工作力1.py
＃！/ usr / bin / env python3.7＃版权所有2019，guro狗万app足彩bi优化，llc＃分配工人转移;每个工人都可能或可能无法在＃特定日期上使用。如果问题无法解决，请使用IIS查找一组＃冲突约束。请注意，除了＃通过IIS报告的内容可能存在额外的冲突。从Gurobipy导入Gurobipy导入Gurobipy导入Grb导入Sys＃每个班次班次所需的工人数量，ShiftRecivements = GP.MutIdict（{Mon1“：3，”Tue2“：2，”Wed3“：4，”Thu4“：4，“星期五5”：5，“SAT6”：6，“Sun7”：5，“Mon8”：2，“Tue9”：2，“Wed10”：3，“星期六”：4，“星期五12”：6，“SAT13“：7，”Sun14“：5，}）＃金额每位工作人员都支付给工作一班工人，支付= GP.Multigict（{amy”：10，“鲍勃”：12，“凯茜”：10，“凯茜”：10，“丹”：8，“ed”：8，“fred”：9，“gu”：11，}）＃工作者可用性可用性= gp.tuplelist（[（'amy'，'tue2'），（'amy'，'wed3'），（'amy'，'fri5'），（'amy'，'sun7'），（'amy'，'tue9'），（'amy'，'wed10'），（'amy'，'thu11'），（'amy'，'fri12'），（'amy'，'sat13'），（'amy'，'sun14'），（'bob'，'mon1'），（'bob'，'tue2'），（'bob'，'fri5'），（'bob'，'sat6'），（'bob'，'mon8'），（'bob'，'thu11'），（'bob'，'星期六'), ('Cathy', 'Wed3'), ('Cathy', 'Thu4'), ('Cathy', 'Fri5'), ('Cathy', 'Sun7'), ('Cathy', 'Mon8'), ('Cathy', 'Tue9'), ('Cathy', 'Wed10'), ('Cathy', 'Thu11'), ('Cathy', 'Fri12'), ('Cathy', 'Sat13'), ('Cathy', 'Sun14'), ('Dan', 'Tue2'), ('Dan', 'Wed3'), ('Dan', 'Fri5'), ('Dan', 'Sat6'), ('Dan', 'Mon8'), ('Dan', 'Tue9'), ('Dan', 'Wed10'), ('Dan', 'Thu11'), ('Dan', 'Fri12'), ('Dan', 'Sat13'), ('Dan', 'Sun14'), ('Ed', 'Mon1'), ('Ed', 'Tue2'), ('Ed', 'Wed3'), ('Ed', 'Thu4'), ('Ed', 'Fri5'), ('Ed', 'Sun7'), ('Ed', 'Mon8'), ('Ed', 'Tue9'), ('Ed', 'Thu11'), ('Ed', 'Sat13'), ('Ed', 'Sun14'), ('Fred', 'Mon1'), ('Fred', 'Tue2'), ('Fred', 'Wed3'), ('Fred', 'Sat6'), ('Fred', 'Mon8'), ('Fred', 'Tue9'), ('Fred', 'Fri12'), ('Fred', 'Sat13'), ('Fred', 'Sun14'), ('Gu', 'Mon1'), ('Gu', 'Tue2'), ('Gu', 'Wed3'), ('Gu', 'Fri5'), ('Gu', 'Sat6'), ('Gu', 'Sun7'), ('Gu', 'Mon8'), ('Gu', 'Tue9'), ('Gu', 'Wed10'), ('Gu', 'Thu11'), ('Gu', 'Fri12'), ('Gu', 'Sat13'), ('Gu', 'Sun14') ]) # Model m = gp.Model("assignment") # Assignment variables: 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) # Constraints: assign exactly shiftRequirements[s] workers to each shift s reqCts = m.addConstrs((x.sum('*', s) == shiftRequirements[s] for s in shifts), "_") # Using Python looping constructs, the preceding statement would be... # # reqCts = {} # for s in shifts: # reqCts[s] = m.addConstr( # gp.quicksum(x[w,s] for w,s in availability.select('*', s)) == # shiftRequirements[s], s) # Save model m.write('workforce1.lp') # 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') m.computeIIS() if m.IISMinimal: print('IIS is minimal\n') else: print('IIS is not minimal\n') print('\nThe following constraint(s) cannot be satisfied:') for c in m.getConstrs(): if c.IISConstr: print('%s' % c.constrName)
