Python Model.addCons方法代码示例

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def scheduling_time_index(J,p,r,w):
    """
    scheduling_time_index: model for the one machine total weighted tardiness problem

    Model for the one machine total weighted tardiness problem
    using the time index formulation

    Parameters:
        - J: set of jobs
        - p[j]: processing time of job j
        - r[j]: earliest start time of job j
        - w[j]: weighted of job j; the objective is the sum of the weighted completion time

    Returns a model, ready to be solved.
    """
    model = Model("scheduling: time index")
    T = max(r.values()) + sum(p.values())
    X = {}   # X[j,t]=1 if job j starts processing at time t, 0 otherwise
    for j in J:
        for t in range(r[j], T-p[j]+2):
            X[j,t] = model.addVar(vtype="B", name="x(%s,%s)"%(j,t))

    for j in J:
        model.addCons(quicksum(X[j,t] for t in range(1,T+1) if (j,t) in X) == 1, "JobExecution(%s)"%(j))

    for t in range(1,T+1):
        ind = [(j,t2) for j in J for t2 in range(t-p[j]+1,t+1) if (j,t2) in X]
        if ind != []:
            model.addCons(quicksum(X[j,t2] for (j,t2) in ind) <= 1, "MachineUB(%s)"%t)

    model.setObjective(quicksum((w[j] * (t - 1 + p[j])) * X[j,t] for (j,t) in X), "minimize")

    model.data = X
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def mils_echelon(T,K,P,f,g,c,d,h,a,M,UB,phi):
    """
    mils_echelon: echelon formulation for the multi-item, multi-stage lot-sizing problem

    Parameters:
        - T: number of periods
        - K: set of resources
        - P: set of items
        - f[t,p]: set-up costs (on period t, for product p)
        - g[t,p]: set-up times
        - c[t,p]: variable costs
        - d[t,p]: demand values
        - h[t,p]: holding costs
        - a[t,k,p]: amount of resource k for producing p in period t
        - M[t,k]: resource k upper bound on period t
        - UB[t,p]: upper bound of production time of product p in period t
        - phi[(i,j)]: units of i required to produce a unit of j (j parent of i)
    """
    rho = calc_rho(phi) # rho[(i,j)]: units of i required to produce a unit of j (j ancestor of i)

    model = Model("multi-stage lotsizing -- echelon formulation")

    y,x,E,H = {},{},{},{}
    Ts = range(1,T+1)
    for p in P:
        for t in Ts:
            y[t,p] = model.addVar(vtype="B", name="y(%s,%s)"%(t,p))
            x[t,p] = model.addVar(vtype="C", name="x(%s,%s)"%(t,p))
            H[t,p] = h[t,p] - sum([h[t,q]*phi[q,p] for (q,p2) in phi if p2 == p])
            E[t,p] = model.addVar(vtype="C", name="E(%s,%s)"%(t,p))        # echelon inventory
        E[0,p] = model.addVar(vtype="C", name="E(%s,%s)"%(0,p))    # echelon inventory

    for t in Ts:
        for p in P:
            # flow conservation constraints
            dsum = d[t,p] + sum([rho[p,q]*d[t,q] for (p2,q) in rho if p2 == p])
            model.addCons(E[t-1,p] + x[t,p] == E[t,p] + dsum, "FlowCons(%s,%s)"%(t,p))

            # capacity connection constraints
            model.addCons(x[t,p] <= UB[t,p]*y[t,p], "ConstrUB(%s,%s)"%(t,p))

        # time capacity constraints
        for k in K:
            model.addCons(quicksum(a[t,k,p]*x[t,p] + g[t,p]*y[t,p] for p in P) <= M[t,k],
                            "TimeUB(%s,%s)"%(t,k))


    # calculate echelon quantities
    for p in P:
        model.addCons(E[0,p] == 0, "EchelonInit(%s)"%(p))
        for t in Ts:
            model.addCons(E[t,p] >= quicksum(phi[p,q]*E[t,q] for (p2,q) in phi if p2 == p),
                            "EchelonLB(%s,%s)"%(t,p))

    model.setObjective(\
        quicksum(f[t,p]*y[t,p] + c[t,p]*x[t,p] + H[t,p]*E[t,p] for t in Ts for p in P), \
        "minimize")

    model.data = y,x,E
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def weber(I,x,y,w):
    """weber: model for solving the single source weber problem using soco.
    Parameters:
        - I: set of customers
        - x[i]: x position of customer i
        - y[i]: y position of customer i
        - w[i]: weight of customer i
    Returns a model, ready to be solved.
    """

    model = Model("weber")

