Scheduling Optimization Solver Trial
- Clone from github:
!git clone https://github.com/scmopt/optseq.git
- Move to optseq trial directry:
cd optseq/optseqtrial
- Change mode of execution file
- for linux (Google Colab.)
!chmod +x optseq-linux
- for Mac
!chmod +x optseq-mac
-
Import package and write a code:>
from optseq import * -
(Option) Install other packages if necessarily:
!pip install plotly pandas numpy matplotlib
See https://scmopt.github.io/manual/07optseq.html and https://www.logopt.com/optseq/
Here is an example (after moving to optseq/optseqtrial directory).
from 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