\$title Extended transport model with stochastic demand and costs (tr20,SEQ=84) * In some of these models we have huge scenario trees. DECIS will solve this by * some internal sampling routines, while DE and LINDO should terminate with a * useful error message. set c ck(c) Center cc(c) Cities stoch / val, prob / r / l, m, h /; parameter coord(c,*); parameter dist(c,c); parameter b(c,c); parameter rv(stoch,r) alias (c,cp); \$gdxin tr20_scen \$load c ck cc coord dist b rv Parameters cap Maximum capacity of one truck / 10.0 / cf Transportation cost per mile and per truck - full run / 0.2 / ce Transportation cost per mile and per truck - empty run / 0.18 / b(c,c) Demand of center and cities maxt Maximum amount of trucks; set dnet(c,c); dnet(ck,cc) = yes; dnet(cc,ck) = yes; set enet(c,c); enet(c,cp) = not sameas(c,cp); maxt=sum(dnet(c,cp), b(dnet)); Parameter df(c,c) random demand factor cr(c,c) Recourse cost (rent-a-truck) per mile crr(c,r) stochastic outcome of cr; \$load crr * ----------------------------------------------- * define the core model * ----------------------------------------------- Free Variable z total cost; Positive Variables f(c,c) Full runs e(c,c) Empty runs y(c,c) Recourse a(c) Allocation stayat(c) Trucks staying at c - i.e. no full or empty runs; Equations tcosts define objective function demand(c,c) serve demand of center and all cities node(c) node constraint for the trucks maxtruck maximum number of trucks to be allocated; tcosts .. z =e= sum(dnet(c,cp), dist(dnet)*(cf*f(dnet) + cr(dnet)*y(dnet))) + sum(enet(c,cp), ce*dist(enet)*e(enet)); demand(dnet(c,cp)).. f(dnet)*cap + y(dnet) =g= df(dnet)*b(dnet); node(c) .. sum(dnet(c,cp), f(c,cp)) + sum(enet(c,cp), e(enet)) + stayat(c) =e= a(c) + sum(enet(cp,c), e(enet)); maxtruck.. cap*sum(c, a(c)) =l= 0.9*maxt; Model transport /tcosts,demand,node,maxtruck/; Set s scenarios / s1*s100 /; parameters s_df(s,c,c); Set dict / s .scenario.'' df .randvar. s_df/; df(dnet) = 1; cr(dnet(c,cp)) = crr(c,'m'); file emp / '%emp.info%' /; put emp '* problem %gams.i%'; loop(dnet, put / 'randvar ' df.tn(dnet) ' discrete ' loop(r, put rv('prob',r):5:2 rv('val',r):5:2)); put / 'stage 2 df f e y stayat demand node'; putclose; solve transport min z using emp scenario dict; * Sampling Scalar h1; loop(s, loop(dnet, h1=uniform(0,1); if( h1