Minizinc: Alternative Modeling Approaches for the Facility Location Problem

This post picks up from where I left off in my previous post: The Facility Location Problem. In the previous post, I gave an introduction to the problem then demonstrated how it can be modeled using PuLP. In the last section, I ported the model one-to-one into Minizinc which offers a more declarative approach to modeling for discrete optimization. The great thing about Minizinc is that it’s much richer with regards to the different ways you can model a problem.

Parameters #

Let’s start with the parameters; these won’t change much but the largest difference from last time is that all the distances and facility costs are ints:

int: num_facilities;
set of int: facility =  1..num_facilities;
array[1..num_facilities] of int: facility_setup_costs;
array[1..num_facilities] of int: facility_capacities;

int: num_customers;
set of int: customer = 1..num_customers;
array[customer] of int: customer_demands;

array[facility,customer] of int: dist_matrix;

Indicator Vars approach #

To recap, in the previous approach, the decision variables were as follows:

array[1..num_facilities,1..num_customers] of var 0..1: allocations;
array[1..num_facilities] of var 0..1: facilities_built;

allocations is a 2-D array whereby each entry is indexed by the facility and customer index. The value of an entry is 1 if a customer is assigned to the given facility and 0 otherwise. This definition is suitable for MILP solvers.

On the other hand, the f-th entry in facilities_built indicates whether the facility should be built (is assigned 1 or more customers) or not built (is not assigned any customer). This array is useful to simplify calculation of the setup costs of all the facilities

We had the following constraints:

• a customer should be assigned to one and only one facility
• a customer should not be assigned to a facility that does not get built
• given the customers assigned to a facility, the sum of all their demands should be less than or equal to the facility’s capacity

Given our choice of decision variables, these constraints are defined as follows:

% customer assigned to no more and no less than 1 facility
constraint forall(c in 1..num_customers)
    (sum(f in 1..num_facilities)(allocations[f,c]) == 1);

% customer should not be assigned to a facility that does not get built
constraint forall(f in 1..num_facilities,c in 1..num_customers)
    (allocations[f,c] <= facilities_built[f]);

% facility capacity meets assigned customer demands
constraint forall(f in 1..num_facilities)
    (sum(c in 1..num_customers)(allocations[f,c]*customer_demands[c])
        <= (facility_capacities[f]*facilities_built[f]));

Injective Function Approach: Mapping Customer to Facility assigned #

Alternatively, rather than keep a 2D array of indicator variables, we could maintain a 1-dimensional array that’s indexed by the set of customers and each entry is the facility that the customer will be assigned to:

array[1..num_customers] of var 1..num_customers: c_to_f_assn;

With this approach, we don’t have to add the two constraints that ensure each customer is assigned exactly one facility - the definition of c_to_f_assn particularly its co-domain of var 1..num_customers guarantee this constraint trivially.

However, for the customer demands - facility constraints and calculation of the cost, we need to introduce an additional variable facilities_built so to track the facilities that end up getting built:

var set of 1..num_facilities: facilities_built = array2set(c_to_f_assn);

% constraints
constraint forall(f in facilities_built)
    (sum(c in 1..num_customers where c_to_f_assn[c] = f)(customer_demands[c])
    <= facility_capacities[f]);

% obj
var int: cost =
    sum(f in facilities_built)(facility_setup_costs[f])
    +
    sum(c in 1..num_customers)(dist_matrix[c_to_f_assn[c],c]);

With this approach, we’ve got fewer constraints though it ends up performing quite poorly.

Set approach: Facility to set of Customers Assignment #

Here’s yet another way of defining the decision variables: have the facility IDs map to the set of assigned customers:

array[facility] of var set of customer: f_to_c_assn;
constraint all_disjoint(f_to_c_assn);
constraint forall(c in customer)(exists(cs in f_to_c_assn)(c in cs));

Both constraints above ensure that a customer is assigned to exactly one facility. all_disjoint is a global constraint that minizinc provides whereby no two sets intersect. To make it concrete, if customer 30 was assigned to facility 3 and facility 5, then the intersection f_to_c_assn[3] intersect f_to_c_assn[5] would contain customer 30 and this would violate the constraint. The second constraint ensures that every customer is assigned to at least one facility since the first constraint evaluates to true in the case where some customers or even all don’t get assigned to any facility.

For the customer demands - facility capacity constraints, we’ve got the following:

constraint forall(f in facility)(
    sum(c in f_to_c_assn[f])(customer_demands[c])
        <= facility_capacities[f]);

Finally, to calculate the cost and minimize it:

var int: cost = sum(f in facility)
    (((card(f_to_c_assn[f]) > 0) * facility_setup_costs[f])
    + sum(c in f_to_c_assn[f])(dist_matrix[f,c])
    );

solve minimize cost;

card returns the cardinality of a set and if a set has cardinality greater than 0, we include its setup costs in the summation. Note that card(f_to_c_assn[f]) > 0 evaluates to bool which is then coerced to 0 if false and to 1 if true.

This formulation is particularly suitable for Constraint Solvers.

Multiple models and Channeling Constraints #

We can combine the two previous approaches above too. We’ll maintain two different views of the assignment:

• f_to_c_assn: map facility to set of customers assigned
• c_to_f_assn: map customer to the facility they’re assigned to

array[facility] of var set of customer: f_to_c_assn;
array[customer] of facility: c_to_f_assn;

So as to make sure both are in sync, we add a channeling constraint:

constraint int_set_channel(c_to_f_assn, f_to_c_assn);

From the docs, int_set_channel requires that facility f is assigned to customer c in the array c_to_f_assn if and only if c is i the set of f_to_c_assn[f].

c_to_f_assn trivially guarantees that every customer is assigned to exactly one facility.

We still need to add the customer demands - facility capacity constraints though this time we don’t need to create any helper variables to track which facilities are built:

% facilities' assigned demands are less than or equal to their respective
% capacities
constraint forall(f in 1..num_facilities)(
    (sum(c in f_to_c_assn[f])(customer_demands[c]))
        <= facility_capacities[f]);

Finally, for calculating the cost:

var int: cost = 
    sum(f in facility)((card(f_to_c_assn[f]) > 0) * facility_setup_costs[f])
    + sum(c in customer)(dist_matrix[c_to_f_assn[c],c]);

solve minimize cost;

While having two views of the same problem might seem superfluous, this approach ends up being faster than the single view of facility to set of customers when using constraint solvers. However, it’s not as fast as the indicator vars approach which is quite suitable for LP solvers. I’d love to have made some graphs for visualization but for now I’ll post the raw values:

With 50 customers and 25 facilities, I get:

• Indicator vars (cbc): 157.6 ms ± 3.6 ms
• Multiple views (cpsatlp): 490.6 ms ± 28.3 ms
• Pure set approach (cpsatlp): 1.196 s ± 0.050 s

With 200 customers and 50 facilities, I get

• Indicator vars (cbc): 1.172 s ± 0.017 s
• Multiple views (cpsatlp): 6.835 s ± 0.151 s
• Pure set approach (cpsatlp): 19.131 s ± 0.113 s

There’s definitely other approaches for tackling this problem. If you’ve got any that I should consider please reach out :)