Obviusly, something is wrong. If someone can help me I will be very grateful.
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OPTION OPTCR=0.00001; SETS I products / PR1, PR2 / J trucks / CAM1*CAM5 / K markets / MER1, MER2 /; PARAMETERS A(I) offer / PR1 50 PR2 50 / B(J) transport / CAM1 25 CAM2 25 CAM3 50 CAM4 50 CAM5 100 / ; TABLE D(K,I) min demand PR1 PR2 MER1 25 25 MER2 25 25 ; SCALAR L transport cost proportional to the weight. L = proportionality constant /1/; PARAMETERS C(J) fixed transportation cost / CAM1 20 CAM2 20 CAM3 40 CAM4 40 CAM5 80 / ; VARIABLES X (I, J, K) quantities transported of product I to market K in truck J Y(J,K) total quantities of product I to market K in truck J ALFA (J, K) binary variable by which a truck can only go to a single market BETA (J) binary variable that marks with 1 the use of a truck and with zero the non-use Z cost of transportation ; POSITIVE VARIABLE X,Y ; BINARY VARIABLE ALFA,BETA ; EQUATIONS COSTE objetive function OFERTA(I) offer of the product I DEMANDA(K,I) demand of the merkat K TRANSPORTE(J) transport of the truck J DEFYJK(J,K) matching equation Y(J,K) and X(I,J,K) CAMER(J,K) equation of binary variable ALFA USOCAM(J); equation of binary variable BETA COSTE .. Z =E= SUM((J,K), Y(J,K)*BETA(J)*C(J)*L); OFERTA(I) .. SUM((J,K), X(I,J,K)) =L= A(I) ; DEMANDA(K,I) .. SUM(J, X(I,J,K) ) =G= D(K,I) ; TRANSPORTE(J) .. SUM((I,K) ,X(I,J,K)) =L= B(J); DEFYJK(J,K) .. Y(J,K) =E= SUM(I,X(I,J,K)); CAMER(J,K) .. Y(J,K) =E= SUM(I,X(I,J,K)*ALFA(J,K)); USOCAM(J) .. BETA(J) =E= SUM(K,ALFA(J,K)); MODEL DICIEMBRE2 /ALL/ ; SOLVE DICIEMBRE2 USING MINLP MINIMIZING Z ; DISPLAY X.L, Z.L, Y.L, ALFA.L, BETA.L; The lst. document tell me this........ MODEL DICIEMBRE2 OBJECTIVE Z TYPE MINLP DIRECTION MINIMIZE SOLVER DICOPT FROM LINE 80 **** SOLVER STATUS 1 Normal Completion **** MODEL STATUS 5 Locally Infeasible **** OBJECTIVE VALUE 0.0000 And this....... ** Warning ** The number of nonlinear derivatives equal to zero in the initial point is large (= 27 percent). A better initial point will probably help the optimization. Pre-triangular equations: 0 Post-triangular equations: 1 ** Infeasible solution. There are no superbasic variables. ** Warning ** The number of nonlinear derivatives equal to zero in the final point is large (= 22 percent). Better bounds on the variables may help the optimization. ** Warning ** The variance of the derivatives in the final point is large (= 4.1 ). A better scaling or better bounds on the variables will probably help the optimization.