting. In the Poisson setting, we relax our assumption of deterministic travel times by assuming that travel times are distributed with a mean equal to the excursion length. We prove that optimal policies in this setting follow a threshold structure and describe this structure. For the half‐line metric space we bound the performance of the randomized algorithm in the Poisson setting, and show through numerical experiments that the performance of the algorithm is often much better than this bound.     

Combining standardized time series area and Cramér–von Mises variance estimators

We propose three related estimators for the variance parameter arising from a steady‐state simulation process. All are based on combinations of standardized‐time‐series area and Cramér–von Mises (CvM) estimators. The first is a straightforward linear combination of the area and CvM estimators; the second resembles a Durbin–Watson statistic; and the third is related to a jackknifed version of the first. The main derivations yield analytical expressions for the bias and variance of the new estimators. These results show that the new estimators often perform better than the pure area, pure CvM, and benchmark nonoverlapping and overlapping batch means estimators, especially in terms of variance and mean squared error. We also give exact and Monte Carlo examples illustrating our findings.© 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

A new analysis of a lot-size model with partial backlogging

We reformulate the cost equation for the lot-size model with partial back-logging. The formulation is in terms of “fictitious demand rate,” a new inventory decision variable that simplifies the analysis. Using decomposition by projection, we obtain an optimal solution in a straightforward manner. The form of the solution sheds additional light on the behavior of the model. Some of these insights are elucidated by numerical examples.     

An early-accept modification to the test plans of military standard 781C

This paper is concerned with the statistical test plans contained in Military Standard 781C, “Reliability Design Qualification and Production Acceptance Tests: Exponential Distribution” and the selection and use of these plans. Modifications to the fixed-length test plans of MIL-STD-781C are presented which allow early-accept decisions to be made without sacrificing statistical validity. The proposed plans differ from the probability ratio sequential tests in the Standard in that rejection is permitted only after a fixed number of failures have been observed.     

The Economic Effect of Military Facility Contraction: A Naval Case Study

The global financial turmoil of 2008 has resulted in the curtailment of military expenditure in most western countries. At a sub-regional-level reductions in the level of activity at a major military facility can have significant economic impact. In the light of this, the paper has two objectives: to analyse the impact of the decision to terminate naval shipbuilding at the United Kingdom’s Portsmouth Naval Base; and, for illustrative purposes, to examine the possible economic consequences of further contraction at the facility. In pursuit of these aims, it is necessary to establish the output, income and employment generated by the base using a bespoke input–output model. The methodology employed can, with appropriate adjustments, be utilised in other military or civilian contexts.     

Counterinsurgency as armed reform: The political history of the Malayan Emergency

Despite the emphasis in doctrine and academia that counterinsurgency is in its essence political, these operations are all too commonly discussed and approached as primarily military endeavors. Informed by the need to refocus counterinsurgency studies, this article revisits a foundational case of the canon – the Malayan Emergency – to discuss its political (i.e., not military) unfolding. The analysis distinguishes itself by emphasizing the diplomatic processes, negotiations, and deals that gave strategic meaning to the military operations underway. In so doing, the article also generates insight on the use of leverage and elite bargains in creating new political settlements and bringing insurgent conflicts to an end.     

Balancing stability and efficiency in team formation as a generalized roommate problem

The assignment of personnel to teams is a fundamental managerial function typically involving several objectives and a variety of idiosyncratic practical constraints. Despite the prevalence of this task in practice, the process is seldom approached as an optimization problem over the reported preferences of all agents. This is due in part to the underlying computational complexity that occurs when intra-team interpersonal interactions are taken into consideration, and also due to game-theoretic considerations, when those taking part in the process are self-interested agents. Variants of this fundamental decision problem arise in a number of settings, including, for example, human resources and project management, military platooning, ride sharing, data clustering, and in assigning students to group projects. In this article, we study an analytical approach to “team formation” focused on the interplay between two of the most common objectives considered in the related literature: economic efficiency (i.e., the maximization of social welfare) and game-theoretic stability (e.g., finding a core solution when one exists). With a weighted objective across these two goals, the problem is modeled as a bi-level binary optimization problem, and transformed into a single-level, exponentially sized binary integer program. We then devise a branch-cut-and-price algorithm and demonstrate its efficacy through an extensive set of simulations, with favorable comparisons to other algorithms from the literature.