How to Install to Jupyter Notebook (Labo) and/or Google Colaboratory

Install

1. Clone from github:

   !git clone https://github.com/mikiokubo/optseqtrial.git

2. Move to optseq trial directry:

   cd optseqtrial

3. Change mode of execution file

   • for linux (Google Colab.)

     !chmod +x optseq-linux

   • for Mac

     !chmod +x optseq-mac

4. Import package and write a code:> from optseqtrial.scop import *

5. (Option) Install other packages if necessarily:

!pip install plotly pandas numpy matplotlib

How to use

See https://mikiokubo.github.io/optseqtrial/ and https://www.logopt.com/optseq/

Here is an example.

from optseqtrial.optseq import *
"""
Example 1
PERT
file name: Example1.py
Copyright Log Opt Co., Ltd.

Consider a 5-activity problem with precedence constraints between the activities.
Such a problem is called PERT (Program Evaluation and Review Technique).
The processing times (durations) of the activities are kept in the dictionary
 duration ={1:13, 2:25, 3:15, 4:27, 5:22 }.
Precedence constraints are given by:
 Activity 1 -> Activity 3; Activity 2 -> Activity 4;
 Activity 3 -> Activity 4; and Activity 3 -> Activity 5.
The objective is to find the maximum completion time (makespan) for all 5 activities.
"""

model = Model()
duration = {1: 13, 2: 25, 3: 15, 4: 27, 5: 22}
act = {}
mode = {}
for i in duration:
    act[i] = model.addActivity("Act[{0}]".format(i))
    mode[i] = Mode("Mode[{0}]".format(i), duration[i])
    act[i].addModes(mode[i])

# temporal (precedense) constraints
model.addTemporal(act[1], act[3])
model.addTemporal(act[2], act[4])
model.addTemporal(act[2], act[5])
model.addTemporal(act[3], act[4])

model.Params.TimeLimit = 1
model.Params.Makespan = True
model.Params.TimeLimit = 1
model.Params.Makespan = True
model.optimize()

 ================ Now solving the problem ================ 


Solutions:
    source   ---     0     0
      sink   ---    55    55
    Act[1]   ---     0    13
    Act[2]   ---     0    25
    Act[3]   ---    13    28
    Act[4]   ---    28    55
    Act[5]   ---    25    47