bpp: Martello and Toth's model to solve the bin packing problem.
    Parameters:
        - s: list with item widths
        - B: bin capacity
    Returns a model, ready to be solved.

def bpp(s,B):
    """bpp: Martello and Toth's model to solve the bin packing problem.
    Parameters:
        - s: list with item widths
        - B: bin capacity
    Returns a model, ready to be solved.
    """
    n = len(s)
    U = len(FFD(s,B)) # upper bound of the number of bins
    model = Model("bpp")
    # setParam("MIPFocus",1)
    x,y = {},{}
    for i in range(n):
        for j in range(U):
            x[i,j] = model.addVar(vtype="B", name="x(%s,%s)"%(i,j))
    for j in range(U):
        y[j] = model.addVar(vtype="B", name="y(%s)"%j)

    # assignment constraints
    for i in range(n):
        model.addCons(quicksum(x[i,j] for j in range(U)) == 1, "Assign(%s)"%i)

    # bin capacity constraints
    for j in range(U):
        model.addCons(quicksum(s[i]*x[i,j] for i in range(n)) <= B*y[j], "Capac(%s)"%j)

    # tighten assignment constraints
    for j in range(U):
        for i in range(n):
            model.addCons(x[i,j] <= y[j], "Strong(%s,%s)"%(i,j))

    # tie breaking constraints
    for j in range(U-1):
        model.addCons(y[j] >= y[j+1],"TieBrk(%s)"%j)

    # SOS constraints
    for i in range(n):
        model.addConsSOS1([x[i,j] for j in range(U)])

    model.setObjective(quicksum(y[j] for j in range(U)), "minimize")
    model.data = x,y

    return model