Add a new constraint to the model

Open

子龙 王

• February 28, 2024 03:50

from gurobipy import Model, GRB, quicksum
import sys

def MIPModel(Data):
    n = Data['n']
    m = Data['m']
    J = Data['J']
    OJ = Data['OJ']
    oj=OJ
    operations_machines = Data['operations_machines']
    operations_times = Data['operations_times']
    largeM = Data['largeM']
    # starttime=[0,1,2,3,4,5,6,7,8]
    D = [[60, 60, 65, 60, 65, 90, 60, 65],
                [65, 65, 85, 75, 60, 75, 85, 80],
                [60, 70, 75, 80, 75, 80, 80, 75],
                [65, 65, 80, 75, 80, 75, 75, 80],
                [60, 65, 65, 60, 75, 80, 80, 75],
                [60, 65, 65, 80, 75, 75, 80, 75],
                [60, 60, 75, 80, 75, 70, 60, 75]]

    # preprocessing
    # obtain the earliest starting time for each operation
    stimeOp = {}
    stimeOpMax = 0
    for i in J:
        for j in OJ[i]:
            if j == 1:
                # stimeOp[i, j] =     starttime[i]
                stimeOp[i, j] = 0
            else:
                opt_time_min = sys.maxsize
                for k in operations_machines[i, j - 1]:
                    if operations_times[i, j - 1, k] < opt_time_min:
                        opt_time_min = operations_times[i, j - 1, k]
                stimeOp[i, j] = stimeOp[i, j - 1] + opt_time_min
            if stimeOpMax < stimeOp[i, j]:
                stimeOpMax = stimeOp[i, j]
    print(stimeOp)
    # the latest completion time for each operation
    ltimeOp = {}
    for i in J:
        for jj in range(len(OJ[i]) - 1, -1, -1):
            j = OJ[i][jj]
            opt_time_min = sys.maxsize
            for k in operations_machines[i, j]:
                if operations_times[i, j, k] < opt_time_min:
                    opt_time_min = operations_times[i, j, k]

            if jj == len(OJ[i]) - 1:
                ltimeOp[i, j] = largeM - opt_time_min
            else:
                ltimeOp[i, j] = ltimeOp[i, j + 1] - opt_time_min
    print(ltimeOp)

    model = Model("model")

    x, y, s = {}, {}, {}
    cmax = model.addVar(lb=stimeOpMax, ub=largeM, vtype="I", name="cmax")

    # define x
    for j in J:  # job
        for i in OJ[j]:  # operation
            # s[j,i]=model.addVar(lb=0,ub=largeM,vtype="I",name="s(%s,%s)"%(j,i))
            s[j, i] = model.addVar(lb=stimeOp[j, i], ub=largeM, vtype="I", name="s(%s,%s)" % (j, i))  # ltimeOp[j,i]
            for k in operations_machines[j, i]:
                x[j, i, k] = model.addVar(lb=0, ub=1, vtype="I", name="x(%s,%s,%s)" % (j, i, k))
    # define y
    for i in J:
        for ip in J:
            for j in OJ[i]:
                for jp in OJ[ip]:
                    y[i, j, ip, jp] = model.addVar(lb=0, ub=1, vtype="I", name="y(%s,%s,%s,%s)" % (i, j, ip, jp))

    # define objective function
    model.setObjective(cmax, GRB.MINIMIZE)
    
    # constraint(3)
    for i in J:
        for j in OJ[i]:
            model.addConstr(sum(x[i, j, k] for k in operations_machines[i, j]) == 1, "assignment(%s,%s)" % (i, j))

