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*FORM FIELD ProblemFormulation*ProblemFormulationThe American Steel Problem can be solved as a transshipment problem. The supply at the supply nodes is the maximum production at the steel mills, i.e., 10,000 and 15,000 for Youngstown and Pittsburgh repsectively. The demand at demand nodes in given by the demand at the field warehouses and the other nodes are transshipment nodes. The costs and bounds on flow through the network are also given. The most compact formulation for this problem is a network formulation (see The Transshipment Problem for details).

*FORM FIELD ComputationalModel*ComputationalModelWe can use the AMPL model file set NODES := Youngstown Pittsburgh Cincinnati 'Kansas City' Chicago Albany Houston Tempe Gary ;
Note that The arc set is 2dimensional and can be defined in a number of different ways: # List of arcs set ARCS := (Youngstown, Albany), (Youngstown, Cincinnati), ... , (Chicago, Gary) ; # Table of arcs set ARCS: Cincinnati 'Kansas City' Chicago Albany Houston Tempe Gary := Youngstown + + + +    Pittsburgh + + +    + . . . # Array of arcs set ARCS := (Youngstown, *) Cincinnati 'Kansas City' Chicago Albany (Pittsburgh, *) Cincinnati 'Kansas City' Chicago Gary . . . (Chicago, *) Tempe Gary ; Since the node set is small and the arc set is "dense", i.e., many of the node pairs are represented in the arc set, we choose a table to define ARCS: set ARCS: Cincinnati 'Kansas City' Chicago Albany Houston Tempe Gary := Youngstown + + + +    Pittsburgh + + +    + Cincinnati    + +   'Kansas City'     + +  Chicago      + + ;
The param NetDemand := Youngstown 10000 Pittsburgh 15000 Albany 3000 Houston 7000 Tempe 4000 Gary 6000 ;
We can use lists, tables or arrays to define the parameters for the American Steel Transhippment Problem,
but in this case we will use a list and define multiple parameters at once. This allows us to "cutandpaste" the parameters from the problem description. Note the use of
param: Cost Lower Upper:= Youngstown Albany 500 . 1000 Youngstown Cincinnati 350 . 3000 Youngstown 'Kansas City' 450 1000 5000 Youngstown Chicago 375 . 5000 Pittsburgh Cincinnati 350 . 2000 Pittsburgh 'Kansas City' 450 2000 3000 Pittsburgh Chicago 400 . 4000 Pittsburgh Gary 450 . 2000 Cincinnati Albany 350 1000 5000 Cincinnati Houston 550 . 6000 'Kansas City' Houston 375 . 4000 'Kansas City' Tempe 650 . 4000 Chicago Tempe 600 . 2000 Chicago Gary 120 . 4000 ; Note that the cost is in $/1000 ton, so we must divide the cost by 1000 in our script file before solving: reset; model transshipment.mod; data steel.dat; let {(m, n) in ARCS} Cost[m, n] := Cost[m, n] / 1000; option solver cplex; solve; display Flow; *FORM FIELD Results*ResultsUsing transshipment.mod , and the data and script files defined in Computational Model we can solve the American Steel Transshipment Problem to get the optimal Flow variables:
If the total supply is greater than the total demand, the transshipment problem will solve, but flow may be left in the network (in this case at the Pittsburgh node). In If total supply is less than demand (hence the problem is infeasible) we can add a dummy supply node (see with arcs to all the demand nodes. The optimal solution will show the "best" nodes to send the extra supply to.   
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< <  *FORM FIELD Conclusions*ConclusionsIn order to minimise the monthly shipment costs, American Steel should follow the shipment plan shown in Table 3.
As with many network problems, it can be illuminating to display the solution graphically as shown in Figure 1.  
> >  *FORM FIELD Conclusions*ConclusionsIn order to minimise the monthly shipment costs, American Steel should follow the shipment plan shown in Table 3.
As with many network problems, it can be illuminating to display the solution graphically as shown in Figure 1. Figure 1 Optimal Shipment Plan
  
