lotsizing_lazy.py

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"""
Approaches:
    - sils: solve the problem using the standard formulation
    - sils_cut: solve the problem using cutting planes
 
Copyright (c) by Joao Pedro PEDROSO and Mikio KUBO, 2012
"""
from pyscipopt import Model, Conshdlr, quicksum, multidict, SCIP_RESULT, SCIP_PRESOLTIMING, SCIP_PROPTIMING
 
 
class Conshdlr_sils(Conshdlr):
 
    def addcut(self, checkonly, sol):
        D, Ts = self.data
        y, x, I = self.model.data
        cutsadded = False
 
        for ell in Ts:
            lhs = 0
            S, L = [], []
            for t in range(1, ell + 1):
                yt = self.model.getSolVal(sol, y[t])
                xt = self.model.getSolVal(sol, x[t])
                if D[t, ell] * yt < xt:
                    S.append(t)
                    lhs += D[t, ell] * yt
                else:
                    L.append(t)
                    lhs += xt
            if lhs < D[1, ell]:
                if checkonly:
                    return True
                else:
                    # add cutting plane constraint
                    self.model.addCons(quicksum([x[t] for t in L]) + \
                                       quicksum(D[t, ell] * y[t] for t in S)
                                       >= D[1, ell], removable=True)
                cutsadded = True
        return cutsadded
 
    def conscheck(self, constraints, solution, checkintegrality, checklprows, printreason, completely):
        if not self.addcut(checkonly=True, sol=solution):
            return {"result": SCIP_RESULT.INFEASIBLE}
        else:
            return {"result": SCIP_RESULT.FEASIBLE}
 
    def consenfolp(self, constraints, nusefulconss, solinfeasible):
        if self.addcut(checkonly=False):
            return {"result": SCIP_RESULT.CONSADDED}
        else:
            return {"result": SCIP_RESULT.FEASIBLE}
 
    def conslock(self, constraint, locktype, nlockspos, nlocksneg):
        pass
 
 
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
 
 
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
 
 
def mk_example():
    """mk_example: book example for the single item lot sizing"""
    T = 5
    _, f, c, d, h = multidict({
        1: [3, 1, 5, 1],
        2: [3, 1, 7, 1],
        3: [3, 3, 3, 1],
        4: [3, 3, 6, 1],
        5: [3, 3, 4, 1],
    })
 
    return T, f, c, d, h
 
 
if __name__ == "__main__":
    T, f, c, d, h = mk_example()
 
    model = sils(T, f, c, d, h)
    y, x, I = model.data
    model.optimize()
    print("\nOptimal value [standard]:", model.getObjVal())
    print("%8s%8s%8s%8s%8s%8s%12s%12s" % ("t", "fix", "var", "h", "dem", "y", "x", "I"))
    for t in range(1, T + 1):
        print("%8d%8d%8d%8d%8d%8.1f%12.1f%12.1f" % (
        t, f[t], c[t], h[t], d[t], model.getVal(y[t]), model.getVal(x[t]), model.getVal(I[t])))
 
    conshdlr = Conshdlr_sils()
    model = sils_cut(T, f, c, d, h, conshdlr)
    model.setBoolParam("misc/allowstrongdualreds", 0)
    model.optimize()
    y, x, I = model.data
    print("\nOptimal value [cutting planes]:", model.getObjVal())
    print("%8s%8s%8s%8s%8s%8s%12s%12s" % ("t", "fix", "var", "h", "dem", "y", "x", "I"))
    for t in range(1, T + 1):
        print("%8d%8d%8d%8d%8d%8.1f%12.1f%12.1f" % (
        t, f[t], c[t], h[t], d[t], model.getVal(y[t]), model.getVal(x[t]), model.getVal(I[t])))