    # constraint(4)
    for i in J:
        for j in OJ[i]:
            if j != OJ[i][0]:


                model.addConstr(s[i, j] >= s[i, j - 1] + quicksum(
                    operations_times[i, j - 1, k] * x[i, j - 1, k] for k in operations_machines[i, j - 1])
                        ,"stime(%s,%s)" % (i, j))
       # constraint(5) 自己
    for k in operations_machines[1, 1]    :
        for i in J:
            model.addConstr(sum(x[i, j, k]    for j in OJ[i] ) == 1, "cons_x(%s,%s)" % (i, j))   
    # constraint(6)
    for i in J:
        for ip in J:
            if i < ip:
                for j in OJ[i]:
                    for jp in OJ[ip]:
                        kkp = [k for k in operations_machines[i, j] if k in operations_machines[ip, jp]]
                        if len(kkp):
                            for iipk in kkp:
                                model.addConstr(s[ip, jp] >= s[i, j] + operations_times[i, j, iipk] - largeM * (
                                            3 - y[i, j, ip, jp] - x[i, j, iipk] - x[ip, jp, iipk]),
                                                "cons_6_(%s,%s,%s,%s)" % (i, j, ip, jp))
                                model.addConstr(
                                    s[i, j] >= s[ip, jp] +
                                    operations_times[ip, jp, iipk] - largeM *
                                    (2 + y[i, j, ip, jp] - x[i, j, iipk] - x[ip, jp, iipk]),
                                    "cons_5_(%s,%s,%s,%s)" % (i, j, ip, jp))
    # cmax constraint
    for i in J:
        model.addConstr(cmax >= s[i, OJ[i][len(OJ[i]) - 1]] + quicksum(
            operations_times[i, OJ[i][len(OJ[i]) - 1], k] * x[i, OJ[i][len(OJ[i]) - 1], k] for k in
            operations_machines[i, OJ[i][len(OJ[i]) - 1]]), "cmax_cons_(%s)" % (i))




    model.params.TimeLimit=90
    model.update()
    model.optimize()
    # print(y)

    for j in J:  # job
        for i in OJ[j]:  # operation
            # s[j,i]=model.addVar(lb=0,ub=largeM,vtype="I",name="s(%s,%s)"%(j,i))
           
            for k in operations_machines[j, i]:
                string= ''
                if abs(  x[j, i, k].x-1) <= 0.01:
                    string =j+i+k+s[j,i].x
                    print(j,i,k,s[j,i].x)
                    # print(s[j,i].x)
    # print(s)
    return model

I have now written a model of shop scheduling that considers many constraints, and now I want to add a new one: the cost of transport between machines
The existing constraint is that the start time of the current operation s[i, j] must be later than the end time of the previous operation s[i, j-1]+ the time taken by the previous operation
There is a distance transport matrix D, representing the time cost between all machines, and the start time of the current operation s[i, j] must be later than the end time of the previous operation s[i, j-1]+ the time taken by the previous operation + the transport time of the machine on which the two operations are located

我现在写好了一个有关车间调度的模型，考虑了众多约束，现在我想添加一个新约束：机器之间的运输代价
现有的约束是：当前操作的开始时间s[i, j]必须晚于前一个操作的结束时间s[i, j-1]+前一个操作所需的时间
我希望额外考虑两个操作之间的运输时间代价 ，存在一个距离运输矩阵D，代表所有机器之间的时间代价，当前操作的开始时间s[i, j]必须晚于前一个操作的结束时间s[i, j-1]+前一个操作所需的时间+两个操作所在机器的运输时间
我尝试在约束4中添加如下代码：

# constraint(4)
for i in J:
    for j in OJ[i]:
        if j != OJ[i][0]:
            previous_machine = None
            current_machine = None
model.update()
for k in operations_machines[i, j-1]:
    if  x[i, j-1, k] >0.5 : # 根据机器分配情况确定前一个操作所在的机器
        previous_machine = k
        break
for k in operations_machines[i, j]:
    if x[i, j, k] > 0.5:  # 根据机器分配情况确定当前操作所在的机器
        current_machine = k
        break
model.addConstr(s[i, j] >= s[i, j - 1] + quicksum(
operations_times[i, j - 1, k] * x[i, j - 1, k] for k in operations_machines[i, j - 1])+
D(previous_machine,operations_machines),"stime(%s,%s)" % (i, j))

result：TypeError: '>' not supported between instances of 'Var' and 'float'

 

 

Related to

• Modify

0

• 

  子龙 王

  • February 28, 2024 04:15

  

  

  The last line of code is wrong. My new code should read:

  D[previous_machine][operations_machines],
  Instead of

  D(previous_machine,operations_machines),
  But it also fails in the following parts:

  for k in operations_machines[i, j-1]:
  if x[i, j-1, k] >0.5:
  previous_machine = k
  break
  for k in operations_machines[i, j]:
  if x[i, j, k] > 0.5:
  current_machine = k
  break

