Python Model.addQConstr方法代码示例

# 需要导入模块: from gurobipy import Model [as 别名]
# 或者: from gurobipy.Model import addQConstr [as 别名]
def build_gurobi_model(case):
    G, B = case.G, case.B
    P = real(case.demands)
    Q = imag(case.demands)
    branches = case.branch_list
    n = len(case.demands)
    vhat = case.vhat
    s2 = 2**.5
    gens = {bus: gen.v for bus, gen in case.gens.items()}
    del gens[0]

    m = GurobiModel("jabr")
    u = [m.addVar(name='u_%d'%i) for i in range(n)]
    R = {(i, j): m.addVar(name='R_%d_%d' % (i, j)) for i, j in branches}
    I = {(i, j): m.addVar(lb=-GRB.INFINITY, name='I_%d_%d' % (i, j)) for i, j in branches}
    for i, j in branches:
        R[j, i] = R[i, j]
        I[j, i] = I[i, j]
    m.update()
    m.addConstr(u[0] == vhat*vhat/s2, 'u0')
    for gen, v in gens.iteritems():
        m.addConstr(u[gen] == v*v/s2, 'u%d' % gen)
    for i, j in branches:
        m.addQConstr(2*u[i]*u[j] >= R[i,j]*R[i,j] + I[i,j]*I[i,j], 'cone_%d_%d' % (i, j))
    k = lambda i: (j for j in B[i, :].nonzero()[1])
    s = lambda i, j: 1 if i < j else -1
    for i in range(1, n):
        m.addConstr(-s2*u[i]*G[i, :].sum() + quicksum(G[i,j]*R[i,j] + B[i,j]*s(i,j)*I[i,j] for j in k(i)) == P[i],
                    'real_flow_%d_%d' % (i, j))
        if i in gens:
            continue
        m.addConstr(s2*u[i]*B[i, :].sum() + quicksum(-B[i,j]*R[i,j] + G[i,j]*s(i,j)*I[i,j] for j in k(i)) == Q[i],
                    'reac_flow_%d_%d' % (i, j))
    m.setObjective(quicksum(R[i,j] for i, j in branches), sense=GRB.MAXIMIZE)
    m.params.outputFlag = 0
    #m.params.barQCPConvTol = 5e-10
    m.optimize()
    if m.status != 2:
        raise ValueError("gurobi failed to converge: %s (check log)" % m.status)
    u_opt = [x.getAttr('x') for x in u]
    R_opt = {(i, j): x.getAttr('x') for (i, j), x in R.items()}
    I_opt = {(i, j): x.getAttr('x') for (i, j), x in I.items()}
    return u_opt, R_opt, I_opt

# 需要导入模块: from gurobipy import Model [as 别名]
# 或者: from gurobipy.Model import addQConstr [as 别名]
def build_model(data):
    
    n = data['n']
    r = data['r']
    p = data['p']
    s = data['s']
    c = data['c']
    h = data['h']
    Q = data['Q']
    T = data['T']
    conflicts = data['conflicts']
    locking_times = data['locking_times']
    
    model = Model("ExaminationSchedulingQ_v2")
    
    print("Building variables...")
    x = {}
    y = {}
    for i in range(n):
        for k in range(r):
            x[i,k] = model.addVar(vtype=GRB.BINARY, name="x_%s_%s" % (i,k))
        for l in range(p):
            y[i, l] = model.addVar(vtype=GRB.BINARY, name="y_%s_%s" % (i,l))

    model.update() # integrate new variables
    
    print("Building constraints...")    

    # Add constraints
    for i in range(n):

        # Add constraint: Each i is planned in exactly one period
        model.addConstr( quicksum([y[i,l] for l in range(p)]) == 1, "c0")

        # Add constraint: Each i has enough seats
        model.addConstr( quicksum([x[i,k] * c[k] for k in range(r)]) >= s[i], "c1")

    print "Bedingungen 1 generiert"

    # Add constraint: Each k has at most one i per period
    for k in range(r):
        for l in range(p):
            model.addQConstr( quicksum([x[i,k] * y[i,l] for i in range(n)]) <= T[k][l], "c2")

    # Add constraint: There are no conflicts quadratic
    for l in range(p):
        model.addQConstr( quicksum([ y[i,l] * y[j,l] * Q[i][j] for i, j in itertools.combinations(range(n),2) if Q[i][j] == 1]) == 0,  "c3")


    ###### Improve speed by generating combinations of i and j outside of loop

    # Set objective
    #model.setObjective( quicksum([ x[i,k] * s[i] for i,k in itertools.product(range(n), range(r)) ]), GRB.MINIMIZE)
    gamma = 1
    model.setObjective( -gamma*quicksum([Q[i][j]*(quicksum([y[i,l]*h[l] - y[j,l]*h[l] for l in range(p)]))*(quicksum([y[i,l]*h[l] - y[j,l]*h[l] for l in range(p)])) for i, j in itertools.combinations(range(n),2) if Q[i][j] == 1 and j > i])  +  quicksum(x[i,k] * s[i] for i,k in itertools.product(range(n),range(r)) )  , GRB.MINIMIZE)
 
    print "Zielfunktion gesetzt"

    # Set Parameter
    #model.params.mipfocus = 3
    #maximum level of linearization
    model.params.preqlinearize = 1
    #max presolve agressivity
    model.params.presolve = 2
    #Choosing root method 3= concurrent = run barrier and dual simplex in parallel
    model.params.method = 1

    return(model)