Equalities with uncertainty
If I had a million dollars for every time I’ve had to explain this, I would definitely not write this post.
Trying to develop robust optimization models with uncertainty appearing in equality constraints is a common mistake, and the reason people do this mistake is almost always due to failure to

understand the difference between a decision variable and an expression involving decision variables

understand the difference between an equality constraint == and an assignment =

understand the difference between a decison variable and a policy or decision rule.
How to buy a horse
To understand the problem, we work with the following problem.
Alice and Bob are going to the market to buy a horse. They will pay the horse by trading with sheep. They do not know the current market price p of a horse, all they know is that it cost somewhere between 50 and 190 sheep. Alice and Bob will bring the sheep in their trucks. Alice will bring a sheep in her truck, while Bob will bring b sheep. They have been told by their manager that they can under no circumstances bring any sheep back to the farm, and they can leave no sheep at the market but all sheep must be used to pay for the horse, and they must pay the market price and nothing else. Both Alice and Bobs truck can pack 200 sheep.
To figure out how many sheep they should bring in their trucks when going to the market, they must thus solve the uncertain feasibility problem
\[0 \leq a \leq 200, 0 \leq b \leq 200, a + b = p~\forall~50 \leq p \leq 190\]The corresponding YALMIP model would be
sdpvar a b p
Model = [0 <= a <= 200, 0 <= b <= 200, a + b == p, 50 <= p <= 190, uncertain(p)]
It should be pretty obvious that this is a nonsense problem. It is completely impossible to select the fixed decisions a and b at the farm, go the market and figure out the price, and magically have the correct number of sheep in their trucks. Indeed, if you try to solve the optimization problem, it will be infeasible. You simply cannot have a linear equality which involves both decisions variables and uncertain variables.
Let us untangle what has gone wrong. The first issue is that the setup simply doesn’t make sense in reallife, which translates to the incorrect use of an equality ==. So what could the manager have meant that they should do, and how does that translate to the model.
It could be that the manager meant that Alice first should load her truck with a sheep (make a fixed decision), go to the market and check the price, and then call Bob and have him bring the remaining sheep. This is a causal structure where Bobs decision b really isn’t a decision, but a simple function of Alice decision and the uncertainty, b = pd. An incorrect model of this would be
sdpvar a b p
Model = [0 <= a <= 200, 0 <= b <= 200, b == pa, 50 <= p <= 190, uncertain(p)]
The problem with this model is that we are still saying that Bobs load is a fixed decision (since it is its own decision variable), while we really only want it to be a function of Alice load and the market price. We thus do not want to model an equality between two independent decision variables and an uncertain price, but simply define the linear map that arise due to the causal (timedependent order) structure in the problem. The correct model would thus be
sdpvar a p
b = pa
Model = [0 <= a <= 200, 0 <= b <= 200, 50 <= p <= 190, uncertain(p)]
In this model there is only 1 decision variable (a). Bobs load is simply an assignment from fixed decisions and uncertainties. We’ve introduced an intermediate placeholder for our convenience, but what YALMIP sees here is really
sdpvar a p
Model = [0 <= a <= 200, 0 <= pa <= 200, 50 <= p <= 190, uncertain(p)]
Another version could be that the manager meant that Alice and Bob actually should pick up the phone and ask what the price is, and then act accordingly. Once again, that simply means we want to define a fixed map from the price to the loads, and we no longer have any decision variables at all. One such decision rule, or policy, is that they always use \(a = \frac{p}{2}\) and \(b = \frac{p}{2}\). In other words, our model would be
sdpvar p
a = p/2
b = p/2
Model = [0 <= a <= 200, 0 <= b <= 50, 50 <= p <= 190, uncertain(p)]
This model is a bit too simple (we have no decision variables!) but it illustrates the idea of a policy compared to a decision. An improvement which could be useful in a more complex scenario is to parameterize the policy. The manager thinks Alice and Bob are too dumb to load cleverly once they know the price (Alice truck is bigger so perhaps they should load more sheep in her truck). Hence, he wants to create a function that they can use to select the number of cheap to load. One such policy is a linear decision rule \(a = t_0 + t_1p, b = s_0 + s_1p\). As long as \(t_0+s_0 = 0\) and \(t_1+s_1 = 1\), they will bring the correct amount. Here, we thus decide upon the decision rule parameters before knowing the price of the horse, but once the price is known, we have a method to distribute the load.