  0
• 

  子龙 王

  • February 28, 2024 04:19

  

  

  I don't know if adding the corresponding code in constraint 4 is the right thing to do, and I appreciate someone giving me some guidance

  0
• 

  子龙 王

  • February 29, 2024 03:27

  

  

  I also tried the following:
  # constraint(4)
  # for i in J:
  #     for j in OJ[i]:
  #         if j ! = OJ[i][0]:

  #             p = None
  #             current_machine = None
  #             model.update()
  #             model. addConstr(s[i, j] >= s[i, j - 1] + quicksum(
  #                 operations_times[i, j - 1, k] * x[i, j - 1, k] for k in operations_machines[i, j - 1])+
  #                quicksum(D[p*x[i, j - 1, p]][q*x[i, j, q]] for p in operations_machines[i,  j - 1] for q in operations_machines[i, j])
  #                ,"stime(%s,%s)"  % (i, j))

  
  There will be:
  TypeError: list indices must be integers or slices, not gurobipy. LinExpr

  Someone told me I should use the indicator, but I still don't know how to do it

  0
• 

  Ahmed Azab

  Gurobi Staff

  • February 29, 2024 07:43

  Hi 子龙 王,

  Please provide values for the model input Data. A small instance with some comments is better.

  Thanks

  - Ahmed

  1
• 

  子龙 王

  • February 29, 2024 13:19

  

  

  The complete executable code is as follows:

  from gurobipy import Model, GRB, quicksum
  import sys
  import numpy as np
  import time
  import os

  from DataRead import getdata

  from itertools import chain

  def MPModel(Data):
      n = Data['n']
      m = Data['m']
      J = Data['J']
      OJ = Data['OJ']
      oj=OJ
      operations_machines = Data['operations_machines']
      operations_times = Data['operations_times']
      largeM = Data['largeM']
      # starttime=[0,1,2,3,4,5,6,7,8]
      D = [[60, 60, 65, 60, 65, 90, 60, 65],
                  [65, 65, 85, 75, 60, 75, 85, 80],
                  [60, 70, 75, 80, 75, 80, 80, 75],
                  [65, 65, 80, 75, 80, 75, 75, 80],
                  [60, 65, 65, 60, 75, 80, 80, 75],
                  [60, 65, 65, 80, 75, 75, 80, 75],
                  [60, 60, 75, 80, 75, 70, 60, 75]]

      # preprocessing
      # obtain the earliest starting time for each operation
      stimeOp = {}
      stimeOpMax = 0
      for i in J:
          for j in OJ[i]:
              if j == 1:
                  # stimeOp[i, j] =     starttime[i]
                  stimeOp[i, j] = 0
              else:
                  opt_time_min = sys.maxsize
                  for k in operations_machines[i, j - 1]:
                      if operations_times[i, j - 1, k] < opt_time_min:
                          opt_time_min = operations_times[i, j - 1, k]
                  stimeOp[i, j] = stimeOp[i, j - 1] + opt_time_min
              if stimeOpMax < stimeOp[i, j]:
                  stimeOpMax = stimeOp[i, j]
      print(stimeOp)
      # the latest completion time for each operation
      ltimeOp = {}
      for i in J:
          for jj in range(len(OJ[i]) - 1, -1, -1):
              j = OJ[i][jj]
              opt_time_min = sys.maxsize
              for k in operations_machines[i, j]:
                  if operations_times[i, j, k] < opt_time_min:
                      opt_time_min = operations_times[i, j, k]

              if jj == len(OJ[i]) - 1:
                  ltimeOp[i, j] = largeM - opt_time_min
              else:
                  ltimeOp[i, j] = ltimeOp[i, j + 1] - opt_time_min
      print(ltimeOp)

      model = Model("model")

      x, y, s = {}, {}, {}
      cmax = model.addVar(lb=stimeOpMax, ub=largeM, vtype="I", name="cmax")

