Delete MIP.py

MIP.py

@@ -1,133 +0,0 @@
-# importing required modules
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-import chart_studio.plotly as py
-import plotly.figure_factory as ff
-import datetime
-from datetime import timedelta
-import time
-import copy
-
-#importing PuLP solver
-import pulp
-from itertools import product
-
-def gantt_for_MIP(df,method):
-    title_chart = 'Job shop Schedule - using '+ method
-    fig = ff.create_gantt(df, index_col='Resource', show_colorbar=True, group_tasks=True, showgrid_x=True, title=title_chart)
-    return fig
-
-
-def MIP_fn(input_file):
-    xls = pd.ExcelFile(input_file)
-    df1 = pd.read_excel(xls, 'Machines Sequence',index_col = 0)
-    df2 = pd.read_excel(xls, 'Processing Time',index_col = 0)
-
-    times =[]
-    machines = []
-
-
-    for ind in df1.index:
-        list_m = []
-        list_t = []
-        for col in df1:
-            list_m.append(df1[col][ind]-1) # for MIP formulation
-            list_t.append(df2[col][ind])
-        times.append(list_t)
-        machines.append(list_m)
-
-
-    pt = df2.values
-    mo = df1.values-1 # for the numbering the machines from 0 to 9
-
-    m=n=len(pt[0])
-    print("The number of machines and jobs in the given input data is - ",m)
-
-    #Max time possible for all the jobs - summation of all the process time
-    M = sum(times[i][j] for i in range(n) for j in range(m))
-
-    model = pulp.LpProblem("MIN_makespan", pulp.LpMinimize)
-
-    dv = pulp.LpVariable.dicts("start_time",
-                                        ((i, j) for i in range(m) for j in range(n)),
-                                        lowBound=0,
-                                        cat='Continuous')
-    #print(dv)
-    b =  pulp.LpVariable.dicts("binary_var",
-                                        ((j, k, i) for i in range(m) for k in range(n) for j in range(n)),
-                                        lowBound=0,
-                                        cat='Binary')
-    # print(b)
-    Cmax = pulp.LpVariable('Cmax',lowBound = 0, cat='Continuous')
-
-
-    # (1) operation sequence inside a job
-    model += Cmax
-    for j in range(m):
-        for i in range(n-1):
-            I = mo[j,i]
-            K = mo[j,i+1]
-            J = j
-
-            model += (dv[J,K] - dv[J,I]) >= pt[J][list(mo[J]).index(I)]
-    #         print((dv[J,K] - dv[J,I]) >= pt[J][list(mo[J]).index(I)])
-
-    # (2) the max start time
-    for j in range(m):
-        for i in range(n):
-
-            I = mo[j,i]
-            J = j
-            model += (Cmax - dv[J,I]) >= pt[J][list(mo[J]).index(I)]
-            #print((Cmax - dv[J,I]) >= pt[J][list(mo[J]).index(I)])
-
-    # (3) no overlap for operations assigned to same machine
-    for (j, k) in product(range(n), range(n)):
-        if k != j:
-            for i in range(m):
-                model += dv[j,i] - dv[k,i] + M*b[j,k,i] >= pt[k][list(mo[k]).index(i)]
-                model += -dv[j,i] + dv[k,i] - M*b[j,k,i] >= pt[j][list(mo[j]).index(i)] - M
-
-    start_time = time.time()
-    model.solve(pulp.PULP_CBC_CMD(timeLimit=200))
-    print("Solution status : ",pulp.LpStatus[model.status])
-
-    # for var in dv:
-    #     var_value = dv[var].varValue
-    #     print( var[0],'-',var[1] ,var_value)
-
-    tota_time = pulp.value(model.objective)
-    print ('Optimal time:',tota_time)
-    print('Solver processing time:%s'% (time.time() - start_time))
-    op = "Optimal Schedule Length :"+ str(tota_time)
-    
-    m_keys=[j for j in range(m)]
-    j_keys=[j for j in range(n)]
-    key_count={key:0 for key in j_keys}
-    j_count={key:0 for key in j_keys}
-    m_count={key:0 for key in m_keys}
-    j_record={}
-
-    for var in dv:
-        var_value = dv[var].varValue
-        
-        start_time=str(datetime.timedelta(seconds=var_value)) # convert seconds to hours, minutes and seconds
-        end_time=str(datetime.timedelta(seconds=var_value+pt[var[1]][list(mo[var[1]]).index(var[0])]))
-            
-        j_record[(var[1],var[0])]=[start_time,end_time]
-        
-        key_count[i]=key_count[i]+1
-            
-
-    df=[]
-    for m in m_keys:
-        for j in j_keys:
-            df.append(dict(Task='Machine %s'%(m), Start='2020-02-01 %s'%(str(j_record[(j,m)][0])), Finish='2020-02-01 %s'%(str(j_record[(j,m)][1])),Resource='Job %s'%(j)))
-        
-    method = 'PuLP - CBC Solver'
-    df_data = pd.DataFrame(df)
-
-    return op,gantt_for_MIP(df,method),df_data
-