sdpvar p t0 t1 s0 s1
a = t0 + t1*p;
b = s0 + s1*p;
Model = [0 <= a <= 200, 0 <= b <= 200, t0+s0 == 0, t1+s1 == 1, 50 <= p <= 190, uncertain(p)]
We explictly derived the condition necessary on the policy parameters for it to be correct. This can be done automatically by exploiting the fact that an equality involving uncertainties make no sense. The following model will work equivalently
sdpvar p t0 t1 s0 s1
a = t0 + t1*p;
b = s0 + s1*p;
Model = [0 <= a <= 200, 0 <= b <= 200, a + b == p, 100 <= p <= 310, uncertain(p)]
So how can this work, since we’ve just learned that you cannot have uncertainties in equalities? When YALMIP finds the equality involving the uncertainty, it will derive the conditions required for the equality to be reasonable, i.e., it will derive the conditions necessary for the decision variables in order to completely eliminate any uncertainty in the equality. In this case, it will see the equality \(t_0 + s_0 + (t_1 + s_1)p = p\), and the only way this can be feasible for uncertain \(p\) is that \(t_1+s_1=1\), and the remaining term in the equality then says \(t_0 + s_0 = 0\).
A warehouse logistics problem
In this second example, we address precisely the same mistakes and correct them in the same way.
Alice manages a warehouse selling goods. The stock of goods available in the warehouse on the morning of day \(i\) is \(w_i\). Everyday, she sells \(s_i\) items but this quantity is fluctuating so all we know apriori is that \(200 \leq s_i \leq 1000\). She also receives delivery of supplies every day. The delivery is not instant but arrives in the morning two days after the order was made (order in the evening of day \(i\) arrives in morning of day \(i+2\)). We call the order \(u_i\). We cannot remove or ship back inventory, i.e. \(u_i \geq 0\). Some customers are not happy with the product, so 10% of all sold items are returned two days after it was sold, for simplicity this is assumed to occur in the morning. Collecting all information, we have the dynamical system \(w_i = w_{i1}  s_{i1} + u_{i2} + 0.1s_{i2}\) The warehouse can only stock 8000 items, and we must always have enough in stock to be able to sell to all prospective customers. At the same time, keeping a large stock is a waste of capital resources, so it should be kept as small as possible. To model that, our goal it to minimize the predicted sum of the stock over the coming days.
Let us start by setting up a typical incorrect model. We assume we are making plans for our restocking for 10 days ahead, and we assume we start from scratch in a situation where we have 2500 items in the inventory (typically you would have some history of sales and restocking orders that you would have to include, and we do this in the final version). To make it even more realistic in terms of what a naive user would do, we write the code in an ugly nonvectorized fashion, and we will also write the dynamical update in a way that hides the simple causality structure. Note that we have to make sure we satisfy the warehouse capabilities both in the morning when new supplies arrive, and in the evening to ensure we aren’t running out of stock during the day.
