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Matlab: Problem Based Optimization

March 4, 2018, 7:06 am
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MATLAB has a new way to express optimization problems. Until now they used a matrix-based API (this is now called: "Solver Based Optimization"). In 2017b they added "Problem Based Optimization" which is kind of a algebraic modeling language (like AMPL or GAMS) inside Matlab. Let's revisit a simple problem related to warehousing [3].

Mathematical Model


We use the following indices:
  • \(p\): products
  • \(f\): factories
  • \(w\): warehouses
  • \(s\): stores
We introduce the following variables:
  • \(x_{p,f,w} \ge 0\): shipments of product \(p\) from factory \(f\) to warehouse \(w\),
  • \(y_{s,w} \in \{0,1\}\): links each store \(s\) to a single warehouse \(w\).
The data associated with the model is:
  • \(pcost_{f,p}\): unit production cost
  • \(tcost_{p}\): unit transportation cost
  • \(dist_{f,w}, dist_{s,w}\): distances
  • \(pcap_{f,p}\): factory production capacities
  • \(wcap_{w}\): warehouse capacity
  • \(d_{s,p}\): demand for product \(p\) at store \(s\)
  • \(turn_{p}\): product turnover rate
The optimization model looks like:

MIP Model
\[\large{\begin{align}
\min&\sum_{p,f,w} (pcost_{f,p}+tcost_p\cdot dist_{f,w})\cdot x_{p,f,w}+\sum_{s,w,p} d_{s,p}\cdot tcost_p \cdot dist_{s,w} \cdot  y_{s,w}\\
&\sum_w x_{p,f,w} \le pcap_{f,p} \>\>\forall f,p \>\> \text{(production capacity)}\\
&\sum_f x_{p,f,w} = \sum_s d_{s,p}\cdot  y_{s,w} \>\>\forall p,w \>\>\text{(demand)}\\
&\sum_{p,s} \frac{d_{s,p}}{turn_{p}} y_{s,w} \le wcap_{w}\>\>\forall w \>\>\text{(warehouse capacity)}\\
&\sum_w y_{s,w} = 1\>\>\forall s \>\>\text{(one warehouse for a store)}\\
&x_{p,f,w} \ge 0 \\
&y_{s,w} \in \{0,1\}
\end{align}}\]

Matlab: Solver Based Implementation


This implementation uses the old matrix based API. The code is somewhat removed from the original problem as we need to use quite some non-trivial code to deal with the details in populating the solver matrices. This is still a very simple model: for more complex models this amount of code and its complexity will only increase. See [1] for more details.


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%-----------------------------------------------------
% Generate a random problem: Facility Locations
%-----------------------------------------------------

rng(1) % for reproducibility
N = 20; % N from 10 to 30 seems to work. Choose large values with caution.
N2 = N*N;
f = 0.05; % density of factories
w = 0.05; % density of warehouses
s = 0.1; % density of sales outlets

F = floor(f*N2); % number of factories
W = floor(w*N2); % number of warehouses
S = floor(s*N2); % number of sales outlets

xyloc = randperm(N2,F+W+S); % unique locations of facilities
[xloc,yloc] = ind2sub([N N],xyloc);

%-----------------------------------------------------
% Generate Random Capacities, Costs, and Demands
%-----------------------------------------------------

P = 20; % 20 products

% Production costs between 20 and 100
pcost = 80*rand(F,P) + 20;

% Production capacity between 500 and 1500 for each product/factory
pcap = 1000*rand(F,P) + 500;

% Warehouse capacity between P*400 and P*800 for each product/warehouse
wcap = P*400*rand(W,1) + P*400;

% Product turnover rate between 1 and 3 for each product
turn = 2*rand(1,P) + 1;

% Product transport cost per distance between 5 and 10 for each product
tcost = 5*rand(1,P) + 5;

% Product demand by sales outlet between 200 and 500 for each
% product/outlet
d = 300*rand(S,P) + 200;

