workforce2.py
#！/usr/bin/env python3.7 # Copyright 2019, 狗万app足彩Gurobi Optimization, LLC #分配工人的转移;每个工人可能在某一天上班，也可能不在。如果问题不能解决，使用IIS迭代#找到所有冲突的约束。import gurobipy as gp from gurobipy import GRB import sys #每班需要的工人数量，shifrequirements = gp。multidict({"Mon1": 3， "Tue2": 2， "Wed3": 4， "Thu4": 4， "Fri5": 5， "Sat6": 6， "Sun7": 5， "Mon8": 2， "Tue9": 2， "Wed10": 3， "Thu11": 4， "Fri12": 6， "Sat13": 7， "Sun14": 5，}) #每个工人被支付一个轮班工人的工资，工资= gp。multidict({“Amy”:10，“Bob”:12，“Cathy”:10，“Dan”:8，“Ed”:8，“Fred”:9，“Gu”:11，})#工人可用性可用性= gp。tuplelist([(“艾米”、“Tue2”)(“艾米”、“Wed3”)(“艾米”、“Fri5”)(“艾米”、“Sun7”)(“艾米”、“Tue9”)(“艾米”、“Wed10”)(“艾米”、“Thu11”)(“艾米”、“Fri12”)(“艾米”、“Sat13”)(“艾米”、“Sun14”)(“鲍勃”、“Mon1”)(“鲍勃”、“Tue2”)(“鲍勃”、“Fri5”)(“鲍勃”、“Sat6”)(“鲍勃”、“Mon8”)(“鲍勃”、“Thu11”)(“鲍勃”、“Sat13”)(“凯西”,“Wed3”)(“凯西”,“Thu4”),(“凯西”、“Fri5”)(“凯西”,“Sun7”)(“凯西”,“Mon8”)(“凯西”,“Tue9”)(“凯西”,“Wed10”)(“凯西”,“Thu11”)(“凯西”,“Fri12”)(“凯西”,“Sat13”)(“凯西”,“Sun14”)(“丹”、“Tue2”)(“丹”、“Wed3”)(“丹”、“Fri5”)(“丹”、“Sat6”)(“丹”、“Mon8”)(“丹”、“Tue9”)(“丹”、“Wed10”)(“丹”、“Thu11”)(“丹”、“Fri12”)(“丹”、“Sat13”),(“丹”、“Sun14”)(“Ed”,“Mon1”)(“Ed”,“Tue2”)(“Ed”,“Wed3”)(“Ed”,“Thu4”)(“Ed”,“Fri5”)(“Ed”,“Sun7”)(“Ed”,“Mon8”)(“Ed”,“Tue9”)(“Ed”,“Thu11”)(“Ed”,“Sat13”)(“Ed”,“Sun14”)(“弗雷德”、“Mon1”)(“弗雷德”、“Tue2”)(“弗雷德”、“Wed3”)(“弗雷德”、“Sat6”)(“弗雷德”、“Mon8”)(“弗雷德”、“Tue9”)(“弗雷德”、“Fri12”)(“弗雷德”、“Sat13”),(“弗雷德”、“Sun14”)(“古”、“Mon1”)(“古”、“Tue2”)(“古”、“Wed3”)(“古”、“Fri5”)(“古”、“Sat6”)(“古”、“Sun7”)(“古”、“Mon8”)(“古”、“Tue9”)(“古”、“Wed10”)(“古”、“Thu11”)(“古”、“Fri12”)(“古”、“Sat13”)(“古”、“Sun14”)])#模型m = gp.Model(“转让”)#赋值变量: x[w,s] == 1 if worker w is assigned to shift s. # Since an assignment model always produces integer solutions, we use # continuous variables and solve as an LP. x = m.addVars(availability, ub=1, name="x") # The objective is to minimize the total pay costs m.setObjective(gp.quicksum(pay[w]*x[w, s] for w, s in availability), GRB.MINIMIZE) # Constraint: assign exactly shiftRequirements[s] workers to each shift s reqCts = m.addConstrs((x.sum('*', s) == shiftRequirements[s] for s in shifts), "_") # Optimize m.optimize() status = m.status if status == GRB.UNBOUNDED: print('The model cannot be solved because it is unbounded') sys.exit(0) if status == GRB.OPTIMAL: print('The optimal objective is %g' % m.objVal) sys.exit(0) if status != GRB.INF_OR_UNBD and status != GRB.INFEASIBLE: print('Optimization was stopped with status %d' % status) sys.exit(0) # do IIS print('The model is infeasible; computing IIS') removed = [] # Loop until we reduce to a model that can be solved while True: m.computeIIS() print('\nThe following constraint cannot be satisfied:') for c in m.getConstrs(): if c.IISConstr: print('%s' % c.constrName) # Remove a single constraint from the model removed.append(str(c.constrName)) m.remove(c) break print('') m.optimize() status = m.status if status == GRB.UNBOUNDED: print('The model cannot be solved because it is unbounded') sys.exit(0) if status == GRB.OPTIMAL: break if status != GRB.INF_OR_UNBD and status != GRB.INFEASIBLE: print('Optimization was stopped with status %d' % status) sys.exit(0) print('\nThe following constraints were removed to get a feasible LP:') print(removed)