sdpvar w1 w2 w3 w4 w5 w6 w7 w8 w9 w10
sdpvar s1 s2 s3 s4 s5 s6 s7 s8 s9 s10
sdpvar u1 u2 u3 u4 u5 u6 u7 u8
Model = [uncertain([s1 s2 s3 s4 s5 s6 s7 s8 s9]),
200 <= [s1 s2 s3 s4 s5 s6 s7 s8 s9] <= 1000,
w1 == 1200,
w2 + s1 == w1,
w3 + s2 == w2 + u1 + 0.1*s1,
w4 + s3 == w3 + u2 + 0.1*s2,
w5 + s4 == w4 + u3 + 0.1*s3,
w6 + s5 == w5 + u4 + 0.1*s4,
w7 + s6 == w6 + u5 + 0.1*s5,
w8 + s7 == w7 + u6 + 0.1*s6,
w9 + s8 == w8 + u7 + 0.1*s7,
w10 + s9 == w9 + u8 + 0.1*s8,
0 <= [w1 w2 w3 w4 w5 w6 w7 w8 w9 w10] <= 8000,
0 <= [w1s1 w2s2 w3s3 w4s4 w5s5 w6s6 w7s7 w8s8 w9s9 w10s10],
0 <= [u1 u2 u3 u4 u5 u6 u7 u8]]
Cost = w1+w2+w3+w4+w5+w6+w7+w8+w9+w10
Let us untangle all the mistakes. First, we have written the dynamical update of the stock in a way that hides the simple update law. It looks like all variables are coupled by a large number of equality constraints, but that’s not true. The stock is completely and uniquely goverened by a linear timeinvariant dynamical system with states \(w_i\) and external inputs \(u_i\) and \(s_i\). Secondly, the future stock is not a decision variable, it is an expression built from the initial stock, the sequence of sales, and the sequence of restocking order (which is the only decision variable). Hence, a first step would be create the predicted future states using the corresponding assignments
sdpvar s1 s2 s3 s4 s5 s6 s7 s8 s9 s10
sdpvar u1 u2 u3 u4 u5 u6 u7 u8
w1 = 2500;
w2 = w1s1;
w3 = w2s2+u1+0.1*s1;
w4 = w3s3+u2+0.1*s2;
w5 = w4s4+u3+0.1*s3;
w6 = w5s5+u4+0.1*s4;
w7 = w6s6+u5+0.1*s5;
w8 = w7s7+u6+0.1*s6;
w9 = w8s8+u7+0.1*s7;
w10 = w9s9+u8+0.1*s8;
Model = [uncertain([s1 s2 s3 s4 s5 s6 s7 s8 s9 s10]),
200 <= [s1 s2 s3 s4 s5 s6 s7 s8 s9 s10] <= 1000,
0 <= [w1 w2 w3 w4 w5 w6 w7 w8 w9 w10] <= 8000,
0 <= [w1s1 w2s2 w3s3 w4s4 w5s5 w6s6 w7s7 w8s8 w9s9 w10s10],
0 <= [u1 u2 u3 u4 u5 u6 u7 u8]]
Cost = w1+w2+w3+w4+w5+w6+w7+w8+w9+w10
This model makes sense physically. Our only decisions are the restocking orders for the coming 10 days, and there are no equalities involving uncertainties, but all future warehouse scenarios are simple maps from realizations of sales, and the restocking plan. Note that w10 is a complex expression containing all sales variables and all restocking order variables.
From a planning perspecive, the model is horrible though. It says Alice has to decide all future restocking orders now, before knowing anything about how the sales goes. We are forcing a fixed decision on all future 8 restocking orders already today, and not epxloiting the fact that our purchase order two days ahead should be based on how much we sell today and tomorrow. We will improve that soon, but first let us solve this so called openloop problem, or nonrecourse.
optimize(Model,Cost)
value([u1 u2 u3 u4 u5 u6 u7 u8])
ans =
400 900 900 900 900 900 900 900
If you let the sales be at its maximum, you will see that this fixed plan satisfies the constraints in that the warehouse will be completely emptied every day, but restocked in the morning to be able to handle a worstcase maximum sale scenario once again (900 shipped in for restocking, 100 returned from two days back, and then every sold out again). It will also handle the worstcase that only 200 items are sold everyday, and the warehouse will manage to keep the stock smaller than the upper limit of 8000 items every day. All combinations in between will also be handled, as that is the whole concept of robust optimization.
If we are interested in the worstcase cost, we cannot look at the value of the variable Cost, as this expression involves uncertain variables, and these do not have any particular computed value. In the optimization, we have only ensured that the constraints are satisfied for all possible realizations of the uncertainty, and not computed any explicit worstcase realization. In the same sense, all we know is that the worstcase cost has been minimized. To extract this worstcase cost, we simply add a bound on the cost, minimize this bound, and look at the bound instead
sdpvar t
optimize([Model,Cost <= t],t)
value(t)
ans =
45120
In other words, on average over the 10 days, we will have a stock of 4512 in the worstcase (which is when only 200 items are sold so the warehouse inventory builds up to unnecessarily large volumes).
