Download e-book for kindle: 11th International Symposium on Process Systems Engineering by Iftekhar A Karimi, Rajagopalan Srinivasan

By Iftekhar A Karimi, Rajagopalan Srinivasan

ISBN-10: 0444595058

ISBN-13: 9780444595058

ISBN-10: 0444595066

ISBN-13: 9780444595065

ISBN-10: 0444595074

ISBN-13: 9780444595072

Vol. 1. half A -- v. 2. half B

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Extra resources for 11th International Symposium on Process Systems Engineering

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However, perhaps the greatest sustainability challenges are those associated with energy production and consumption. Energy consumption directly contributes to the majority of carbon dioxide emissions , and recent estimates of future economic growth imply that global energy consumption may more than triple over the coming decades. Since at present carbon capture and sequestration technologies are very expensive, in difficult economic times emphasis will be given first to energy conservation and fuel switching as primary carbon management approaches .

Numerical Experiments In this section, we evaluate the effectiveness of demand shaping, as it is depicted in Sections 3 and 4, in minimizing backlogging costs that result in the presence of unbalanced supply and demand. We simulate a production/sales environment that is modeled on a realistic problem setting taken from IBM’s server supply chain. The simulation has two parameters – the demand variability ϑ and the substitution rate δ – that may be altered to create alternative settings. Within this two dimensional space, we compare the expected backlogging costs that arise both with and without shaping to assess the value of shaping optimization, and to highlight its sensitivity to the two control parameters.

When shaping profile This quantity is computed to populate the i th entry in Bs ( hs ) . 4. Demand Shaping Optimization Having outlined customer behavior and the effects of shaping actions, we turn in this section to a description of the optimization model that selects our recommended shaping actions. The optimization is based on a stochastic view of demand forecasts and is formulated as a Markov decision process. Because of the large size of the model, we solve it using approximate dynamic programming.

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11th International Symposium on Process Systems Engineering by Iftekhar A Karimi, Rajagopalan Srinivasan

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