Dynamic capacity reservation and due date quoting in a make‐to‐order system

We consider a make‐to‐order manufacturer facing random demand from two classes of customers. We develop an integrated model for reserving capacity in anticipation of future order arrivals from high priority customers and setting due dates for incoming orders. Our research exhibits two distinct features: (1) we explicitly model the manufacturer's uncertainty about the customers' due date preferences for future orders; and (2) we utilize a service level measure for reserving capacity rather than estimating short and long term implications of due date quoting with a penalty cost function. We identify an interesting effect (“t‐pooling”) that arises when the (partial) knowledge of customer due date preferences is utilized in making capacity reservation and order allocation decisions. We characterize the relationship between the customer due date preferences and the required reservation quantities and show that not considering the t‐pooling effect (as done in traditional capacity and inventory rationing literature) leads to excessive capacity reservations. Numerical analyses are conducted to investigate the behavior and performance of our capacity reservation and due date quoting approach in a dynamic setting with multiple planning horizons and roll‐overs. One interesting and seemingly counterintuitive finding of our analyses is that under certain conditions reserving capacity for high priority customers not only improves high priority fulfillment, but also increases the overall system fill rate. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

New inequalities for finite and infinite group problems from approximate lifting

In this paper, we derive new families of facet‐defining inequalities for the finite group problem and extreme inequalities for the infinite group problem using approximate lifting. The new valid inequalities for the finite group problem include two‐ and three‐slope facet‐defining inequalities as well as the first family of four‐slope facet‐defining inequalities. The new valid inequalities for the infinite group problem include families of two‐ and three‐slope extreme inequalities. These new inequalities not only illustrate the diversity of strong inequalities for the finite and infinite group problems, but also provide a large variety of new cutting planes for solving integer and mixed‐integer programming problems. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Attrition through a partially coordinated area defense

An area defense consists of several groups that act independently, i.e., do not communicate with each other. Each group has a fixed number of defenders and a controller that allocates these defenders optimally against the individual attackers comprising an attack. We analyze the effectiveness of this partially coordinated defense against a simultaneous attack of known size in which all attackers are considered to be equally lethal. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Layers of Experiments with Adaptive Combined Design

In the field of nanofabrication, engineers often face unique challenges in resource‐limited experimental budgets, the sensitive nature of process behavior with respect to controllable variables, and highly demanding tolerance requirements. To effectively overcome these challenges, this article proposes a methodology for a sequential design of experiments through batches of experimental runs, aptly named Layers of Experiments with Adaptive Combined Design (LoE/ACD). In higher layers, where process behavior is less understood, experimental regions cover more design space and data points are more spread out. In lower layers, experimental regions are more focused to improve understanding of process sensitivities in a local, data‐rich environment. The experimental design is a combination of a space‐filling and an optimal design with a tuning parameter that is dependent on the amount of information accumulated over the various layers. The proposed LoE/ACD method is applied to optimize a carbon dioxide (epet‐CO2) assisted deposition process for fabricating silver nanoparticles with pressure and temperature variables. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 127–142, 2015     

Inventory control of by-products

The system to be controlled produces n products simultaneously in fixed proportions every time it is activated. Demands for the products in any period are components of an n dimensional vector random variable with known distribution function. Cases of excess demands backlogged and excess demands lost are considered. In the former the notion of k convexity can be generalized to guarantee relatively simple form for the optimal policy in an n decision problem. In the latter, this generalization was not successful although when there is no setup cost, a convexity argument can be used to show that the optimal policy has a simple form.     

Command and Control of India's Nuclear Forces

The Indian government has not made a public comment about the status of its nuclear weapon program since approving a nuclear doctrine in 2003. However, there is now enough information in the public domain to determine that the command-and-control system for the nuclear program has steadily matured in accordance with the intent of the approved nuclear doctrine. The Indian government has successfully mitigated many of the issues that plague the conventional military. The result is a basic command-and-control system that is focused only on the delivery, if ordered by the prime minister, of nuclear weapons. The system is not as robust as those of the United States and Russia, but is in place and ready as new Indian nuclear weapons enter into operation. The command-and-control system is developing to meet India's needs and political compulsions, but not necessarily as part of a more assertive nuclear policy.     

The bounded interval generalized assignment model

The bounded interval generalized assignment model is a “many-for-one” assignment model. Each task must be assigned to exactly one agent; however, each agent can be assigned multiple tasks as long as the agent resource consumed by performing the assigned tasks falls within a specified interval. The bounded interval generalized assignment model is formulated, and an algorithm for its solution is developed. Algorithms for the bounded interval versions of the semiassignment model and sources-to-uses transportation model are also discussed.