    X,Y,z,xaux,yaux = {},{},{},{},{}
    X = model.addVar(lb=-model.infinity(), vtype="C", name="X")
    Y = model.addVar(lb=-model.infinity(), vtype="C", name="Y")
    for i in I:
        z[i] = model.addVar(vtype="C", name="z(%s)"%(i))
        xaux[i] = model.addVar(lb=-model.infinity(), vtype="C", name="xaux(%s)"%(i))
        yaux[i] = model.addVar(lb=-model.infinity(), vtype="C", name="yaux(%s)"%(i))

    for i in I:
        model.addCons(xaux[i]*xaux[i] + yaux[i]*yaux[i] <= z[i]*z[i], "MinDist(%s)"%(i))
        model.addCons(xaux[i] == (x[i]-X), "xAux(%s)"%(i))
        model.addCons(yaux[i] == (y[i]-Y), "yAux(%s)"%(i))

    model.setObjective(quicksum(w[i]*z[i] for i in I), "minimize")

    model.data = X,Y,z
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def gcp(V,E,K):
    """gcp -- model for minimizing the number of colors in a graph
    Parameters:
        - V: set/list of nodes in the graph
        - E: set/list of edges in the graph
        - K: upper bound on the number of colors
    Returns a model, ready to be solved.
    """
    model = Model("gcp")
    x,y = {},{}
    for k in range(K):
        y[k] = model.addVar(vtype="B", name="y(%s)"%k)
        for i in V:
            x[i,k] = model.addVar(vtype="B", name="x(%s,%s)"%(i,k))

    for i in V:
        model.addCons(quicksum(x[i,k] for k in range(K)) == 1, "AssignColor(%s)"%i)

    for (i,j) in E:
        for k in range(K):
            model.addCons(x[i,k] + x[j,k] <= y[k], "NotSameColor(%s,%s,%s)"%(i,j,k))

    model.setObjective(quicksum(y[k] for k in range(K)), "minimize")

    model.data = x
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def test_circle():
    points =[
            (2.802686, 1.398947),
            (4.719673, 4.792101),
            (1.407758, 7.769566),
            (2.253320, 2.373641),
            (8.583144, 9.769102),
            (3.022725, 5.470335),
            (5.791380, 1.214782),
            (8.304504, 8.196392),
            (9.812677, 5.284600),
            (9.445761, 9.541600)]

    m = Model()
    a = m.addVar('a', lb=None)
    b = m.addVar('b', ub=None)
    r = m.addVar('r')

    # minimize radius
    m.setObjective(r, 'minimize')

    for i,p in enumerate(points):
        # NOTE: SCIP will not identify this as SOC constraints!
        m.addCons( sqrt((a - p[0])**2 + (b - p[1])**2) <= r, name = 'point_%d'%i)

    m.optimize()

    bestsol = m.getBestSol()
    assert abs(m.getSolVal(bestsol, r) - 5.2543) < 1.0e-3
    assert abs(m.getSolVal(bestsol, a) - 6.1242) < 1.0e-3
    assert abs(m.getSolVal(bestsol, b) - 5.4702) < 1.0e-3

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def sils(T,f,c,d,h):
    """sils -- LP lotsizing for the single item lot sizing problem
    Parameters:
        - T: number of periods
        - P: set of products
        - f[t]: set-up costs (on period t)
        - c[t]: variable costs
        - d[t]: demand values
        - h[t]: holding costs
    Returns a model, ready to be solved.
    """
    model = Model("single item lotsizing")
    Ts = range(1,T+1)
    M = sum(d[t] for t in Ts)
    y,x,I = {},{},{}
    for t in Ts:
        y[t] = model.addVar(vtype="I", ub=1, name="y(%s)"%t)
        x[t] = model.addVar(vtype="C", ub=M, name="x(%s)"%t)
        I[t] = model.addVar(vtype="C", name="I(%s)"%t)
    I[0] = 0

    for t in Ts:
        model.addCons(x[t] <= M*y[t], "ConstrUB(%s)"%t)
        model.addCons(I[t-1] + x[t] == I[t] + d[t], "FlowCons(%s)"%t)

    model.setObjective(\
        quicksum(f[t]*y[t] + c[t]*x[t] + h[t]*I[t] for t in Ts),\
        "minimize")

    model.data = y,x,I
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
    def create_model():
        # create solver instance
        s = Model()

        # add some variables
        x = s.addVar("x", obj = -1.0, vtype = "I", lb=-10)
        y = s.addVar("y", obj = 1.0, vtype = "I", lb=-1000)
        z = s.addVar("z", obj = 1.0, vtype = "I", lb=-1000)

