1function [Q,U,R,T,C,X,lG,totiter] = solver_mna_open(sn, options)
3config = options.config;
5if ~isfield(config,
'dep_scv')
6 config.dep_scv = 'qna'; % 'qna' for Whitt formula, 'etaqa' for QBD joint moments
12scv = sn.scv; scv(isnan(scv))=0;
36 case {SchedStrategy.FCFS, SchedStrategy.INF,SchedStrategy.PS}
38 pie{ist}{k} = map_pie(PH{ist}{k});
39 D0{ist,k} = PH{ist}{k}{1};
40 if any(isnan(D0{ist,k}))
41 D0{ist,k} = -GlobalConstants.Immediate;
43 PH{ist}{k} = map_exponential(GlobalConstants.Immediate);
57 if sn.nodetype(sn.stationToNode(jst)) ~= NodeType.Source
60 if rt((ist-1)*K+r, (jst-1)*K+s)>0
61 f2((ist-1)*K+r, (jst-1)*K+s) = 1; % C^2ij,r
68lambdas_inchain = cell(1,C);
69scvs_inchain = cell(1,C);
72 inchain = sn.inchain{c};
73 sourceIdx = sn.refstat(inchain(1));
74 lambdas_inchain{c} = sn.rates(sourceIdx,inchain);
75 scvs_inchain{c} = scv(sourceIdx,inchain);
76 lambda(c) = sum(lambdas_inchain{c}(isfinite(lambdas_inchain{c})));
77 d2c(c) = qna_superpos(lambdas_inchain{c},scvs_inchain{c});
78 T(sourceIdx,inchain
') = lambdas_inchain{c};
81d2(sourceIdx)=d2c(sourceIdx,:)*lambda'/sum(lambda);
85while (max(max(abs(a1_1-a1))) > options.iter_tol || max(max(abs(a2_1-a2))) > options.iter_tol )&& it <= options.iter_max %#ok<max>
90 % update throughputs at all stations
93 inchain = sn.inchain{c};
95 T(m,inchain) = V(m,inchain) .* lambda(c);
104 lambda_i = sum(T(ist,:));
108 a1(ist,r) = a1(ist,r) + T(jst,s)*rt((jst-1)*K+s, (ist-1)*K+r);
109 a2(ist,r) = a2(ist,r) + (1/lambda_i) * f2((jst-1)*K+s, (ist-1)*K+r)*T(jst,s)*rt((jst-1)*K+s, (ist-1)*K+r);
115 % update flow trhough queueing station
118 ist = sn.nodeToStation(ind);
121 case SchedStrategy.INF
124 d2(ist,s) = a2(ist,s);
128 inchain = sn.inchain{c};
130 T(ist,k) = a1(ist,k);
131 U(ist,k) = S(ist,k)*T(ist,k);
132 Q(ist,k) = T(ist,k).*S(ist,k)*V(ist,k);
133 R(ist,k) = Q(ist,k)/T(ist,k);
136 case SchedStrategy.PS
138 inchain = sn.inchain{c};
140 TN(ist,k) = lambda(c)*V(ist,k);
141 UN(ist,k) = S(ist,k)*TN(ist,k);
143 %Nc = sum(sn.njobs(inchain)); % closed population
144 Uden = min([1-GlobalConstants.FineTol,sum(UN(ist,:))]);
146 %QN(ist,k) = (UN(ist,k)-UN(ist,k)^(Nc+1))/(1-Uden); % geometric bound type approximation
147 QN(ist,k) = UN(ist,k)/(1-Uden);
148 RN(ist,k) = QN(ist,k)/TN(ist,k);
151 case {SchedStrategy.FCFS}
152 mu_ist = sn.rates(ist,1:K);
153 mu_ist(isnan(mu_ist))=0;
154 rho_ist_class = a1(ist,1:K)./(GlobalConstants.FineTol+sn.rates(ist,1:K));
155 rho_ist_class(isnan(rho_ist_class))=0;
156 lambda_ist = sum(a1(ist,:));
157 mi = sn.nservers(ist);
158 rho_ist = sum(rho_ist_class) / mi;
159 if rho_ist < 1-options.tol
160 if strcmp(config.dep_scv,
'etaqa') && mi == 1
161 % ETAQA-based departure SCV via QBD joint moments
163 % fit aggregate arrival MAP from parametric info
164 arri_agg = APH.fitMeanAndSCV(1/lambda_ist, sum(a2(ist,:))).getProcess;
165 serv_agg = APH.fitMeanAndSCV(1/sum(mu_ist(mu_ist>0).*a1(ist,mu_ist>0))/lambda_ist, ...
