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hpmgendpr.m
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hpmgendpr.m
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%Author: Zsolt T. Kosztyan Ph.D habil., University of Pannonia,
%Faculty of Economics, Department of Quantitative Methods
%----------------
%Calculate an optimal project structure matrix and pareto-optimal resource
%allocation for pareto-optimal resource balanced hybrid discrete time-cost
%trade-off problem (PO-HDTCTP)
%The algorithm is the implementation of Hybrid Project Management agent
%(HPMa) by hybrid genetic algorithm and a multi-genetic algorithm to
%specify pareto-optimal resource allocation
%----------------
%Output:
%PSM: An N by N+2+nR+1 PSM=[DSM,TD,CD,RD,SST] matrix, where
% DSM is an N by N upper triangular binary matrix of the calculated logic
% domain
% TD is an N by 1 column vector of task durations
% CD is an N by 1 column vector of cost demands
% RD is an N by nR matrix of resource demands
% SST is an N by 1 column vector of scheduled start time of tasks
%----------------
%Inputs:
%PDM: PDM=[PEM,TD,CD,RD] matrix (N by N+(2+nR)w),
%where PEM is an N by N upper triangular matrix of logic domain,
% TD is an N by w matrix of task durations
% CD is an N by w matrix of cost demands
% RD is an N by w*nR matrix of resource demands
%const: 3 elements row vector of [Ct,Cc,Cs] values, where
% Ct is the time constraint (max constraint)
% Cc is the cost constraint (max constraint)
% Cs is the score constraint (min constraint)
%typefcn: Type of target function
% 1=minTPT, 2=minTPC, 3=maxTPS, ~ composite
%w: Number of modes
%----------------
%Usage:
%PSM=hpmgendpr(PDM,const,typefcn,w)
%----------------
%Example:
%PDM=[triu(rand(10)*.5+.5),20*rand(10,3),30*rand(10,3),5*rand(10,6)];
%const=[percentt(PDM,3,.9),percentc(PDM,3,.9),percents(PDM,.7)];
%typefcn=999; %let target function be a composite target function
%w=3; %set number of modes to 3
%tic;PSM=hpmgendpr(PDM,const,typefcn,w);toc
%----------------
%Prepositions and Requirements:
%1.)The logic domian must be an upper triangular matrix, where the matrix
%elements are between 0 to 1 interval.
%2.)At least one matrix element is lower than 1 and upper than 0. => There
%are at least one decision variable
%3.)The number of modes(w) is a positive integer
%4.)The number of resources (nR) is a positive number
%5.)The elements of Time/Cost/Resource Domains are positive real numbers.
%6.)Usually a monotonity are assumed, which, means: if tk,i<tk,j =>
%ck,i>=ck,j, where 1<=k<=N is the k-th task, 1<=i,j<=w are the selected
%modes. Nevertheless, this assumption is not required in this simulation.
function PSM=hpmgendpr(PDM,const,typefcn,w)
global PEM P Q T C R Ct Cc Cs Typ
%global values:
% PEM is the input logic domain
% P is the score matrix of inclusion task dependencies/completions
%the logic domain (initially PEM=P)
% Q is the score matrix of exclusion task dependencies/completions.
