What algorithm is used for my optimization?

Dear Community,

I have inserted my model into Gurobi and had it solved. Now I am wondering what algorithm is actually used for my problem. Since I only have continuous variables and have converted my exponential functions to linear functions, I expected gurobi to use the concurrent method for continuous problems. Looking at my output, gurobi finds integer variables after presolve and therefore applies the branch-and-bound algorithm, is this correct? Why is this the case?

Thank you very much!

c = 1
k = 2
a = 0.05
b = 1/500
T = 8*60
e = 5
num_work = k+1
num_bre = k
m = Model("Optimization")
m.params.NonConvex = 2

t = m.addVars(num_work, lb=0, ub=T, name="work")
u = m.addVars(num_bre, lb=0, ub=T, name="break")
r = m.addVars(num_work, lb=0, ub=c, name="work rate")
z = m.addVars(num_bre, lb=0, ub=c,  name="work rate break")
#exponential functions
l = m.addVars(num_work, lb=-GRB.INFINITY, name="exponent")
y = m.addVars(num_work, name="e-Funktion")
w = m.addVars(num_bre, lb=-GRB.INFINITY, name="exponent2")
p = m.addVars(num_bre, name="e-Funktion2")
#objective function
q = m.addVars(num_work, name="Objective Function")

FuncPieces = -1
FuncPieceError = 0.09

#constraints for fatigue
m.addConstrs((-b * t[i] == l[i] for i in range(num_work)), name="fatigue")
for i in range(num_work):
     m.addGenConstrExp(l[i], y[i], name=f"fatigue_exponential_{i}")

#constraints for recovery
m.addConstrs(((-a) * u[i] == w[i] for i in range(num_bre)), name="recovery")
for i in range(num_bre):
     m.addGenConstrExp(w[i], p[i], name=f"recovery_exponential_{i}")

m.addConstr(1 == r[0])
for i in range(num_bre):
    m.addConstr(r[i]*y[i] == z[i])
for i in range(num_bre):
    m.addConstr((z[i]+(c-z[i]) * (1-p[i])) == r[i+1])

m.addConstr((quicksum(t[i] for i in range(num_work)) + quicksum(u[i] for i in range(num_bre))) <= T)
m.addConstr(((quicksum(u[i] for i in range(num_bre))) >= 0))

m.setObjective(quicksum((r[i] - r[i]*y[i])/b for i in range(num_work)), GRB.MAXIMIZE)
m.optimize()

 Output 1.part: 

Model has 3 quadratic objective terms
Model has 4 quadratic constraints
Model has 5 general constraints
Variable types: 23 continuous, 0 integer (0 binary)
Coefficient statistics:
  Matrix range     [2e-03, 1e+00]
  QMatrix range    [1e+00, 1e+00]
  QLMatrix range   [1e+00, 1e+00]
  Objective range  [5e+02, 5e+02]
  QObjective range [1e+03, 1e+03]
  Bounds range     [1e+00, 5e+02]
  RHS range        [1e+00, 5e+02]
  QRHS range       [1e+00, 1e+00]
Presolve added 78 rows and 1533 columns
Presolve time: 0.03s
Presolved: 100 rows, 1557 columns, 29313 nonzeros
Presolved model has 4 bilinear constraint(s)
Variable types: 1522 continuous, 35 integer (7 binary)
Root relaxation: objective 4.094919e+02, 129 iterations, 0.02 seconds (0.01 work units)
    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0  409.49186    0    5          -  409.49186      -     -    0s
H    0     0                     312.9253998  409.49186  30.9%     -    0s
H    0     0                     315.9124841  409.49186  29.6%     -    0s
H    0     0                     320.9946870  409.49186  27.6%     -    0s
     0     0  403.75504    0    7  320.99469  403.75504  25.8%     -    0s
     0     0  403.75504    0    7  320.99469  403.75504  25.8%     -    0s
     0     0  403.75504    0    7  320.99469  403.75504  25.8%     -    0s

Best,
Elina