FC Coding is
One step from 1st Principles to Solution!


Buy, Sell, or Hold Options
for Business Strategies

War Gaming:
what if this, what if that?
Buy this, Sell that?
test your ideas on a computer!

Goal for this page: get top level management, engineers, & scientists thinking outside-the-box!

This page will show CEOs and management how to test their Business Strategies and get a good look at the outcome of their decisions.

First, we need to estimate the amount of oil needed at each plant. (Recommend downloading the CurvFit App, that has its source code included, in order to get an idea on what to do here.) The following code is just a sketch of what will be necessary to estimate oil production (i.e., 'demandEst') requirements for 'tomorrow' at each plant/refinery. The objective is to minimize curve fit error (i.e., match "tomorrow's Supply" to Sales history data).

call history ! match curve to data, thus producing a curve [y(i,j)] ! to extrapolate oil quantities of all 'k'th grades ! needed tomorrow, 't' future time, at each 'i'th plant. ! Curve fit 'salesHistory' data at each plant for all grades ! See curvFit source file for ideas how to do this curve ! fitting process. It produces curves that can extrapolate ! your data to time 't'. Need many data points for good ! extrapolation values. t = time of 'tomorrow' do i = 1, nRefineries do j = 1, n??? do k = 1, nProducts demandEst( i,j,k) = y( i,j,k) @ t end do end do end do

The above code should provide a good estimate for oil production necessary for 'tomorrow'. With parameter 'demandEst' calculated, the find statement can be completed as follows:

Global all problem supplyNeeds ooo FIND supplyEst; IN refineries; BY JUPITER; MATCHING supplyErr; TO MAXIMIZE profit

The 2nd level Find statement code will execute Oil Production at All or several plants/refineries. The objective is to maximize profit while minimizing Pollution.

model refineries pollution = 0: profit = 0: cost = 0: supplyErr = 0 do i = 1, nRefineries do j = 1, nProducts supplyQty(j) = supplyEst(i, j) end do crudeUsed = 0: crudeErr = 0 ! finds Qty production @ each refinery to minimize overall pollution ! to restrict 'supplyQty(j)' to equal 'supplyEst(j)' find supplyQty; in processing; by Jove; with upper hi; and lower low; matching crudeErr; to minimize pollution do j = 1, nProducts supplyErr = supplyErr + (supplyQty(j) - supplyEst(i, j))**2 end do end do ! find best routes to deliver products ooo find routes; in distribution; ooo to minimize distPollution profit = ??? ! calculate / measure it! cost = ??? ! ditto profit = profit - cost end model Processing ! jth distillation unit @ ith refinery ooo crudeErr = crudeErr + (totCrudeIn(j)– crudeUsed)**2 79 format( 1x,f8.4,20(g14.5, 1x)) end model distribution distPollution = 0 ! your (algebraic?) equations that model your distribution go here. ooo end

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Problem-Solving Applications include:

CurvFit: a curve fitting program with Lorentzian, Sine, Exponential and Power series are available models to match your data.

Match-n-Freq: a Matched Filter program used to filter signals and slim pulses.

Industry Problem-Solving Descriptions include:

Business Strategies & War Gaming: Buy, Sell, Hold options may be tested for an entire company, individual plant(s), or whole product lines. Imagine an increase in control settings from a 1 or 2 digits (i.e., a guess value) to an 8+ digit accuracy resulting from a Calculus programming calculation!

Pulse Slimming to minimize InterSymbol Interference: via Arbitrary Equalization with Simple LC Structures to reduce errors.

Voice Coil Motor: basically an electromagnetic transducer in which a coil placed in a magnetic pole gap experiences a force proportional to the current passing through the coil.

Electrical Filter Design: find the transfer function's poles & zeros; H(s) = Yout(s) / Yin(s).

Digitized Signal from Magnetic Recording: Magnetic recording of transitions written onto a computer disc drive may produce an isolated pulse as shown.

AC Motor Design: a simulation program for A.C. motor design that was reapplied as a constrained optimization problem with 12 unknown parameters and 7 constraints.

PharmacoKinetics: an open-two- compartment model with first order absorption into elimination from central compartment is presented here.