/// <summary>
/// Randomly generates a firm object (production technology and output market parameters).
/// </summary>
/// <param name="ip">A pointer to the collection of input parameters.</param>
/// <param name="FirmID">Unique identifier for this firm (run number)</param>
public Firm(InputParameters ip, int FirmID)
{
// Choose random values for DISP2 (the top DISP1 resources
// account for DISP2 percent of total resource costs), and
// density (sparsity) of resource consumption pattern matrix
this.g = GenRandNumbers.GenUniformDbl(ip.DISP2_MIN, ip.DISP2_MAX);
this.d = GenRandNumbers.GenUniformDbl(ip.DNS_MIN, ip.DNS_MAX);
// Generate the true product margins and the true, optimal
// decision vector. Keep generating new margins until there
// is at least one product in the optimal mix.
RowVector MAR, DECT0;
do
{
MAR = this.GenMargins(ip);
DECT0 = MAR.Map(x => (x < 1.0) ? 0.0 : 1.0);
} while (DECT0.TrueForAll(x => x == 0.0));
// Generate vector of maximum production quantities
this.mxq = this.GenMXQ(ip);
// And associated vector of optimal production quantities
ColumnVector QT = mxq.ewMultiply(DECT0);
// Flowchart 5.1 - Create resource consumption pattern matrix
this.res_cons_pat = GenResConsPat(ip);
// Flowchart 5.2 - Compute TRU
// Calculate vector of total units of resource
// consumption, by product
ColumnVector TRU = this.CalcResConsumption(QT);
// Flowchart 5.3 - Compute MAXRU
// Calculate resource consumption under the assumption
// that all products are produced at maximum quantity
ColumnVector MAXRU = this.CalcResConsumption(mxq);
RowVector RCC, PC_B, RCCN;
double TCT0;
#region Flowchart 5.4 - Generate RCC, RCU, and RCCN
/* -------------------------------- */
// Flowchart 5.4(a)-(g)
// Generate vector of total resource costs (RCC)
RCC = GenRCC(ip);
/* -------------------------------- */
// Flowchart 5.4(h)
// Now generate unit resource costs (RCU) by doing element-wise
// division of RCC by MAXRU
this.rcu = RCC.Map((x, i) => x / MAXRU[i]);
/* -------------------------------- */
// Flowchart 5.4(i)
// Compute new RCC vector (RCCN) based on unit resource
// costs (RCU) and true unit resource consumption (TRU)
RCCN = this.rcu.ewMultiply(TRU);
// Check to see if the first resource (RCCN[0]) is the largest.
// If not, increase RCU[0] by just enough to make it so.
if (RCCN[0] < RCCN.Skip(1).Max() + 1)
{
RCCN[0] = Math.Ceiling(RCCN.Max()) + 1.0;
this.rcu[0] = RCCN[0] / TRU[0];
}
#endregion
// Flowchart 5.5 - Calculate PC_B
// Calculate true unit product costs
PC_B = this.CalcTrueProductCosts();
// Flowchart 5.6 - Compute total costs TCT0
// Compute total costs
TCT0 = this.CalcTotCosts(QT);
// Flowchart 5.7 - Rename RCCN to RCC
RCC = RCCN;
initial_rcc = RCC;
#region Flowchart 5.8 - Calculate SP, TRV0, PROFITT0
// Calculate product selling prices, total revenue, and profit
this.sp = PC_B.ewMultiply(MAR);
double TRV0 = this.sp * QT;
this.profitt0 = TRV0 - TCT0;
#endregion
// 5.9(a) Create RANK vector
// Note: this method provides a stable sort. It's important to use a stable sort.
// LOOKUP IN VERSION.TXT WHY IT'S IMPORTANT TO USE A STABLE SORT HERE.
initial_rank = Enumerable.Range(0, RCC.Dimension).OrderByDescending(i => RCC[i]).ToArray();
#region Flowchart 5.9(b) - Create RES_CONS_PAT_PRCT
this.res_cons_pat_prct = new RectangularMatrix(ip.RCP, ip.CO);
for (int r = 0; r < this.res_cons_pat.RowCount; ++r)
{
RowVector rv = this.res_cons_pat.Row(r);
if (TRU[r] != 0.0)
{
rv = rv.Map((alt_ij, col) => alt_ij * QT[col] / TRU[r]);
if (Math.Abs(rv.Sum() - 1.0) > 0.01)
{
throw new ApplicationException("Sum of row of RES_CONS_PAT_PRCT not equal to 1.");
}
}
else
{
rv = rv.Map(alt_ij => 0.0);
}
this.res_cons_pat_prct.CopyRowInto(rv, r);
}
#endregion
#region Flowchart 5.9(c) - Create correlation matrix
// Create correlation matrix for rows of RES_CONS_PAT_PRCT
MultivariateSample mvs = new MultivariateSample(ip.RCP);
for (int c = 0; c < this.res_cons_pat_prct.ColumnCount; ++c)
{
mvs.Add(this.res_cons_pat_prct.Column(c));
}
this.pearsoncorr = new SymmetricMatrix(ip.RCP);
for (int i = 0; i < mvs.Dimension; ++i)
{
for (int j = i; j < mvs.Dimension; ++j)
{
//PearsonCorr[i, j] = mvs.PearsonRTest( i, j ).Statistic;
this.pearsoncorr[i, j] = mvs.TwoColumns(i, j).PearsonRTest().Statistic;
}
}
#endregion
// Flowchart 5.10 - Logging true system
// Note: I'm deliberately passing copies of the fields MXQ, SP, etc.
