Quick start - Gams

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Report writing Corn Wheat Available Value Land .Total 100.0000 Land .Use by 50.0000 50.0000 Land .Marginal 52.0000 Labor.Total 500.0000 Labor.Use by 300.0000 200.0000 Labor.Marginal 9.5000 GAMS permits one to do calculations using solution information to improve the information content of the output. This exercise is commonly called report writing. Information relative to the variable, equation and model solution is passed to GAMS from solvers. These data can be used in report writing computations. In GAMS the solution level for a variable is Variablename.L while it is Equationname.L for an equation. The dual or shadow price information for an equation is addressed as Equationname.M and the reduced cost for a variable is Equationname.M. The numerical values of these parameters are generally undefined until a solve is performed and retains the value from the most recent solve from then on. In the algebraic version of the equilibrium model (econequilalg.gms) I introduce the following report writing sequence set qitem /Demand, Supply, "Market Clearing"/; set item /Quantity,Price/ parameter myreport(qitem,item,commodities); myreport("Demand","Quantity",commodities)= Qd.l(commodities); myreport("Supply","Quantity",commodities)= Qs.l(commodities); myreport("Market Clearing","Price",commodities)= p.l(commodities); display myreport; which saves the supply and demand quantities along with the market clearing price. The resultant report is generated with a display statement and is ---- 39 PARAMETER myreport Corn Wheat Supply .Quantity 1.711 8.156 Demand .Quantity 1.711 8.156 Market Clearing.Price 2.671 4.618 where I have color coded the originating statements and resultant output. A report writing sequence is also introduced into the optimization example (goodoptalgebra.gms) as follows set item /Total,"Use by",Marginal/; set qitem /Available,Corn,Wheat,Cotton,Value/; parameter Thisreport(resources,item,qitem) Report on resources; Thisreport(resources,"Total","Available")=endowments(resources); Thisreport(resources,"Use by",qitem)= sum(products$sameas(products,qitem), resourceusage(resources,products) *production.l(products)); Courtesy of B.A. McCarl, October 2002 40
Thisreport(resources,"Marginal","Value")= available.m(resources); option thisreport:2:1:2; display thisreport; where both equation marginals (shadow prices) and variable levels are included in the report writing calculations. This yields the report Total Use by Use by Marginal Available Corn Wheat Value Land 100.00 50.00 50.00 52.00 Labor 500.00 300.00 200.00 9.50 where I have color coded the originating statements and resultant output. The report wring topic is extensively discussed in the Report Writing chapter with a more advanced discussion also appearing in the Output via Put Commands chapter. Why use GAMS and algebraic modeling Finally I feel it is beneficial to examine the attributes and difficulties with GAMS based algebraic modeling. This is done under the following topics Use of algebraic modeling Context changes, Expandability Augmenting models Aid with initial formulation and subsequent changes Adding report writing Self-Documenting Nature Large Model Facilities Automated Problem Handling Model Library and widespread professional use Use by Others Ease of use with nonlinear, mixed integer, CGE and other problem forms Interface with other packages Use of algebraic modeling GAMS permits one to express a formulation in general algebraic terms using symbolic summation notation. This allows modelers to concisely state problems, largely independent of the data and exact application context. Such formulations are inherently expandable, easily subjected to context changes, and easily augmented as will be discussed just below. However use of algebraic modeling can be a two edged sword GAMS algebraic requirements and summation notation are difficult for some users. Some people will always desire to deal with the exact problem context, not an abstract general formulation. This does lead to a strategy Courtesy of B.A. McCarl, October 2002 41
Page 1 and 2: Quick Start Tutorial drawn from GAM
Page 3 and 4: Ease of use with NLP, MIP, CGE and
Page 5 and 6: Solving a nonlinear equation system
Page 7 and 8: What is the new Z variable in the o
Page 9 and 10: Notes Pdemand.. P =g= 6 - 0.3*Qd; (
Page 11 and 12: Notes SOLVE PROBLEM USING MCP; (eco
Page 13 and 14: The .l, .lo and .up appendages on t
Page 15 and 16: other three lines. I also omit a re
Page 17 and 18: will be implemented. In particular,
Page 19 and 20: Notes ---- Qd Qd (.LO, .L, .UP = 0,
Page 21 and 22: RESOURCE USAGE, LIMIT 0.090 1000.00
Page 23 and 24: The columns associated with each en
Page 25 and 26: The optimization example is as foll
Page 27 and 28: POSITIVE VARIABLES P(commodities) Q
Page 29 and 30: Examples Notes ... /; PARAMETER c(j
Page 31 and 32: Notes When a statement like cc(j)=
Page 33 and 34: OBJective.. - 109*x(Corn) - 90*x(Wh
Page 35 and 36: particular instructions or indicati
Page 37 and 38: Subsequent usage. The item names,
Page 39: Notes Display 'here it is', "hello"
Page 43 and 44: Shoptime 6 8 30 5 12; POSITIVE VARI
Page 45 and 46: One important GAMS feature its self
Page 47 and 48: proper internal documentation and a

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