        # add some constraint
        s.addCons(314*x + 867*y + 860*z == 363)
        s.addCons(87*x + 875*y - 695*z == 423)

        # create conshdlr and include it to SCIP
        conshdlr = MyConshdlr(shouldtrans=True, shouldcopy=False)
        s.includeConshdlr(conshdlr, "PyCons", "custom constraint handler implemented in python",
                          sepapriority = 1, enfopriority = -1, chckpriority = 1, sepafreq = 10, propfreq = 50,
                          eagerfreq = 1, maxprerounds = -1, delaysepa = False, delayprop = False, needscons = True,
                          presoltiming = SCIP_PRESOLTIMING.FAST, proptiming = SCIP_PROPTIMING.BEFORELP)

        cons1 = s.createCons(conshdlr, "cons1name")
        ids.append(id(cons1))
        cons2 = s.createCons(conshdlr, "cons2name")
        ids.append(id(cons2))
        conshdlr.createData(cons1, 10, "cons1_anothername")
        conshdlr.createData(cons2, 12, "cons2_anothername")

        # add these constraints
        s.addPyCons(cons1)
        s.addPyCons(cons2)
        return s

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def gpp(V,E):
    """gpp -- model for the graph partitioning problem
    Parameters:
        - V: set/list of nodes in the graph
        - E: set/list of edges in the graph
    Returns a model, ready to be solved.
    """
    model = Model("gpp")

    x = {}
    y = {}
    for i in V:
        x[i] = model.addVar(vtype="B", name="x(%s)"%i)
    for (i,j) in E:
        y[i,j] = model.addVar(vtype="B", name="y(%s,%s)"%(i,j))

    model.addCons(quicksum(x[i] for i in V) == len(V)/2, "Partition")

    for (i,j) in E:
        model.addCons(x[i] - x[j] <= y[i,j], "Edge(%s,%s)"%(i,j))
        model.addCons(x[j] - x[i] <= y[i,j], "Edge(%s,%s)"%(j,i))

    model.setObjective(quicksum(y[i,j] for (i,j) in E), "minimize")

    model.data = x
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def eoq_soco(I,F,h,d,w,W):
    """eoq_soco --  multi-item capacitated economic ordering quantity model using soco
    Parameters:
        - I: set of items
        - F[i]: ordering cost for item i
        - h[i]: holding cost for item i
        - d[i]: demand for item i
        - w[i]: unit weight for item i
        - W: capacity (limit on order quantity)
    Returns a model, ready to be solved.
    """
    model = Model("EOQ model using SOCO")

    T,c = {},{}
    for i in I:
        T[i] = model.addVar(vtype="C", name="T(%s)"%i)  # cycle time for item i
        c[i] = model.addVar(vtype="C", name="c(%s)"%i)  # total cost for item i

    for i in I:
        model.addCons(F[i] <= c[i]*T[i])

    model.addCons(quicksum(w[i]*d[i]*T[i] for i in I) <= W)

    model.setObjective(quicksum(c[i] + h[i]*d[i]*T[i]*0.5 for i in I), "minimize")

    model.data = T,c
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def gcp_fixed_k(V,E,K):
    """gcp_fixed_k -- model for minimizing number of bad edges in coloring a graph
    Parameters:
        - V: set/list of nodes in the graph
        - E: set/list of edges in the graph
        - K: number of colors to be used
    Returns a model, ready to be solved.
    """
    model = Model("gcp - fixed k")

    x,z = {},{}
    for i in V:
        for k in range(K):
            x[i,k] = model.addVar(vtype="B", name="x(%s,%s)"%(i,k))
    for (i,j) in E:
        z[i,j] = model.addVar(vtype="B", name="z(%s,%s)"%(i,j))

    for i in V:
        model.addCons(quicksum(x[i,k] for k in range(K)) == 1, "AssignColor(%s)" % i)

    for (i,j) in E:
        for k in range(K):
            model.addCons(x[i,k] + x[j,k] <= 1 + z[i,j], "BadEdge(%s,%s,%s)"%(i,j,k))

    model.setObjective(quicksum(z[i,j] for (i,j) in E), "minimize")

    model.data = x,z
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def test_event():
    # create solver instance
    s = Model()
    s.hideOutput()
    s.setPresolve(SCIP_PARAMSETTING.OFF)
    eventhdlr = MyEvent()
    s.includeEventhdlr(eventhdlr, "TestFirstLPevent", "python event handler to catch FIRSTLPEVENT")

    # add some variables
    x = s.addVar("x", obj=1.0)
    y = s.addVar("y", obj=2.0)

    # add some constraint
    s.addCons(x + 2*y >= 5)
    # solve problem
    s.optimize()

    # print solution
    assert round(s.getVal(x)) == 5.0
    assert round(s.getVal(y)) == 0.0

    del s

    assert 'eventinit' in calls
    assert 'eventexit' in calls
    assert 'eventexec' in calls
    assert len(calls) == 3

