A study of the vehicle transportation system for a manufacturer is presented. An algorithm based on a dynamic programming model is developed so as to find the optimal transportation arrangements referring to the compo...
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A study of the vehicle transportation system for a manufacturer is presented. An algorithm based on a dynamic programming model is developed so as to find the optimal transportation arrangements referring to the composition of the vehicles as well as the routing of these vehicles. The algorithm is run under the current condition as well as under a number of different scenarios. It is shown the algorithm can solve the problem with reduced computational complexity. The findings and suggestions resulting from the study can help the department manager in reviewing current operations arrangement and determining future operations arrangements.
This paper presents a modeling framework for estimating energy demand and CO2 emissions from process industries. The model has been used to project the same for four energy-intensive industries-steel, cement, fertiliz...
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This paper presents a modeling framework for estimating energy demand and CO2 emissions from process industries. The model has been used to project the same for four energy-intensive industries-steel, cement, fertilizer, and aluminum-in India, which account for nearly 50% of the energy consumed in the industrial sector.
A modelling framework based on linear dynamicprogramming techniques is presented which has been used to estimate energy demand as well as CO2 emissions associated with the Indian cement industry for different scenari...
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A modelling framework based on linear dynamicprogramming techniques is presented which has been used to estimate energy demand as well as CO2 emissions associated with the Indian cement industry for different scenarios during the period 1992-2021. Copyright (C) 1999 John Wiley & Sons, Ltd.
We optimize the trade-off between economic and ecological concerns in conservation biology by using a novel method to link a spatially explicit individual-based model to a dynamic programming model. To date, few optim...
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We optimize the trade-off between economic and ecological concerns in conservation biology by using a novel method to link a spatially explicit individual-based model to a dynamic programming model. To date, few optimality models have been presented to optimize this trade-off, especially when the common currency cannot be easily measured in dollars. We use a population simulation model (e.g. spatially explicit individual-based model) to model a hypothetical forest bird population's response to different cutting and planting regimes. We then link these results to a dynamic programming model to determine the optimal choice a manager should make at each time step to minimize revenue foregone by not harvesting timber while maintaining a given population of birds. Our results show that if optimal management choices are made further back in time, future (terminal) reward may be greater. As the end of the management period approaches, past management practices influence the terminal reward more than future practices can. Thus if past revenue lost is high, the future reward will be low as compared to when past revenue lost is low. The general strategy of setting some minimum viable population size and then using a population simulator linked to a dynamic programming model to ask how to maintain such a population size with minimum economic loss should have nearly universal applicability in conservation biology. (C) 1999 Elsevier Science B.V. All rights reserved.
This paper introduces a Bayesian decision theoretic model of optimal production in the presence of learning-curve uncertainty. The well-known learning-curve model is extended to allow for random variation in the learn...
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This paper introduces a Bayesian decision theoretic model of optimal production in the presence of learning-curve uncertainty. The well-known learning-curve model is extended to allow for random variation in the learning process with uncertainty regarding some parameter of the variation. A production run generates excess value (above its current revenue) for a Bayesian manager in two ways: it pushes the firm further along the learning curve, increasing the likelihood of lower costs for future runs;and it provides information, through the observed costs, that reduces the uncertainty regarding the rate at which costs are decreasing. We provide conditions under which one of the classical deterministic learning-curve results-namely, that optimal production exceeds the myopic level-carries over to the extended framework. We demonstrate that another classical deterministic learning-curve result-namely, that optimal production increases with cumulative production-does not hold in the Bayesian setting.
In the standard search problem there is an infinite pool of items whose distribution of values is known. A decision maker draws an item from the pool, observes its value, and decides whether to keep it or to draw anot...
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In the standard search problem there is an infinite pool of items whose distribution of values is known. A decision maker draws an item from the pool, observes its value, and decides whether to keep it or to draw another item. He can keep only one item, and he seeks the item with the largest value. In the standard uncertainty resolution problem there is only one item, and the value of that item remains uncertain even after it is drawn. The decision maker sequentially collects observations on the value of the item and decides whether to keep the item, discard the item, or take another observation. Uncertain search marries the sequential drawing from a pool of items from the search literature with the unknown value of a drawn item from the uncertainty resolution literature. Presented in the context of technology adoption, it considers drawing from a pool of new technologies whose values remain unknown even after being drawn. The decision maker sequentially purchases information in order to Bayesianly update the prior distribution of the technology's value. After each observation, the decision maker either adopts the technology (and hence quits searching), takes another costly observation, rejects the technology and quits searching, or rejects the technology and draws the next technology from the pool for observation. The solution to this uncertain search problem is surprisingly simple: solve the version of the uncertainty resolution problem in which the return to rejecting the technology is replaced by an exit value. Then use successive approximation to find a fixed point: an exit value that equals the expected value of the uncertainty resolution problem.
Consider a single-item, periodic review, stationary inventory model with stochastic demands, proportional ordering costs, and convex holding and shortage costs, where shortages are backordered and Veinott's well k...
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Consider a single-item, periodic review, stationary inventory model with stochastic demands, proportional ordering costs, and convex holding and shortage costs, where shortages are backordered and Veinott's well known terminal condition holds. Orders can be scheduled for any period, but the actual inventory level is determined every T periods through an audit. This leads to a dynamic programming model where stage n contains periods (n - 1)T + 1 through nT. For both discounted and averaging criteria, a simple rule optimally describes the orders for the T periods of a stage as a function of the state (beginning inventory level) and the cumulative T-period order. The latter is optimally determined by a base stock policy with two base stock levels: one for the final stage, another for the rest. (The horizon may be finite or infinite.) Methods are presented for computing optimal policies, together with bounds on the costs of (suboptimal) myopic policies. models with proportional costs and continuous demands are studied in detail. Computational experiments indicate that myopic policies perform quite well for such models. The selection of a best review period T is covered briefly. Applications of our model include just in time settings where audit decisions play a negligible role.
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