      # define x
      for j in J:  # job
          for i in OJ[j]:  # operation
              # s[j,i]=model.addVar(lb=0,ub=largeM,vtype="I",name="s(%s,%s)"%(j,i))
              s[j, i] = model.addVar(lb=stimeOp[j, i], ub=largeM, vtype="I", name="s(%s,%s)" % (j, i))  # ltimeOp[j,i]
              for k in operations_machines[j, i]:
                  x[j, i, k] = model.addVar(lb=0, ub=1, vtype="I", name="x(%s,%s,%s)" % (j, i, k))
      # define y
      for i in J:
          for ip in J:
              for j in OJ[i]:
                  for jp in OJ[ip]:
                      y[i, j, ip, jp] = model.addVar(lb=0, ub=1, vtype="I", name="y(%s,%s,%s,%s)" % (i, j, ip, jp))

      # define objective function
      model.setObjective(cmax, GRB.MINIMIZE)
      
      # constraint(3)
      for i in J:
          for j in OJ[i]:
              model.addConstr(sum(x[i, j, k] for k in operations_machines[i, j]) == 1, "assignment(%s,%s)" % (i, j))
      # constraint(4)
      for i in J:
          for j in OJ[i]:
              if j != OJ[i][0]:

                  p = None
                  current_machine = None
                  model.update()
                  model.addConstr(s[i, j] >= s[i, j - 1] + quicksum(
                      operations_times[i, j - 1, k] * x[i, j - 1, k] for k in operations_machines[i, j - 1])
                       ,"stime(%s,%s)" % (i, j))
      # constraint(4)  idea1
      # for i in J:
      #     for j in OJ[i]:
      #         if j != OJ[i][0]:

      #             p = None
      #             current_machine = None
      #             model.update()
      #             model.addConstr(s[i, j] >= s[i, j - 1] + quicksum(
      #                 operations_times[i, j - 1, k] * x[i, j - 1, k] for k in operations_machines[i, j - 1])+
      #                 quicksum(D[p*x[i, j - 1, p]][q*x[i, j, q]] for p in operations_machines[i, j - 1] for q in operations_machines[i, j])
      #                 ,"stime(%s,%s)" % (i, j))
      # # constraint(4)  idea2
      # for i in J:
      #     for j in OJ[i]:
      #         if j != OJ[i][0]:

      #             previous_machine = None
      #             current_machine = None
      #             model.update()
      #             for k in operations_machines[i, j-1]:
      #                 if  x[i, j-1, k] > 0.5: # 根据机器分配情况确定前一个操作所在的机器
      #                     previous_machine = k
      #                 break
      #             for k in operations_machines[i, j]:
      #                 if x[i, j, k] > 0.5:  # 根据机器分配情况确定当前操作所在的机器
      #                     current_machine = k
      #                 break
      #             model.addConstr(s[i, j] >= s[i, j - 1] + quicksum(
      #                 operations_times[i, j - 1, k] * x[i, j - 1, k] for k in operations_machines[i, j - 1])+
      #                D[[previous_machine][current_machine]]
      #                ,"stime(%s,%s)" % (i, j))
         # constraint(5) 
      for k in operations_machines[1, 1]    :
          for i in J:
              model.addConstr(sum(x[i, j, k]    for j in OJ[i] ) == 1, "cons_x(%s,%s)" % (i, j))   
      # constraint(6)
      for i in J:
          for ip in J:
              if i < ip:
                  for j in OJ[i]:
                      for jp in OJ[ip]:
                          kkp = [k for k in operations_machines[i, j] if k in operations_machines[ip, jp]]
                          if len(kkp):
                              for iipk in kkp:
                                  model.addConstr(s[ip, jp] >= s[i, j] + operations_times[i, j, iipk] - largeM * (
                                              3 - y[i, j, ip, jp] - x[i, j, iipk] - x[ip, jp, iipk]),
                                                  "cons_6_(%s,%s,%s,%s)" % (i, j, ip, jp))
                                  model.addConstr(
                                      s[i, j] >= s[ip, jp] +
                                      operations_times[ip, jp, iipk] - largeM *
                                      (2 + y[i, j, ip, jp] - x[i, j, iipk] - x[ip, jp, iipk]),
                                      "cons_5_(%s,%s,%s,%s)" % (i, j, ip, jp))
      # cmax constraint
      for i in J:
          model.addConstr(cmax >= s[i, OJ[i][len(OJ[i]) - 1]] + quicksum(
              operations_times[i, OJ[i][len(OJ[i]) - 1], k] * x[i, OJ[i][len(OJ[i]) - 1], k] for k in
              operations_machines[i, OJ[i][len(OJ[i]) - 1]]), "cmax_cons_(%s)" % (i))

