Example of use of Unit Loss Function

In a previous post I showed how to write and solve a unit loss function G(k). Here I show how to use it.

Here I solve some (s,Q) inventory models. These models have a variable Q: order quantity. It is sometimes argued to use the EOQ order quantity for this. EOQ stands for Economic Order Quantity. In the model I try to see how much difference it makes when we use this EOQ or just solve for Q in the (s,Q) models directly.

The results are:

----    155 PARAMETER results                              EOQ      Qendog        Qeoq EOQ .Q                 5200.000 CSEO.Q                             5876.746    5200.000 CSEO.Total Cost                 6337360.618 6337934.428 CSEO.Inv+Short Cost              137360.618  137934.428 CSEO.s                             5658.711    5749.015 CSEO.k                                2.094       2.152 CIS .Q                             5846.467    5200.000 CIS .Total Cost                 6331413.493 6331942.157 CIS .Inv+Short Cost              131413.493  131942.157 CIS .s                             5292.515    5373.301 CIS .k                                1.860       1.912

We can see for two inventory models (Cost per Stockout Event – CSOE and Cost per Item Short – CIS) the differences in Q and s are significant. However the effect on total cost and relevant cost is more limited: things are pretty flat out there.

In practice formulas based on the first order conditions are used to solve these inventory models. However I think there is a case to be made to look at the original cost functions and optimize these directly. This relates more to the original problem and also we may be a little bit more flexible if we want to add a few more wrinkles. In some cases there are closed solutions, e.g. the well known EOQ formula is:

In the model below again we use the original cost function and minimize that for Q. Note that for other cases it may not be that easy to find closed solutions.

$ontext    Some (s,Q) inventory models    We evaluate two inventory models:       (1) Cost per Stockout Event (CSOE) Model       (2) Cost per Item Short (CIS) Model    It is sometimes suggested to input Q*=EOQ into these models    opposed to optimizing directly for Q. Here we try to    see how much a difference this makes. $offtext scalars    D         'mean demand ($/year)'           /62000/    sigma_D   'standard deviation of demand'    /8000/    c         'cost per item ($/item)'           /100/    cK        'order cost ($/order)'       /3270.9678/    ci        'annual inventory cost'    h         'holding charge (% of unit cost)' /0.15/    mu_dl     'mean over lead time'    sigma_dl  'sigma over lead time'    L         'lead time (days)'                  /14/    B1        'CSOE penalty'                   /50000/    cs        'Item short cost'                   /45/    Qeoq      'EOQ' ; ci = h*c; mu_dl = D/(365/L); sigma_dl = sigma_D/sqrt(365/L); parameter results(*,*,*); *------------------------------------------------------ * Deterministic EOQ model *------------------------------------------------------ variables     tc  'total cost'     Q   'order quantity' ; * prevent division by zero Q.lo = 0.1; equations     costdef1    'total cost calculation for simple deterministic case' ; costdef1..    tc =e= c*D + cK*(D/Q) + ci*(Q/2); model eoq /costdef1/; solve eoq minimizing tc using nlp; Qeoq = Q.l; results('EOQ','Q','EOQ') = Qeoq; *------------------------------------------------------ *  Cost per Stockout Event Model *------------------------------------------------------ positive variables    k    PStockout  'P[x>=k]'    s          'order point' ; equations     costdef2  'CSOE total cost function'     cdf       'this implements P[x>=k]'     sdef      'calculation of order point s' ; costdef2..    tc =e=  c*D + cK*(D/Q) + ci*(Q/2+k*sigma_dl) + B1*(D/Q)*PStockOut; cdf..    Pstockout =e= 1-errorf(k); sdef..    s =e= mu_dl + k*sigma_dl; model csoe /costdef2,cdf,sdef/; *------------------------------------------------------ *  Cost per Item Short (CIS)  Model *------------------------------------------------------ variables     G   'unit loss function' ; equations     costdef3 'CIS total cost function'     Gdef     'this implements G(k)' ; costdef3..    tc =e=  c*D + cK*(D/Q) + ci*(Q/2+k*sigma_dl) + cs*sigma_dl*G*(D/Q); Gdef..    G =e= 1/sqrt(2*pi)*exp(-0.5*sqr(k)) - k * (1-errorf(k)); model cis /costdef3,sdef,Gdef/; *------------------------------------------------------ *  Results with Q endogenous *------------------------------------------------------ solve csoe minimizing tc using nlp; results('CSEO','Total Cost','Qendog') = TC.L; results('CSEO','Inv+Short Cost','Qendog') = TC.L-c*D; results('CSEO','Q','Qendog') = Q.L; results('CSEO','s','Qendog') = s.L; results('CSEO','k','Qendog') = k.L; solve cis minimizing tc using nlp; results('CIS','Total Cost','Qendog') = TC.L; results('CIS','Inv+Short Cost','Qendog') = TC.L-c*D; results('CIS','Q','Qendog') = Q.L; results('CIS','k','Qendog') = k.L; results('CIS','s','Qendog') = s.L; *------------------------------------------------------ *  Results with Q fixed to EOQ *------------------------------------------------------ Q.fx = Qeoq; solve csoe minimizing tc using nlp; results('CSEO','Total Cost','Qeoq') = TC.L; results('CSEO','Inv+Short Cost','Qeoq') = TC.L-c*D; results('CSEO','Q','Qeoq') = Q.L; results('CSEO','k','Qeoq') = k.L; results('CSEO','s','Qeoq') = s.L; solve cis minimizing tc using nlp; results('CIS','Total Cost','Qeoq') = TC.L; results('CIS','Inv+Short Cost','Qeoq') = TC.L-c*D; results('CIS','Q','Qeoq') = Q.L; results('CIS','k','Qeoq') = k.L; results('CIS','s','Qeoq') = s.L; display results;