optseq 0.1.2

OptSeq is a general-purpose scheduling optimization solver specialized for scheduling optimization.

OptSeq

OptSeq is a general-purpose scheduling optimization solver specialized for scheduling optimization.

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

Install

pip install "optseq[all]"

How to use

See https://github.com/moai-lab-jp/optseq and https://www.logopt.com/optseq/

Here is an example.

"""
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.
"""
from optseq import Mode, Model

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

# temporal (precedent) 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.OutputFlag = True
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