我将非常感谢您提供有关运行我的pyomo脚本文件的上述错误消息的帮助 - “pyomo solve Katrina_Model5.py Katrina_paper.dat --solver = gurobi --summary --stream-solver --report-timing “在命令提示符下。我还是该软件的新手。我已经提供了完整的代码和数据文件来帮助您。
以下是我的问题的完整语法：
from pyomo.environ import *
#--define the mode:
model = AbstractModel()
#--declaring parameters:
model.n = Param(within=PositiveIntegers, doc='total no. of depots & afected areas')
model.L = Param(within=PositiveIntegers, doc='Max. no. of nodes a salesman may visit')
model.K = Param(initialize=2, within=PositiveIntegers, doc='Min. no. of nodes a salesman may visit')
#model.m = Param(within=PositiveIntegers, doc='no. of initial salesmen positioned at depot i &j')
#--declare model sets names:
model.I = RangeSet(model.n, name='Set of Origin/intermediary nodes')
model.J = RangeSet(model.n, name='Set of affected areas/destination nodes')
model.A = model.I*model.J
model.D = RangeSet(2, name='Set of depots comprises first d nodes of Set V')
model.U = RangeSet(3,5, name='Set of impacted areas/or customers')
model.V = model.D | model.U
#-- define additional parameters with indexed sets:
model.d = Param(model.I, model.J, doc='Represents cost/travel time matrix.')
#--define model Variables:
model.x = Var(model.I, model.J, within=Binary, name="Var of a salesman traveling.")
model.u = Var(within=RangeSet(2,5), name="no. of nodes visited on traveler's path from origin up to node i")
model.m =Var(model.D, name='no. of initial salesmen positioned at depot i &j')
"""#model's objective function defined.#"""
def objective_rule(model):
return sum(model.d[i,j]*model.x[i,j] for (i,j) in model.A)
model.objective = Objective(rule=objective_rule, sense=minimize, name="Total distance traveled")
"""--Below we define and declare the constraints of the model --"""
#.....constraint # 2
def constrTWO_rule(model, i):
return sum(model.x[i,j] for j in model.U) == m[i]
model.ConsOutTrvler = Constraint(model.D, rule=constrTWO_rule)
#.....constraint # 3
def constrTHREE_rule(model, j):
return sum(model.x[i,j] for i in model.U) == m[j]
model.ConsInTrvler = Constraint(model.D, rule=constrTHREE_rule)
#.....constraint # 4
def constrFOUR_rule(model, j):
return sum(model.x[i,j] for i in model.V) == 1
model.ConsTrvlerInn = Constraint(model.U, rule=constrFOUR_rule)
#.....constraint # 5
def constrFIVE_rule(model, i):
return sum(model.x[i,j] for j in model.V) == 1
model.ConsTrvlerOut = Constraint(model.U, rule=constrFIVE_rule)
#......constraint # 6
def constrSIX_rule(model, i):
return u[i] + (L-2)*sum(model.x[k,i]-model.x[i,k] for k in model.D)-L + 1 <= 0
model.consLowBounds = Constraint(model.U, rule=constrSIX_rule)
#.....constraint # 7
def constrSEVEN_rule(model, i):
return u[i] + sum(model.x[k,i] + (2-K)*model.x[i,k] for k in model.D) >= 2
model.consUpBounds = Constraint(model.U, rule=constrSEVEN_rule)
#.....constraint # 8 ---DOUBLE-CHECK FORMULATION
def constrEIGHT_rule(model, k, i):
return model.x[k,i] + model.x[i,k] <= 1
model.consNotOneAffArea = Constraint(model.D, model.U, rule=constrEIGHT_rule)
#.....constraint # 9 ---DOUBLE-CHECK FORMULATION
def constrNINE_rule(model, i, j):
return ( u[i] - u[j] + L*x[i,j] + (L-2)*x[i,j] ) <= L-1
model.consSubTourElim = Constraint(model.U, rule=constrNINE_rule)
并且，以下是使用的'.dat'数据文件：
param n := 5 ;
param L := 5 ;
param K := 2 ;
param d: 1 2 3 4 5 :=
1 0 8 4 9 9
2 8 0 6 7 10
3 4 6 0 5 6
4 9 7 5 0 4
5 9 10 6 4 0 ;