%In this simulation Q=1-P
% T is the N by w matrix of time demands (=the time domain)
% C is the N by w matrix of cost demands (=the cost domain)
% R is the N by w*nR matrix of resource demands (=the resource domain)
% Ct is the time constraint
% Cc is the cost constraint
% Cs is the score constraint
% Typ is the selection number of the target functions (see above)
%---Initialization of the global values---
Typ=typefcn;
Ct=const(1);
Cc=const(2);
Cs=const(end);
N=size(PDM,1); %The number of activities
pem=triu(PDM(:,1:N)); %only the upper triangle will be considered
T=PDM(:,N+1:N+w); %N+1..N+w-th columns in PDM matrix is the time domain
C=PDM(:,N+1+w:N+2*w); %N+w+1..N+2*w-th columns in PDM matrix is the cost
%domain
R=PDM(:,N+1+2*w:end); %N+2*w+1..N+(2+nR)*w-th columns in PDM matrix is the
%resource domain
PEM=pem;
P=pem;
Q=ones(N)-P; %Q=1-P
%---End of the initialization of the global values---
n=numel(PEM(PEM>0&PEM<1));
MinPopSize=100; %Minimal number of population
PopSize=min(max(1000,round((2^n)/100)),MinPopSize); %The population size
%depends on the number of uncertainties
%---Set of genetic algorithm (GA)---
chromosomedq=initpopdq(PopSize); %Specify the initial population
% Display:off = There is no need any information to display
% PopulationSize:PopSize = The Population size in a parallel running is
%PopSize
% Vectorized:on and UseParallel:true parallelize the genetic algorithm
% Generations:100 and StallGenLimit:100 sets the maximal number of
%generations to 100
% TolCon:1e-6 means, that the GA will stop, if the given tolerance is
%reached
% CreationFcn:@createpemdq specifies createpemdq function when generating
%population.
% HybridFcn:@fmincon specifies a hybrid function which is used after
%running GA.
% InitialPopulation:chromosomedq chromosomedq predefines set of chromosomes
%are used as an initial population
% CrossoverFcn:@crossoverhybridd specifies a unique crossoverhybridd
%crossover function, which recombines two PSM=[DSM,MODES] matrices
% MutationFcn:@mutationadaptfeasible randomly generates directions that are
%adaptive with respect to the last successful or unsuccessful generation
% SelectionFcn:{@selectiontournament,4} Tournament selection chooses each
%parent by choosing�Tournament size�players at random and then choosing the
%best individual out of that set to be a parent.�Tournament size�is�4.
options=gaoptimset('Display','off','PopulationSize',PopSize,...
'Vectorized','on','UseParallel',true,'Generations',100,...
'StallGenLimit',100,'TolCon',1e-6,'InitialPopulation',chromosomedq,...
'CreationFcn',@createpemdq,'HybridFcn',@fmincon,...
'CrossoverFcn',@crossoverhybridd,...
'MutationFcn',@mutationadaptfeasible,...
'SelectionFcn',{@selectiontournament,4});
LB=[zeros(1,numel(PEM(PEM>0&PEM<1))),ones(1,N)]; %Lower Bound
UB=[ones(1,numel(PEM(PEM>0&PEM<1))),ones(1,N)*w];%Upper Bound
%---End of setting GA---
%---Runing GA---
% @targetfcnd: the target function (=targetfcnd)
% n+N: the number of variables (n=number of uncertainties + N=number of
%activities
% LB,UB: Lower/Upper Bounds
% @constrainrtpard: The nonlinear constraint function
% options: The set of options (see above).
%Note: Despite binary and integer values have to be seeked, in order to
%apply custom creation, crossover and hybrid functions the values
%considered as rounded continuous values
chromosome=ga(@targetfcnd,n+N,[],[],[],[],LB,UB,@constrainrtpard,options);
%---End of runing GA---
%---Start of output formatting---
PSM=updatepemd(chromosome); %PSM=[DSM,MODES] = Logic Domain and the vector
%of the selected modes
PSM(diag(PSM)==0,:)=0; %All excluded tasks dependencies/time demands/
PSM(:,diag(PSM)==0)=0; %cost demands are erased from the PSM matrix
PSM=pmtopsmr([PSM,zeros(N,1)],T,C,R); %The output PSM = [DSM,TD,CD,RD,EST]
PSM=PSM(:,1:end-1); %cut the last column
%---Start of Pareto Optimizing---
SST=multires_solve(PSM(:,1:N),PSM(:,N+1),PSM(:,N+3:end)); %Specify an SST
%for a Pareto-Optimal resource allocation
%---End of Pareto Optimizing---
PSM=[PSM,SST]; %Let SST be the last column
PSM(diag(PSM)==0,:)=0; %All excluded tasks dependencies/time demands/
PSM(:,diag(PSM)==0)=0; %cost demands are erased from the PSM matrix
%---End of output formatting---