   

      model.params.TimeLimit=90
      model.update()
      model.optimize()
      # print(y)

      for j in J:  # job
          for i in OJ[j]:  # operation
              # s[j,i]=model.addVar(lb=0,ub=largeM,vtype="I",name="s(%s,%s)"%(j,i))
             
              for k in operations_machines[j, i]:
                  string= ''
                  if abs(  x[j, i, k].x-1) <= 0.01:
                      string =j+i+k+s[j,i].x
                      print(j,i,k,s[j,i].x)
                      # print(s[j,i].x)
      # print(s)
      return model
  filename='datasss.txt'
  Data=getdata(filename)

  n=Data['n']
  m=Data['m']
  J=Data['J']
  OJ=Data['OJ']
  print(OJ)
  operations_machines=Data['operations_machines']
  operations_times=Data['operations_times']
  largeM=Data['largeM']

  mipmodel=MPModel(Data) 

   

  txt document content is:
  4 4 1
  4 4 3 54 1 34 4 61 2 2 4 3 54 1 34 4 61 2 2 4 3 54 1 34 4 61 2 2 4 3 54 1 34 4 61 2 2
  4 4 4 9 1 15 2 89 3 70 4 4 9 1 15 2 89 3 70 4 4 9 1 15 2 89 3 70 4 4 9 1 15 2 89 3 70
  4 4 1 38 2 19 3 28 4 87 4 1 38 2 19 3 28 4 87 4 1 38 2 19 3 28 4 87 4 1 38 2 19 3 28 4 87
  4 4 1 95 3 34 2 7 4 29 4 1 95 3 34 2 7 4 29 4 1 95 3 34 2 7 4 29 4 1 95 3 34 2 7 4 29

   

  txt explanation：

  -in the first line there are (at least) 2 numbers: the first is the number of jobs and the second the number of machines (the 3rd is not necessary, it is the average number of machines per operation)

  -Every row represents one job: the first number is the number of operations of that job, the second number (let's say k>=1) is the number of machines that can process the first operation; then according to k, there are k pairs of numbers (machine,processing time) that specify which are the machines and the processing times; then the data for the second operation and so on...

  
  Example: Fisher and Thompson 6x6 instance, alternate name (mt06)

  6   6   1   
  6   1   3   1   1   1   3   1   2   6   1   4   7   1   6   3   1   5   6   
  6   1   2   8   1   3   5   1   5   10  1   6   10  1   1   10  1   4   4   
  6   1   3   5   1   4   4   1   6   8   1   1   9   1   2   1   1   5   7   
  6   1   2   5   1   1   5   1   3   5   1   4   3   1   5   8   1   6   9   
  6   1   3   9   1   2   3   1   5   5   1   6   4   1   1   3   1   4   1   
  6   1   2   3   1   4   3   1   6   9   1   1   10  1   5   4   1   3   1   

  first row = 6 jobs and 6 machines 1 machine per operation
  second row: job 1 has 6 operations, the first operation can be processed by 1 machine that is machine 3 with processing time 1.

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• 

  Ahmed Azab

  Gurobi Staff

  • March 01, 2024 03:24

  The reasons for the errors you have in constraint 4 are as follows:

  # constraint(4)  idea1

   quicksum(D[p*x[i, j - 1, p]][q*x[i, j, q]]

  TypeError: list indices must be integers or slices, not gurobipy.LinExpr

  This error occurred because you used linear expressions  p*x[i, j - 1, p] and q*x[i, j, q] as the index for D where D is a Python list.
  
   # constraint(4)  idea2

  if  x[i, j-1, k] > 0.5:

  TypeError: '>' not supported between instances of 'Var' and 'float'
  
  Here, you attempted to add an if condition for x[i, j-1, k], which does not yet have a value, since it is a decision variable.
  
  Please refer to this article for more details about How do I model conditional statements in Gurobi?
  
  - Ahmed

  0
• 

  子龙 王

  • March 01, 2024 04:15

  

  

  I have looked at the relevant website and understood the application of the if constraint in gurobi, but I still don't know how to write my own questions I wonder if it is convenient to provide specific code details

  0

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