%---------------------------------------------------------
% Generate Objective and Constraint Matrices and Vectors
%---------------------------------------------------------

obj1 = zeros(P,F,W); % Allocate arrays
obj2 = zeros(S,W);

distfw = zeros(F,W); % Allocate matrix for factory-warehouse distances
for ii = 1:F
for jj = 1:W
        distfw(ii,jj) = abs(xloc(ii) - xloc(F + jj)) + abs(yloc(ii) ...
            - yloc(F + jj));
end
end

distsw = zeros(S,W); % Allocate matrix for sales outlet-warehouse distances
for ii = 1:S
for jj = 1:W
        distsw(ii,jj) = abs(xloc(F + W + ii) - xloc(F + jj)) ...
            + abs(yloc(F + W + ii) - yloc(F + jj));
end
end

for ii = 1:P
for jj = 1:F
for kk = 1:W
            obj1(ii,jj,kk) = pcost(jj,ii) + tcost(ii)*distfw(jj,kk);
end
end
end

for ii = 1:S
for jj = 1:W
        obj2(ii,jj) = distsw(ii,jj)*sum(d(ii,:).*tcost);
end
end

obj = [obj1(:);obj2(:)]; % obj is the objective function vector

matwid = length(obj);

Aineq = spalloc(P*F + W,matwid,P*F*W + S*W); % Allocate sparse Aeq
bineq = zeros(P*F + W,1); % Allocate bineq as full

% Zero matrices of convenient sizes:
clearer1 = zeros(size(obj1));
clearer12 = clearer1(:);
clearer2 = zeros(size(obj2));
clearer22 = clearer2(:);

% First the production capacity constraints
counter = 1;
for ii = 1:F
for jj = 1:P
        xtemp = clearer1;
        xtemp(jj,ii,:) = 1; % Sum over warehouses for each product and factory
        xtemp = sparse([xtemp(:);clearer22]); % Convert to sparse
        Aineq(counter,:) = xtemp'; % Fill in the row
        bineq(counter) = pcap(ii,jj);
        counter = counter + 1;
end
end

% Now the warehouse capacity constraints
vj = zeros(S,1); % The multipliers 
for jj = 1:S
    vj(jj) = sum(d(jj,:)./turn); % A sum of P elements
end

for ii = 1:W
    xtemp = clearer2;
    xtemp(:,ii) = vj;
    xtemp = sparse([clearer12;xtemp(:)]); % Convert to sparse
    Aineq(counter,:) = xtemp'; % Fill in the row
    bineq(counter) = wcap(ii);
    counter = counter + 1;
end

Aeq = spalloc(P*W + S,matwid,P*W*(F+S) + S*W); % Allocate as sparse
beq = zeros(P*W + S,1); % Allocate vectors as full

counter = 1;
% Demand is satisfied:
for ii = 1:P
for jj = 1:W
        xtemp = clearer1;
        xtemp(ii,:,jj) = 1;
        xtemp2 = clearer2;
        xtemp2(:,jj) = -d(:,ii);
        xtemp = sparse([xtemp(:);xtemp2(:)]'); % Change to sparse row
        Aeq(counter,:) = xtemp; % Fill in row
        counter = counter + 1;
end
end

% Only one warehouse for each sales outlet:
for ii = 1:S
    xtemp = clearer2;
    xtemp(ii,:) = 1;
    xtemp = sparse([clearer12;xtemp(:)]'); % Change to sparse row
    Aeq(counter,:) = xtemp; % Fill in row
    beq(counter) = 1;
    counter = counter + 1;
end

%-----------------------------------------------------
% Bound Constraints and Integer Variables
%-----------------------------------------------------

intcon = P*F*W+1:length(obj);

lb = zeros(length(obj),1);
ub = Inf(length(obj),1);
ub(P*F*W+1:end) = 1;

opts = optimoptions('intlinprog','Display','off','PlotFcn',@optimplotmilp);

%-----------------------------------------------------
% Solve the Problem
%-----------------------------------------------------

[solution,fval,exitflag,output] = intlinprog(obj,intcon,...
                                     Aineq,bineq,Aeq,beq,lb,ub,opts);


Matlab: Problem Based Implementation


This implementation is using the new Matlab Problem Based Optimization language features [2]. In my opinion this is a big improvement. When reading this code we at least recognize the original problem. In addition we see the code is more compact.