Clearly, the fixed plan is stupid. A first improvement is to change the restocking plan to take known sales into account. If we sell \(s_i\) items tomorrow, we can already now say that we should make an order tomorrow evening which compensates for the sales, and then let the optimization procedure adjust this with some fixed plan to optimize the worstcase cost. Hence, we use \(u_i = v_i + s_i\) where \(v_i\) is our decision variable.
sdpvar s1 s2 s3 s4 s5 s6 s7 s8 s9 s10
sdpvar v1 v2 v3 v4 v5 v6 v7 v8
w1 = 2500;
u1 = v1 + s1;
w2 = w1  s1;
u2 = v2 + s2;
w3 = w2s2+u1+0.1*s1;
u3 = v3 + s3;
w4 = w3s3+u2+0.1*s2;
u4 = v4 + s4;
w5 = w4s4+u3+0.1*s3;
u5 = v5 + s5;
w6 = w5s5+u4+0.1*s4;
u6 = v6 + s6;
w7 = w6s6+u5+0.1*s5;
u7 = v7 + s7;
w8 = w7s7+u6+0.1*s6;
u8 = v8 + s8;
w9 = w8s8+u7+0.1*s7;
w10 = w9s9+u8+0.1*s8;
Model = [uncertain([s1 s2 s3 s4 s5 s6 s7 s8 s9 s10]),
200 <= [s1 s2 s3 s4 s5 s6 s7 s8 s9 s10] <= 1000,
0 <= [w1 w2 w3 w4 w5 w6 w7 w8 w9 w10] <= 8000,
0 <= [w1s1 w2s2 w3s3 w4s4 w5s5 w6s6 w7s7 w8s8 w9s9 w10s10],
0 <= [u1 u2 u3 u4 u5 u6 u7 u8]]
Cost = w1+w2+w3+w4+w5+w6+w7+w8+w9+w10;
sdpvar t
optimize([Model,Cost <= t],t)
value(t)
ans =
19660
This simple rule leads to a massively improved worstcase capital cost. Can we improve it by not just restocking exactly the sold items plus or minus some fixed term, but optimize over this policy?
sdpvar s1 s2 s3 s4 s5 s6 s7 s8 s9 s10
sdpvar t1 t2 t3 t4 t5 t6 t7 t8
w1 = 2500;
u1 = v1 + t1*s1;
w2 = w1  s1;
u2 = v2 + t2*s2;
w3 = w2s2+u1+0.1*s1;
u3 = v3 + t3*s3;
w4 = w3s3+u2+0.1*s2;
u4 = v4 + t4*s4;
w5 = w4s4+u3+0.1*s3;
u5 = v5 + t5*s5;
w6 = w5s5+u4+0.1*s4;
u6 = v6 + t6*s6;
w7 = w6s6+u5+0.1*s5;
u7 = v7 + t7*s7;
w8 = w7s7+u6+0.1*s6;
u8 = v8 + t8*s8;
w9 = w8s8+u7+0.1*s7;
w10 = w9s9+u8+0.1*s8;
Model = [uncertain([s1 s2 s3 s4 s5 s6 s7 s8 s9 s10]),
200 <= [s1 s2 s3 s4 s5 s6 s7 s8 s9 s10] <= 1000,
0 <= [w1 w2 w3 w4 w5 w6 w7 w8 w9 w10] <= 8000,
0 <= [w1s1 w2s2 w3s3 w4s4 w5s5 w6s6 w7s7 w8s8 w9s9 w10s10],
0 <= [u1 u2 u3 u4 u5 u6 u7 u8]]
Cost = w1+w2+w3+w4+w5+w6+w7+w8+w9+w10;
sdpvar t
optimize([Model,Cost <= t],t)
value(t)
ans =
19660
No improvement. However, since the dynamics evolve over several days with returned items and deliveries, we should perhaps use more information in our policies. The restocking order next week should be allowed to use all information gathered up until that evening.