Output.LogFirm(
ip, this, FirmID,
MAR, DECT0,
TRV0, TCT0, profitt0,
RCC);
}
static void Main(string[] args)
{
#region Console header
DrawASCIIart();
#endregion
#region Read input file and create InputParameters object
FileInfo inputFile = new FileInfo(Environment.CurrentDirectory + @"\input.txt");
if (!inputFile.Exists)
{
Console.WriteLine("Could not find input file: \n{0}", inputFile.FullName);
Console.WriteLine("Aborting. Press ENTER to end the program.");
Console.ReadLine();
return;
}
InputParameters ip = new InputParameters(inputFile);
#endregion
#region Make a copy of the input file
// We found it helpful to make a copy of the input file every time we ran the
// simulation. We stamp the copy's filename with the date and time so that
// we know which results files correspond to which input file.
DateTime dt = DateTime.Now;
string inputFileCopyName =
String.Format(
"input {0:D2}-{1:D2}-{2:D4} {3:D2}h {4:D2}m {5:D2}s, seed {6:G}.txt",
dt.Month,
dt.Day,
dt.Year,
dt.Hour,
dt.Minute,
dt.Second,
GenRandNumbers.GetSeed()
);
FileInfo inputFileCopy = new FileInfo(Environment.CurrentDirectory + @"\" + inputFileCopyName);
inputFile.CopyTo(inputFileCopy.FullName, true);
File.SetCreationTime(inputFileCopy.FullName, dt);
File.SetLastWriteTime(inputFileCopy.FullName, dt);
#endregion
#region Create output files
Output.CreateOutputFiles(ip);
#endregion
#region Generate Sample of Firms and their Cost Systems
Firm[] sampleFirms = new Firm[ip.NUM_FIRMS];
for (int firmID = 1; firmID <= ip.NUM_FIRMS; ++firmID)
{
Console.WriteLine(
"Starting firm {0:D3} of {1}",
firmID + 1, sampleFirms.Length
);
Firm f = new Firm(ip, firmID);
sampleFirms[firmID - 1] = f;
for (int a_indx = 0; a_indx < ip.ACP.Count; ++a_indx)
{
int a = ip.ACP[a_indx];
for (int p_indx = 0; p_indx < ip.PACP.Count; ++p_indx)
{
int p = ip.PACP[p_indx];
for (int r_indx = 0; r_indx < ip.PDR.Count; ++r_indx)
{
int r = ip.PDR[r_indx];
// Create a cost system
CostSys costsys = new CostSys(ip, f, a, p, r);
f.costSystems.Add(costsys);
int costSysID = f.costSystems.Count;
Output.LogCostSys(costsys, firmID, costSysID);
// Generate a starting decision for the cost system.
RowVector startingDecision;
if (ip.STARTMIX == 0)
{
startingDecision = f.CalcOptimalDecision();
}
else
{
var ones = Enumerable.Repeat(1.0, ip.CO).ToList();
startingDecision = new RowVector(ones);
for (int i = 0; i < startingDecision.Dimension; ++i)
{
if (GenRandNumbers.GenUniformDbl() < ip.EXCLUDE)
{
startingDecision[i] = 0.0;
}
}
}
/* Examine error in cost from implementing this decision.
* Assume the firm implements the decision startingDecision. Upon
* doing so, it will observe total resource consumption. It will then
* allocate resources to cost pools, as per the B parameter of the cost
* system, choose drivers as per the D parameter of the cost system,
* and then allocate resources to cost objects and compute reported costs.
* The reported costs are returned as PC_R. The difference
* between these and the true benchmark costs (PC_B) is used to compute
* the mean percent error in costs.
*/
RowVector PC_R = costsys.CalcReportedCosts(ip, startingDecision);
RowVector PC_B = f.CalcTrueProductCosts();
double MPE = PC_B.Zip(PC_R, (pc_b, pc_r) => Math.Abs(pc_b - pc_r) / pc_b).Sum() / PC_B.Dimension;
Output.LogCostSysError(costsys, firmID, costSysID, startingDecision, PC_B, PC_R, MPE);
/* Assume the firm implements the decision startingDecision. Upon
* doing so, it will observe total resource consumption. It will then
* allocate resources to cost pools, as per the B parameter of the cost
* system, choose drivers as per the D parameter of the cost system,
* and then allocate resources to cost objects and compute reported costs.
* The reported costs are returned as PC_R. Upon observing the
* reported costs, the firm may wish to update its original decision. When
* it implements the updated decision, costs will change again. The outcome
* of this process will either be an equilibrium decision (fixed point), or
* a cycle of decisions.
*/
(CostSystemOutcomes stopCode, RowVector endingDecision) = costsys.EquilibriumCheck(ip, startingDecision);
Output.LogCostSysLoop(costsys, firmID, costSysID, startingDecision, endingDecision, stopCode);
}
}
}
}
#endregion
Console.WriteLine("Writing output files...");
Output.WriteOutput();
Console.WriteLine("Done!");
}