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%-----------------------------------------------------
% Generate a random problem: Facility Locations
%-----------------------------------------------------

rng(1) % for reproducibility
N = 20; % N from 10 to 30 seems to work. Choose large values with caution.
N2 = N*N;
f = 0.05; % density of factories
w = 0.05; % density of warehouses
s = 0.1; % density of sales outlets

F = floor(f*N2); % number of factories
W = floor(w*N2); % number of warehouses
S = floor(s*N2); % number of sales outlets

xyloc = randperm(N2,F+W+S); % unique locations of facilities
[xloc,yloc] = ind2sub([N N],xyloc);

%-----------------------------------------------------
% Generate Random Capacities, Costs, and Demands
%-----------------------------------------------------

P = 20; % 20 products

% Production costs between 20 and 100
pcost = 80*rand(F,P) + 20;

% Production capacity between 500 and 1500 for each product/factory
pcap = 1000*rand(F,P) + 500;

% Warehouse capacity between P*400 and P*800 for each product/warehouse
wcap = P*400*rand(W,1) + P*400;

% Product turnover rate between 1 and 3 for each product
turn = 2*rand(1,P) + 1;

% Product transport cost per distance between 5 and 10 for each product
tcost = 5*rand(1,P) + 5;

% Product demand by sales outlet between 200 and 500 for each
% product/outlet
d = 300*rand(S,P) + 200;

%-----------------------------------------------------
% Generate Variables and Constraints
%-----------------------------------------------------

distfw = zeros(F,W); % Allocate matrix for factory-warehouse distances
for ii = 1:F
for jj = 1:W
        distfw(ii,jj) = abs(xloc(ii) - xloc(F + jj)) + abs(yloc(ii) ...
            - yloc(F + jj));
end
end

distsw = zeros(S,W); % Allocate matrix for sales outlet-warehouse distances
for ii = 1:S
for jj = 1:W
        distsw(ii,jj) = abs(xloc(F + W + ii) - xloc(F + jj)) ...
            + abs(yloc(F + W + ii) - yloc(F + jj));
end
end

x = optimvar('x',P,F,W,'LowerBound',0);
y = optimvar('y',S,W,'Type','integer','LowerBound',0,'UpperBound',1);

capconstr = sum(x,3) <= pcap';
demconstr = squeeze(sum(x,2)) == d'*y;
warecap = sum(diag(1./turn)*(d'*y),1) <= wcap';
salesware = sum(y,2) == ones(S,1);

%-----------------------------------------------------
% Create Problem and Objective
%-----------------------------------------------------

factoryprob = optimproblem;

objfun1 = sum(sum(sum(x,3).*(pcost'),2),1);

objfun2 = 0;
for p = 1:P
    objfun2 = objfun2 + tcost(p)*sum(sum(squeeze(x(p,:,:)).*distfw));
end

r = sum(distsw.*y,2); % r is a length s vector
v = d*(tcost(:));
objfun3 = sum(v.*r);

factoryprob.Objective = objfun1 + objfun2 + objfun3;

factoryprob.Constraints.capconstr = capconstr;
factoryprob.Constraints.demconstr = demconstr;
factoryprob.Constraints.warecap = warecap;
factoryprob.Constraints.salesware = salesware;

%-----------------------------------------------------
% Solve the Problem
%-----------------------------------------------------

opts = optimoptions('intlinprog','Display','off','PlotFcn',@optimplotmilp);
[sol,fval,exitflag,output] = solve(factoryprob,opts);


GAMS Implementation


For completeness, here is the GAMS representation [3]: 

