def sils_cut(T,f,c,d,h, conshdlr):
    """solve_sils -- solve the lot sizing problem with cutting planes
       - start with a relaxed model
       - used lazy constraints to elimitate fractional setup variables with cutting planes
    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 the final model solved, with all necessary cuts added.
    """
    Ts = range(1,T+1)

    model = sils(T,f,c,d,h)
    y,x,I = model.data

    # relax integer variables
    for t in Ts:
        model.chgVarType(y[t], "C")
    model.addVar(vtype="B", name="fake")        # for making the problem MIP

    # compute D[i,j] = sum_{t=i}^j d[t]
    D = {}
    for t in Ts:
        s = 0
        for j in range(t,T+1):
            s += d[j]
            D[t,j] = s

    #include the lot sizing constraint handler
    model.includeConshdlr(conshdlr, "SILS", "Constraint handler for single item lot sizing",
                          sepapriority = 0, enfopriority = -1, chckpriority = -1, sepafreq = -1, propfreq = -1,
                          eagerfreq = -1, maxprerounds = 0, delaysepa = False, delayprop = False, needscons = False,
                          presoltiming = SCIP_PRESOLTIMING.FAST, proptiming = SCIP_PROPTIMING.BEFORELP)
    conshdlr.data = D,Ts

    model.data = y,x,I
    return model
solve_sils -- solve the lot sizing problem with cutting planes
       - start with a relaxed model
       - used lazy constraints to elimitate fractional setup variables with cutting planes
    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 the final model solved, with all necessary cuts added.