Optimal resource allocation and redistribution strategy in military conflicts with Lanchester square law attrition

We address the problem of optimal decision‐making in conflicts based on Lanchester square law attrition model where a defending force needs to be partitioned optimally, and allocated to two different attacking forces of differing strengths and capabilities. We consider a resource allocation scheme called the Time Zero Allocation with Redistribution (TZAR) strategy, where allocation is followed by redistribution of defending forces, on the occurrence of certain decisive events. Unlike previous work on Lanchester attrition model based tactical decision‐making, which propose time sequential tactics through an optimal control approach, the present article focuses on obtaining simpler resource allocation tactics based on a static optimization framework, and demonstrates that the results obtained are similar to those obtained by the more complex dynamic optimal control solution. Complete solution for this strategy is obtained for optimal partitioning of resources of the defending forces. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Modeling and analysis of uncertain time‐critical tasking problems

This paper describes modeling and operational analysis of a generic asymmetric service‐system situation in which (a) Red agents, potentially threatening, but in another but important interpretation, are isolated friendlies, such as downed pilots, that require assistance and “arrive” according to some partially known and potentially changing pattern in time and space; and (b) Reds have effectively limited unknown deadlines or times of availability for Blue service, i.e., detection, classification, and attack in a military setting or emergency assistance in others. We discuss various service options by Blue service agents and devise several approximations allowing one to compute efficiently those proportions of tasks of different classes that are successfully served or, more generally, if different rewards are associated with different classes of tasks, the percentage of the possible reward gained. We suggest heuristic policies for a Blue server to select the next task to perform and to decide how much time to allocate to that service. We discuss this for a number of specific examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

Optimal replacement of continuously degrading systems in partially observed environments

Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the component fails when it is accumulated degradation first reaches a fixed threshold. Assuming periodic inspection of the component, the objective is to minimize the long‐run average cost per unit time of performing preventive and reactive replacements for two distinct cases. The first case examines instantaneous replacements and fixed costs, while the second considers time‐consuming replacements and revenue losses accrued during periods of unavailability. Formulated and solved are mixed state space, partially observable Markov decision process models, both of which reveal the optimality of environment‐dependent threshold policies with respect to the component's cumulative degradation level. Additionally, it is shown that for each degradation value, a threshold policy with respect to the environment belief state is optimal if the environment alternates between two states. The threshold policies are illustrated by way of numerical examples using both synthetic and real wind turbine data. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 395–415, 2015     

Predicting older-donor kidneys' post-transplant renal function using pre-transplant data

This paper provides a methodology for predicting post-transplant kidney function, that is, the 1-year post-transplant estimated Glomerular Filtration Rate (eGFR-1) for each donor-candidate pair. We apply customized machine-learning algorithms to pre-transplant donor and recipient data to determine the probability of achieving an eGFR-1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR-1 ≥ 30 ml/min. For some donor-candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older-donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR-1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR-1 ≥ 30 ml/min and that survive 1 year have higher 10-year death-censored graft survival probabilities than all older-donor transplants that survive 1 year (73.61% vs. 70.48%, respectively).     

Efficient algorithms for flexible job shop scheduling with parallel machines

Job shop scheduling with a bank of machines in parallel is important from both theoretical and practical points of view. Herein we focus on the scheduling problem of minimizing the makespan in a flexible two-center job shop. The first center consists of one machine and the second has k parallel machines. An easy-to-perform approximate algorithm for minimizing the makespan with one-unit-time operations in the first center and k-unit-time operations in the second center is proposed. The algorithm has the absolute worst-case error bound of k − 1 , and thus for k = 1 it is optimal. Importantly, it runs in linear time and its error bound is independent of the number of jobs to be processed. Moreover, the algorithm can be modified to give an optimal schedule for k = 2 .     

The Odyssey of the Comprehensive Nuclear-Test-Ban Treaty

This study examines the failures of the William J. Clinton and Barack Obama administrations to secure ratification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). It applies an integrated analytical framework for assessing treaty ratification that builds upon previous research in order to understand why the Clinton administration failed to achieve CTBT ratification in 1999 and why the Obama administration has so far failed to advance the treaty in the Senate. The study concludes that CTBT ratification, despite Obama administration pledges of support, remains highly unlikely. Finally, the study analyzes the common domestic political factors present in both cases and suggests areas for further research.