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def mctransp(I,J,K,c,d,M):
    """mctransp -- model for solving the Multi-commodity Transportation Problem
    Parameters:
        - I: set of customers
        - J: set of facilities
        - K: set of commodities
        - c[i,j,k]: unit transportation cost on arc (i,j) for commodity k
        - d[i][k]: demand for commodity k at node i
        - M[j]: capacity
    Returns a model, ready to be solved.
    """

    model = Model("multi-commodity transportation")

    # Create variables
    x = {}
    for (i,j,k) in c:
        x[i,j,k] = model.addVar(vtype="C", name="x(%s,%s,%s)" % (i,j,k), obj=c[i,j,k])
# todo
    arcs = tuplelist([(i,j,k) for (i,j,k) in x])

    # Demand constraints
    for i in I:
        for k in K:
            model.addCons(sum(x[i,j,k] for (i,j,k) in arcs.select(i,"*",k)) == d[i,k], "Demand(%s,%s)" % (i,k))

    # Capacity constraints
    for j in J:
        model.addConstr(sum(x[i,j,k] for (i,j,k) in arcs.select("*",j,"*")) <= M[j], "Capacity(%s)" % j)

    model.data = x
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def tsp(V,c):
    """tsp -- model for solving the traveling salesman problem with callbacks
       - start with assignment model
       - add cuts until there are no sub-cycles
    Parameters:
        - V: set/list of nodes in the graph
        - c[i,j]: cost for traversing edge (i,j)
    Returns the optimum objective value and the list of edges used.
    """
    model = Model("TSP_lazy")
    conshdlr = TSPconshdlr()

    x = {}
    for i in V:
        for j in V:
            if j > i:
                x[i,j] = model.addVar(vtype = "B",name = "x(%s,%s)" % (i,j))

    for i in V:
        model.addCons(quicksum(x[j, i] for j in V if j < i) +
                      quicksum(x[i, j] for j in V if j > i) == 2, "Degree(%s)" % i)

    model.setObjective(quicksum(c[i, j] * x[i, j] for i in V for j in V if j > i), "minimize")

    model.data = x
    return model, conshdlr

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def diet(F,N,a,b,c,d):
    """diet -- model for the modern diet problem
    Parameters:
        F - set of foods
        N - set of nutrients
        a[i] - minimum intake of nutrient i
        b[i] - maximum intake of nutrient i
        c[j] - cost of food j
        d[j][i] - amount of nutrient i in food j
    Returns a model, ready to be solved.
    """
    model = Model("modern diet")

    # Create variables
    x,y,z = {},{},{}
    for j in F:
        x[j] = model.addVar(vtype="I", name="x(%s)" % j)

    for i in N:
        z[i] = model.addVar(lb=a[i], ub=b[i], vtype="C", name="z(%s)" % i)

    # Constraints:
    for i in N:
        model.addCons(quicksum(d[j][i]*x[j] for j in F) == z[i], name="Nutr(%s)" % i)

    model.setObjective(quicksum(c[j]*x[j]  for j in F), "minimize")

    model.data = x,y,z
    return model

# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import addCons [as 别名]
def mtz_strong(n,c):
    """mtz_strong: Miller-Tucker-Zemlin's model for the (asymmetric) traveling salesman problem
    (potential formulation, adding stronger constraints)
    Parameters:
        n - number of nodes
        c[i,j] - cost for traversing arc (i,j)
    Returns a model, ready to be solved.
    """

    model = Model("atsp - mtz-strong")
    
    x,u = {},{}
    for i in range(1,n+1):
        u[i] = model.addVar(lb=0, ub=n-1, vtype="C", name="u(%s)"%i)
        for j in range(1,n+1):
            if i != j:
                x[i,j] = model.addVar(vtype="B", name="x(%s,%s)"%(i,j))

    for i in range(1,n+1):
        model.addCons(quicksum(x[i,j] for j in range(1,n+1) if j != i) == 1, "Out(%s)"%i)
        model.addCons(quicksum(x[j,i] for j in range(1,n+1) if j != i) == 1, "In(%s)"%i)

    for i in range(1,n+1):
        for j in range(2,n+1):
            if i != j:
                model.addCons(u[i] - u[j] + (n-1)*x[i,j] + (n-3)*x[j,i] <= n-2, "LiftedMTZ(%s,%s)"%(i,j))

    for i in range(2,n+1):
        model.addCons(-x[1,i] - u[i] + (n-3)*x[i,1] <= -2, name="LiftedLB(%s)"%i)
        model.addCons(-x[i,1] + u[i] + (n-3)*x[1,i] <= n-2, name="LiftedUB(%s)"%i)

    model.setObjective(quicksum(c[i,j]*x[i,j] for (i,j) in x), "minimize")
    
    model.data = x,u
    return model