Yield management models

Y. Y Feng and B. C. Xiao. A continuous-time yield management model with multiple prices and reversible price changes. Management Science, 46(5) 644-657, 2000. 

Y. G. Liang. Solution to the continuous time dynamic yield management model. Transportation Science, 33 117-123, 1999. 

Duffie, D., Kan, R., 1996. A yield-factor model of interest rates. Math. Financ. 6 (4), 379-406. Fitton, P., McNatt, J., 1997. The four faces of an interest rate model. In Fabozzi, F. (Ed.), Advances in Fixed Income Valuation Modelling and Risk Management. FJF Associates, New Hope, PA. 

Fixed reorder quantity inventory model—A form of independent demand item management model in which an order for a fixed quantity, Q, is placed whenever stock on hand plus on order reaches a predetermined reorder level, R. The fixed order quantity Q may be determined by the economic order quantity, by a fixed order quantity (such as a carton or a truckload), or by another model yielding a fixed result. The reorder point R, may be deterministic or stochastic, and in either instance is large enough to cover the maximum expected demand during the replenishment lead time. Fixed reorder quantity models assume the existence of some form of a perpetual inventory record or some form of physical tracking, e.g., a two-bin system, that is able to determine when the reorder point is reached. These reorder systems are sometimes called fixed order quantity systems, lot-size systems, or order point-order quantity systems. 

Dyna.micPerforma.nce, Most models do not attempt to separate the equiUbrium behavior from the mass-transfer behavior. Rather they treat adsorption as one dynamic process with an overall dynamic response of the adsorbent bed to the feed stream. Although numerical solutions can be attempted for the rigorous partial differential equations, simplifying assumptions are often made to yield more manageable calculating techniques. 

We take a Bayesian approach to research process modeling, which encourages explicit statements about the prior degree of uncertainty, expressed as a probability distribution over possible outcomes. Simulation that builds in such uncertainty will be of a what-if nature, helping managers to explore different scenarios, to understand problem structure, and to see where the future is likely to be most sensitive to current choices, or indeed where outcomes are relatively indifferent to such choices. This determines where better information could best help improve decisions and how much to invest in internal research (research about process performance, and in particular, prediction reliability) that yields such information. 

The Agricultural Runoff Management (ARM) Model, developed by Hydrocomp Inc. for the U.S.EPA (2 ). It simulates the hydrology, sediment yield, and nutrient and pesticide behavior of the land phase of the hydrological cycle. The same organizations also developed the Non-Point Source (NPS) Model (3) which handles the washoff of miscellaneous pollutants from land surfaces. 

Therefore, in this approach, we develop Risk Model III as a reformulation of Risk Model II by employing the mean-absolute deviation (MAD), in place of variance, as the measure of operational risk imposed by the recourse costs to handle the same three factors of uncertainty (prices, demands, and yields). To the best of our knowledge, this is the first such application of MAD, a widely-used metric in the area of system identification and process control, for risk management in refinery planning. 

One important question we have asked ourselves many times since we managed to crack the North Sea atmospheric residue in the ARCO pilot unit for the first time was if the results are reliable. Does the pilot unit show the same trend and ranking as expected for the commercial FCCU, and can the yields from the pilot unit be used for modeling Initially there was no answer to these qnestions since no commercial data were available for comparison. The only possibility was to compare the tests done in the ARCO pilot unit with the tests done in the pilot nnit at M. W. Kellogg s some years earlier. 

Most organizations today are based on the scientific management theory, yielding the bmeaucratic model. The bureaucracy is the typical organizational structure, which is founded on the principles of logic, order, and legitimate authority. The characteristics of a bureaucratic organization include a clear division of labor, job specialization, a hierarchy of authority implemented... 

At this point, the yield model can be used to represent the pyrolysis behavior of specific feedstocks. To generalize the model, the effect of feed properties must be incorporated. Naphthas are complex mixtures of hydrocarbons. Feed characterization is needed to condense a detailed naphtha description into a manageable set of parameters, which uniquely defines feed-dependent conversion and yield effects. 

Sherman, K. (1991). Yields, models, and management of Large Marine Ecosystems. In Food Chains (Sherman, K., Alexander, L., and Gold, B., eds.). Westview Press, Boulder, CO. pp. 1—34. 

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