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sets
  k 'locations'/factory1*factory20, warehouse1*warehouse20, store1*store40/
  f(k) 'factories'/factory1*factory20/
  w(k) 'warehouses'/warehouse1*warehouse20/
  s(k) 'stores'/store1*store40/
  p 'products'/product1*product20/
;

parameters
   xloc(k)    'locations: x-coordinates'
   yloc(k)    'locations: y-coordinates'
   pcost(f,p) 'unit production cost'
   pcap(f,p)  'production capacity'
   wcap(w)    'warehouse capacity'
   turn(p)    'product turnover rate'
   tcost(p)   'unit transportation cost'
   d(s,p)     'demand'
;
xloc(k) = uniformint(1,20);
yloc(k) = uniformint(1,20);
pcost(f,p) = uniform(20,100);
pcap(f,p) = uniform(500,1500);
wcap(w) = uniform(400*card(p),800*card(p));
turn(p) = uniform(1,3);
tcost(p) = uniform(5,10);
d(s,p) = uniform(200,500);


positive variable x(p,f,w) 'flow factory to warehouse';
binary variable y(s,w)     'assign warehouse to store';
variable z                 'objective variable';

equations
   obj          'objective'
   prodcap(f,p) 'production capacity'
   whcap(w)     'warehouse capacity'
   demand(p,w)
   assign(s)    'assign wh to store'
;

obj..
  z =e= sum((p,f,w), (pcost(f,p)+tcost(p)*distfw(f,w))*x(p,f,w))
        + sum((s,w,p), d(s,p)*tcost(p)*distsw(s,w)*y(s,w));

prodcap(f,p)..
sum(w,x(p,f,w)) =l= pcap(f,p);

whcap(w)..
sum((p,s),(d(s,p)/turn(p))*y(s,w)) =l= wcap(w);

demand(p,w)..
sum(f,x(p,f,w)) =e= sum(s, d(s,p)*y(s,w));

assign(s)..
sum(w,y(s,w)) =e= 1;

model m /all/;
option optcr=0;
solvem minimizing z using mip;


It is instructive to see the differences in approaches. Here are some highlights:

ExpressionMatlab (Model Based)GAMS
\[\sum_{p,f,w} pcost_{f,p} \cdot x_{p,f,w}\]
sum(sum(sum(x,3).*(pcost'),2),1)
sum((p,f,w), pcost(f,p)*x(p,f,w))
\[\sum_{p,f,w} tcost_p \cdot distfw_{f,w} \cdot x_{p,f,w}\]
objfun2 = 0;
for p = 1:P
   objfun2 = objfun2 + tcost(p)*sum(sum(squeeze(x(p,:,:)).*distfw));
end
sum((p,f,w), tcost(p)*distfw(f,w)*x(p,f,w))
\[\sum_w x_{p,f,w} \le pcap_{f,p}\]
sum(x,3) <= pcap'
sum(w,x(p,f,w)) =l= pcap(f,p)
\[\sum_f x_{p,f,w} = \sum_s d_{s,p}\cdot  y_{s,w}\]
squeeze(sum(x,2)) == d'*y
sum(f,x(p,f,w)) =e= sum(s, d(s,p)*y(s,w))
\[\sum_{p,s} \frac{d_{s,p}}{turn_{p}} y_{s,w} \le wcap_{w}\]
sum(diag(1./turn)*(d'*y),1) <= wcap'
sum((p,s),(d(s,p)/turn(p))*y(s,w)) =l= wcap(w)
\[\sum_w y_{s,w} = 1\]
sum(y,2) == ones(S,1)
sum(w,y(s,w)) =e= 1

There is a notable difference in number of different language constructs we use. In Matlab we use:

  1. Different versions of sum
  2. A loop
  3. Diag, transpose ('), element wise division (./)
  4. Squeeze
In GAMS we basically only use sum.


References



  1. Factory, Warehouse, Sales Allocation Model: Solver-Based, http://www.mathworks.com/help/optim/ug/factory-warehouse-sales-allocation-model.html
  2. Factory, Warehouse, Sales Allocation Model: Model-Based, http://www.mathworks.com/help/optim/examples/factory-warehouse-sales-allocation-model.html
  3. Matlab vs GAMS: Integer Programming, http://yetanothermathprogrammingconsultant.blogspot.com/2016/10/matlab-vs-gams-integer-programming.html
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