166 sum(a1(ist,:).*scv(ist,:))/lambda_ist).getProcess;
167 JM = qbd_depproc_jointmom(arri_agg, serv_agg, [1,0; 2,0]);
168 E1 = JM(1); E2 = JM(2);
169 d2(ist) = (E2 - E1^2) / E1^2;
171 % fall back to QNA formula on failure
173 mubar(ist) = lambda_ist ./ rho_ist;
177 c2(ist) = c2(ist) + a1(ist,r)/lambda_ist * (mubar(ist)/mi/mu_ist(r))^2 * (scv(ist,r)+1 );
181 d2(ist) = 1 + rho_ist^2*(c2(ist)-1)/sqrt(mi) + (1 - rho_ist^2) *(sum(a2(ist,:))-1);
186 mubar(ist) = lambda_ist ./ rho_ist;
190 c2(ist) = c2(ist) + a1(ist,r)/lambda_ist * (mubar(ist)/mi/mu_ist(r))^2 * (scv(ist,r)+1 );
194 d2(ist) = 1 + rho_ist^2*(c2(ist)-1)/sqrt(mi) + (1 - rho_ist^2) *(sum(a2(ist,:))-1);
198 Q(ist,k) = sn.njobs(k);
203 T(ist,k) = a1(ist,k);
204 U(ist,k) = T(ist,k) * S(ist,k) /sn.nservers(ist);
210 switch sn.nodetype(ind)
212 line_error(mfilename,
'Fork nodes not supported yet by QNA solver.');
218 % splitting - update flow scvs
221 if sn.nodetype(sn.stationToNode(jst)) ~= NodeType.Source
224 if rt((ist-1)*K+r, (jst-1)*K+s)>0
225 f2((ist-1)*K+r, (jst-1)*K+s) = 1 + rt((ist-1)*K+r, (jst-1)*K+s) * (d2(ist)-1);
237 ist = sn.nodeToStation(ind);
239 case {SchedStrategy.FCFS}
240 mu_ist = sn.rates(ist,1:K);
241 mu_ist(isnan(mu_ist))=0;
242 rho_ist_class = a1(ist,1:K)./(GlobalConstants.FineTol+sn.rates(ist,1:K));
243 rho_ist_class(isnan(rho_ist_class))=0;
244 lambda_ist = sum(a1(ist,:));
245 mi = sn.nservers(ist);
246 rho_ist = sum(rho_ist_class) / mi;
247 if rho_ist < 1-options.tol
250 arri_class = map_exponential(Inf);
252 arri_class = APH.fitMeanAndSCV(1/a1(ist,k),a2(ist,k)).getProcess; %
MMAP repres of arrival process
for class k at node ist
253 %arri_class = Erlang.fit(1/a1(ist,k),a2(ist,k)).getProcess;
254 %arri_class = map_exponential(1/a1(ist,k));
255 arri_class = {arri_class{1},arri_class{2},arri_class{2}};
258 arri_node = arri_class;
260 arri_node = mmap_super(arri_node,arri_class,
'default');
262 %arri_node = mmap_super_safe({arri_node,arri_class}, config.space_max,
'default'); % combine arrival process from different
class
266 [Qret{1:K}] = MMAPPH1FCFS({arri_node{[1,3:end]}}, {pie{ist}{:}}, {D0{ist,:}},
'ncMoms', 1);
267 Q(ist,:) = cell2mat(Qret);
270 Q(ist,k) = sn.njobs(k);
274 R(ist,k) = Q(ist,k) ./ T(ist,k);
293function [d2]=qna_superpos(lambda,a2)
294a2 = a2(isfinite(lambda));
295lambda = lambda(isfinite(lambda));
296d2 = a2(:)
'*lambda(:) / sum(lambda);