Integer constraints allow you to create models with these important features:
Implications, such as "If Condition A holds then Condition B holds."
Transaction costs or setup costs, such as "The cost of an item is zero if I buy zero of the item, but the cost is $A transaction cost plus $B per item if I buy more than zero." See Example: Fixed Cost.
Logical constraints, such as "Airlock door A and door B cannot both be open at the same time."
Many modeling problems are equivalent to logical models that use indicator variables. This topic describes how to use indicator variables and logical models. These models are based on the Big-M formulation, where a variable x and a constant M are assumed to satisfy the inequalities –M ≤ x ≤ M.
Recall that constraints in optimization have an implied "and." Constraints c1, c2, and c3 are satisfied when all three constraints are satisfied: c1 and c2 and c3.
Suppose you have a continuous variable x that is bounded above by a constant M:
x ≤ M.
You want to associate a binary indicator variable z to x so that z = 1 whenever x > 0. To do so, use the Big-M formulation by including the constraint:
x ≤ M z.
This constraint ensures that whenever x > 0, then z = 1 necessarily. The Big-M formulation has more applications, as discussed in Express Logical Constraints Using Real Functions and Binary Indicator Variables.
Suppose that you want to model a reservoir over integer times. At each positive integer time t in a fixed range, the reservoir can accept water of quantity xin(t), or can discharge an amount of water xout(t), in continuous amounts up to a maximum M either in or out. Your model should enforce that if xin(t) > 0 then xout(t) = 0, and if xout(t) > 0 then xin(t) = 0. How do you model this constraint? One way is to constrain
xin(t) * xout(t) = 0.
However, this constraint causes the problem to become nonlinear, and solvers generally have difficulty with this type of constraint.
A better way to implement the constraint is to use an indicator binary variable zin(t) that is 1 whenever xin(t) > 0, and a similar binary variable zout(t) that is 1 whenever xout(t) > 0. Assuming that you have such variables, add the constraint
zin(t) + zout(t) ≤ 1,
which ensures that xin(t) and xout(t) are not both positive.
To ensure that zin(t) = 1 whenever xin(t) > 0, use the Big-M formulation. Assume that xin(t) is bounded above by M, a positive number, for all t. Include the constraint
xin(t) ≤ M*zin(t).
To ensure that zin(t) = 0 whenever xin(t) = 0, include zin(t) in the objective function. In this way, minimizing the objective function causes zin(t) to be zero whenever possible.
Similarly, to connect zout(t) and xout(t), incorporate the constraint
xout(t) <= M*zout(t).
In summary, to enforce the constraint that xin(t) and xout(t) cannot both be positive, you create two binary variables zin(t) and zout(t) for each time t, and include these three constraints:
xin(t) <= M*zin(t) % Ensures that zin(t) = 1 whenever xin(t) > 0.
xout(t) <= M*zout(t) % Ensures that zout(t) = 1 whenever xout(t) > 0.
zin(t) + zout(t) <= 1 % Ensures that zin(t) and zout(t) are not both positive.
The MATLAB® commands in the problem-based approach are as follows:
T = 50; % Number of times M = 40; % Maximum size of x variables xin = optimvar('xin',T,'LowerBound',0,'UpperBound',M); xout = optimvar('xout',T,'LowerBound',0,'UpperBound',M); zin = optimvar('zin',T,'Type','integer','LowerBound',0,'UpperBound',1); zout = optimvar('zout',T,'Type','integer','LowerBound',0,'UpperBound',1); prob = optimproblem; xinzin = xin <= M*zin; xoutzout = xout <= M*zout; zinzout = zin + zout <= 1; prob.Constraints.xinzin = xinzin; prob.Constraints.xoutzout = xoutzout; prob.Constraints.zinzout = zinzout; prob.Objective = sum(zin + zout);
Note the following:
All the constraints are vectors of constraints of length
T, as are the optimization variables and indicator
All the constraints are defined with single statements, not in a loop, which gives the best performance.
This section contains logical statements and the corresponding MATLAB commands with binary variables. The statements assume that the
binary optimization variables, meaning each has type
0, and upper bound
|Description||Logical Statement||MATLAB Commands|
z = 1 - w;
|At least one of ||
z + w >= 1;
|At most one of ||
z + w <= 1;
f >= z; f >= w; f <= z + w;
f <= z; f <= w; f >= z + w - 1;
f >= z - w; f >= w - z; f <= z + w; f <= 2 - (z + w);
This section connects a real function g(x) and a binary variable z. Typically, you introduce z into the problem as an indicator variable to model some aspect of the problem, such as an indicator of when g(x) > 0. All of these constraints are based on the Big-M formulation.
