A bayes approach to quality control of an M/G/1 queue

This article provides a Bayes approach for the quality control of a production process which is defined by an M/G/1 queue. An inspection procedure which is jointly optimal from the queue's operational characteristics and quality-control perspectives is found using a queue-inspection renewal cycle analysis (with overall expected profit per unit time as the optimization objective). Numerical results are obtained, highlighting the relationships between quality control and (queue-like) production management.     

A new surrogate dual multiplier search procedure

Efficient computation of tight bounds is of primary concern in any branch-and-bound procedure for solving integer programming problems. Many successful branch-and-bound approaches use the linear programming relaxation for bounding purposes. Significant interest has been reported in Lagrangian and surrogate duals as alternative sources of bounds. The existence of efficient techniques such as subgradient search for solving Lagrangian duals has led to some very successful applications of Lagrangian duality in solving specially structured problems. While surrogate duals have been theoretically shown to provide stronger bounds, the difficulty of surrogate dual-multiplier search has discouraged their employment in solving integer programs. Based on the development of a new relationship between surrogate and Lagrangian duality, we suggest a new strategy for computing surrogate dual values. The proposed approach allows us to directly use established Lagrangian search methods for exploring surrogate dual multipliers. Computational experience with randomly generated capital budgeting problems validates the economic feasibility of the proposed ideas.     

On the (S − 1, S) lost sales inventory model with priority demand classes

In this paper an inventory model with several demand classes, prioritised according to importance, is analysed. We consider a lot‐for‐lot or (S ? 1, S) inventory model with lost sales. For each demand class there is a critical stock level at and below which demand from that class is not satisfied from stock on hand. In this way stock is retained to meet demand from higher priority demand classes. A set of such critical levels determines the stocking policy. For Poisson demand and a generally distributed lead time, we derive expressions for the service levels for each demand class and the average total cost per unit time. Efficient solution methods for obtaining optimal policies, with and without service level constraints, are presented. Numerical experiments in which the solution methods are tested demonstrate that significant cost reductions can be achieved by distinguishing between demand classes. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 593–610, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10032     

The value of losing control: Competition in markets for complements

When selling complementary products, manufacturers can often benefit from considering the resulting cross‐market interdependencies. Although using independent retailers makes it difficult to internalize these positive externalities, the ensuing double marginalization can mitigate within‐market competition. We use standard game theoretic analysis to determine optimal distribution channel strategies (through independent retailers or integrated) for competing manufacturers who participate in markets for complements. Our results suggest that a firm's optimal channel choice is highly dependent on its competitive positioning. A firm with a competitive advantage in terms of product characteristics (customer preferences) or production capabilities (cost) might benefit from selling through company‐controlled stores, allowing coordinated pricing across the two markets, whereas a less competitive firm might be better off using independent channel intermediaries to mitigate price competition. We consider two scenarios depending on whether the two firms make their distribution channel decisions sequentially or simultaneously. Although firms are unlikely to make such decisions at exactly the same instant, the simultaneous model also serves as a proxy for the scenario where firms decide sequentially, but where they cannot observe each other's strategic channel choices. For the sequential case, we find that the sequence of entry can have tremendous impact on the two firms'profits; whereas in some cases, the first mover can achieve substantially higher profits, we find that when the two markets are of sufficiently different size and only loosely related, a firm with a competitive advantage might be better off as a follower. Interestingly, our results suggest that, when the markets are of rather similar size, both firms are better off if they enter the industry sequentially. In those cases, the first entrant has incentive to reveal its planned channel strategies, and the follower has incentive to seek out and consider this information. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Integrated capacity,demand, and production planning with subcontracting and overtime options

Models for integrated production and demand planning decisions can serve to improve a producer's ability to effectively match demand requirements with production capabilities. In contexts with price‐sensitive demands, economies of scale in production, and multiple capacity options, such integrated planning problems can quickly become complex. To address these complexities, this paper provides profit‐maximizing production planning models for determining optimal demand and internal production capacity levels under price‐sensitive deterministic demands, with subcontracting and overtime options. The models determine a producer's optimal price, production, inventory, subcontracting, overtime, and internal capacity levels, while accounting for production economies of scale and capacity costs through concave cost functions. We use polyhedral properties and dynamic programming techniques to provide polynomial‐time solution approaches for obtaining an optimal solution for this class of problems when the internal capacity level is time‐invariant. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Designing safety space in a supply chain to handle system‐wide disruptions

In some supply chains serious disruptions are system wide. This happens during periods of severe weather, as when storms cause shuttle tankers serving oil platforms in the North Sea to stop movements of crude oil, barges are frozen in the Mississippi, or all airplanes are grounded after a blizzard. Other notable instances of system‐wide disruption happened after the attack on the World Trade Center when all aircraft were grounded and the natural gas and crude‐oil pipelines were tangled by hurricanes in 2005. We model a situation where shutting down supply facilities is very difficult and expensive because of excessive inventory buildup from an inability to move out the production. We present a planning model that balances the cost of spare capacity versus shutting down production when planning for disruptions. The model uses an assignment model embedded in a simulation. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Provocation,war and restraint under the nuclear shadow: The Kargil conflict 1999

ABSTRACT

The Kargil conflict was a limited war between India and Pakistan fought along the Kashmir Line of Control during the spring/summer of 1999. Named for the principal town in the combat zone on the Indian side, it was the first open warfare between India and Pakistan as declared nuclear weapons states and included the first combat employment of the Indian Air Force since 1971. Despite its intensity, it was also characterised by considerable restraint on both sides. The potential for conventional escalation, however, was high and the possibility of nuclear confrontation could not be excluded. It had significant long-term ramifications for both countries and constitutes an important part of the backdrop to their relations today.