sdpvar s1 s2 s3 s4 s5 s6 s7 s8 s9 s10
sdpvar v1 v2 v3 v4 v5 v6 v7 v8
sdpvar t1 t2(1,2) t3(1,3) t4(1,4) t5(1,5) t6(1,6) t7(1,7) t8(1,8)
w1 = 2500;
u1 = v1 + t1*s1;
w2 = w1  s1;
u2 = v2 + t2*[s1 s2]';
w3 = w2s2+u1+0.1*s1;
u3 = v3 + t3*[s1 s2 s3]';
w4 = w3s3+u2+0.1*s2;
u4 = v4 + t4*[s1 s2 s3 s4]';
w5 = w4s4+u3+0.1*s3;
u5 = v5 + t5*[s1 s2 s3 s4 s5]';
w6 = w5s5+u4+0.1*s4;
u6 = v6 + t6*[s1 s2 s3 s4 s5 s6]';
w7 = w6s6+u5+0.1*s5;
u7 = v7 + t7*[s1 s2 s3 s4 s5 s6 s7]';
w8 = w7s7+u6+0.1*s6;
u8 = v8 + t8*[s1 s2 s3 s4 s5 s6 s7 s8]';
w9 = w8s8+u7+0.1*s7;
w10 = w9s9+u8+0.1*s8;
Model = [uncertain([s1 s2 s3 s4 s5 s6 s7 s8 s9 s10]),
200 <= [s1 s2 s3 s4 s5 s6 s7 s8 s9 s10] <= 1000,
0 <= [w1 w2 w3 w4 w5 w6 w7 w8 w9 w10] <= 8000,
0 <= [w1s1 w2s2 w3s3 w4s4 w5s5 w6s6 w7s7 w8s8 w9s9 w10s10],
0 <= [u1 u2 u3 u4 u5 u6 u7 u8]]
Cost = w1+w2+w3+w4+w5+w6+w7+w8+w9+w10;
sdpvar t
optimize([Model,Cost <= t],t)
value(t)
ans =
19660
Nope. Taking todays sale into account is sufficient in this scenario. At this point though, it shoulds be evident that one should vectorize the code. We do that, but go back to our simple policy.
s = sdpvar(1,10);
v = sdpvar(1,9);
t = sdpvar(1,9);
w = []
u = [];
for i = 1:10
if i == 1
w = [w 2500];
elseif i == 2
w = [w w(i1)s(i1)];
else
w = [w w(i1)s(i1)+u(i2)+0.1*s(i2)];
end
if i <= 9
u = [u v(i)+s(i)];
end
end
Model = [uncertain(s),
200 <= s <= 1000,
0 <= w <= 8000,
0 <= ws,
0 <= u]
Cost = sum(w);
sdpvar t
optimize([Model,Cost <= t],t)
We can take this one step further and see the dynamics as a standard discretetime dynamical system. Let us denote the state \(x_i\), and this state contains \(w_i, s_{i1}\) and \(u_{i1}\). We have \(x_{i+1} = Ax_i + Bu_i + Gs_i = \left(\begin{matrix}1 & 0.1 & 1\\ 0 & 0 & 0\\ 0 & 0 & 0\end{matrix}\right)x_i + \left(\begin{matrix} 0\\ 0\\ 1\end{matrix}\right)u_i+ \left(\begin{matrix} 1\\ 1\\ 0\end{matrix}\right)s_i\)
With this notation, the prediction model is very compact. Note that we use unnecessarily many \(u\), but that’s just to keep the code simple. They are defined and constrained, but never used in the predictions.
s = sdpvar(1,10);
v = sdpvar(1,9);
A = [1 0.1 1;
0 0 0;
0 0 0]
B = [0 0 1]';
G = [1 1 0]';
x = [2500;0;0];
u = [];
for i = 1:9
u = [u s(i) + v(i)];
x = [x A*x(:,i) + B*u(i) + G*s(i)];
end
Model = [uncertain(s),
200 <= s <= 1000,
0 <= x(1,:) <= 8000,
0 <= x(1,:)s,
0 <= u]
Cost = sum(x(1,:));
sdpvar t
optimize([Model,Cost <= t],t)
At this point, one should note that this is a very special case of robust model predictive control
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