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point that was used to design lead-time regulation decision rules. An option6 in this case could be to

       calculate the parameters for a linearized model for each of several capacity operating points

       design lead time regulation decision rules for each operating point using the model for that operating point

       switch between decision rules as operating conditions vary.

      2.4.2 Linearization Using Taylor Series Expansion – Multiple Independent Variables

      A nonlinear function f(x,y,…) of several variables x, y, can be expanded into an infinite sum of terms of that function’s derivatives evaluated at operating point xo, yo, …:

      StartLayout 1st Row f left-parenthesis x comma y comma ellipsis right-parenthesis equals f left-parenthesis x Subscript o Baseline comma y Subscript o Baseline comma ellipsis right-parenthesis plus StartFraction 1 Over 1 factorial EndFraction left-parenthesis x minus x Subscript o Baseline right-parenthesis StartFraction partial-differential f Over partial-differential x EndFraction Math bar pipe bar symblom Subscript x Sub Subscript o Subscript comma y Sub Subscript o Subscript comma ellipsis Baseline plus StartFraction 1 Over 2 factorial EndFraction left-parenthesis x minus x Subscript o Baseline right-parenthesis squared StartFraction partial-differential squared f Over partial-differential x squared EndFraction Math bar pipe bar symblom Subscript x Sub Subscript o Subscript comma y Sub Subscript o Subscript comma ellipsis Baseline plus ellipsis 2nd Row plus StartFraction 1 Over 1 factorial EndFraction left-parenthesis y minus y Subscript o Baseline right-parenthesis StartFraction partial-differential f Over partial-differential y EndFraction Math bar pipe bar symblom Subscript x Sub Subscript o Subscript comma y Sub Subscript o Subscript comma ellipsis Baseline plus StartFraction 1 Over 2 factorial EndFraction left-parenthesis y minus y Subscript o Baseline right-parenthesis squared StartFraction partial-differential squared f Over partial-differential y squared EndFraction Math bar pipe bar symblom Subscript x Sub Subscript o Subscript comma y Sub Subscript o Subscript comma ellipsis Baseline plus ellipsis EndLayout (2.4)

      Over some range of (xxo), (y – yo), … higher-order terms can be neglected and a linear model is a sufficiently good approximation of the nonlinear model in the vicinity of the operating point:

      where

      Example 2.10 Production System Lead Time when WIP and Capacity are Variable

      In the case where the production work system illustrated in Figure 2.15 has variable work in progress (WIP) w(t) hours and variable production capacity r(t) hours/day, the lead time is

l left-parenthesis t right-parenthesis almost-equals StartFraction w left-parenthesis t right-parenthesis Over r left-parenthesis t right-parenthesis EndFraction

      For work in progress operating point wo and capacity operating point ro, an approximating linear function for lead time in the vicinity of operating point wo,ro, can be obtained using Equations 2.5 and 2.6:

l left-parenthesis t right-parenthesis almost-equals StartFraction w Subscript o Baseline Over r Subscript o Baseline EndFraction plus upper K Subscript r Baseline left-parenthesis r left-parenthesis t right-parenthesis minus r Subscript o Baseline right-parenthesis plus upper K Subscript w Baseline left-parenthesis w left-parenthesis t right-parenthesis minus w Subscript o Baseline right-parenthesis

      where

upper K Subscript r Baseline equals StartFraction partial-differential l Over partial-differential r EndFraction Math bar pipe bar symblom Subscript w Sub Subscript o Subscript comma r Sub Subscript o Subscript Baseline equals minus StartFraction w Subscript o Baseline Over r Subscript o Superscript 2 Baseline EndFraction upper K Subscript w Baseline equals StartFraction partial-differential l Over partial-differential w EndFraction Math bar pipe bar symblom Subscript w Sub Subscript o Subscript comma r Sub Subscript o Subscript Baseline equals StartFraction 1 Over r Subscript o Baseline EndFraction

      2.4.3 Piecewise Approximation

      In practice, variables in models of production systems may have a limited range of values. Maximum values of variables such as work in progress and production capacity cannot be exceeded, and these variables cannot have negative values. In many cases, operating conditions where limits have been reached may not be of primary interest when analyzing and designing the dynamic behavior of production systems. On the other hand, models can be developed that represent important combinations of operating conditions, each of which represents dynamic behavior under those specific conditions. A set of piecewise linear approximations then can be used to represent non-linear relationships between variables.

      Example 2.11 Piecewise Approximation of a Logistic Operating Curve

      Figure 2.17 Actual production capacity function and a piecewise linear approximation.

      As shown in Figure 2.17, the actual capacity function can be approximated in a piecewise manner by two segments, delineated by a WIP transition point wt hours, where for w(t) ≥ wt

r Subscript a Baseline left-parenthesis t right-parenthesis almost-equals r Subscript f Baseline

      and for w(t) < wt

r Subscript a Baseline left-parenthesis t right-parenthesis almost-equals 
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