Assume that the constant M and the function g(x) satisfy the bounds
–M ≤ g(x) ≤ M.
|If z = 1 then g(x) ≤ 0||
g(x) <= M*(1 - z);
|If z = 1 then g(x) ≥ 0||
g(x) >= -M*(1 - z);
|If z = 1 then g(x) = 0||
g(x) <= M*(1 - z); g(x) >= -M*(1 - z);
|If g(x) ≤ 0 then z = 1||
g(x) >= -M*z;
|If g(x) ≥ 0 then z = 1||
g(x) <= M*z;
If g(x) > 0 then z = 1
If g(x) < 0 then z = 0
Create a new binary variable
g(x) <= M*z;
g(x) >= -M*z1;
z + z1 == 1;
This formulation is indeterminate when g(x) = 0.
Use the previous logical constraints together with binary indicator variables to create code that implements new formulas.
For scalar functions g(x) and h(x) satisfying the bound constraint M, implement the condition:
If g(x) ≥ 0 then h(x) ≥ 0
Introduce a binary indicator variable
g(x) <= M*z; h(x) >= -M*(1 - z);
|g(x) = z*x where z is a binary variable|
Represent this condition as two constraints:
If z = 1 then g(x) - x = 0
If z = 0 then g(x) = 0
g(x) <= x + M*(1-z); g(x) >= x - M*(1-z); g(x) <= M*z; g(x) >= -M*z;
Suppose that the cost of producing a quantity x of an item is
You can model this nonlinear cost using a linear variable x and a binary indicator variable z. Create constraints so that z = 1 whenever x > 0, and include z in the objective function so that z = 0 whenever x = 0. Assume that the problem includes a bound M so that x ≤ M.
x <= M*z; % Constraint, makes z = 1 when x > 0 cost = a*z + b*x;
If you minimize the cost, when
x = 0 then
= 0 also.
Sometimes you want to model a constraint that is enforced when condition A holds
or condition B holds or condition C holds. To do so, create binary indicator
that indicate when the corresponding conditions A, B, and C hold, and include the
zA + zB + zC >= 1;
As another example, model the absolute value constraint |x| = 5, which means x = 5 or x = –5. Create two indicator variables
z2 that indicate when x = 5 and x = –5, respectively. Then include the constraint
z1 + z2 >= 1;
One way to set z1 = 1 when x = 5 is to introduce three new indicator variables
z11 = 1 when
x < 5 and
z1 = 0,
z12 = 1 when
x = 5 and
z13 = 1 when
x > 5 and
Then introduce the constraint
z11 + z12 + z13 = 1;
z11, use these three constraints.
-(1 - z11) <= z1; z1 <= (1 - z11); x - 5 <= M(1 - z11);
z12, use these four constraints.
-(1 - z12) <= z1 - 1; z1 - 1 <= (1 - z12); -M(1 - z12) <= x - 5; x - 5 <= M(1 - z12);
z13, use these three constraints.
-(1 - z13) <= z1; z1 <= (1 - z13); x - 5 >= -M(1 - z13);
To finish the model, specify similar constraints for
z23 that correspond to
z2 and the condition x = –5.
The classic book on modeling for optimization is Williams . For a discussion of why the Big-M formulation of binary indicator variables is mathematically complete and not extensible, see Hooker . For further examples of using binary indicator variables in mathematical modeling, see Brown and Dell  and Stevens and Palocsay .
 Williams, H. Paul. Model Building in Mathematical Programming, 5th Edition. Wiley, 2013.
 Hooker, John. A Principled Approach to MILP Modeling. Carnegie Mellon University, 2008. Available at https://coral.ise.lehigh.edu/mip-2008/talks/hooker.pdf.
 Brown, Gerald G. and Robert F. Dell. Formulating Integer Linear Programs: A Rogues' Gallery. INFORMS Transactions on Education 7 (2), 2007, pp. 153–159. Available at https://doi.org/10.1287/ited.7.2.153.
 Stevens, Scott P. and Susan W. Palocsay. Teaching Use of Binary Variables in Integer Linear Programs: Formulating Logical Conditions. INFORMS Transactions on Education 18 (1), 2017, pp. 28–36. Available at https://doi.org/10.1287/ited.2017.0177.