cheche-pm

cheche_pm - Project Scheduling Toolkit

A Python package for project scheduling, designed to simplify project management tasks.

Table of Contents

• Installation
• Usage
  • Create project
  • from_csv
  • CPM
  • Priority List
  • Scheduling methods
  • Genetic Algorithm
  • Risk Analysis
• Contributor
• License

Installation

To install cheche_pm, you can use pip:

pip install cheche_pm

Usage

To use cheche_pm, import the Project class and start utilizing its methods. Here are some of the key methods provided by the package:

Create project

creates an empty project. Activities can be added manually.

from cheche_pm import Project

p = p.Project()

p.add_activity(activity_name='A',activity_duration=2,activity_precedence = [None], activity_resources= [2,4,5])
p.add_activity(activity_name='B',activity_duration=3,activity_precedence =['A'],activity_resources= [3,7,8])
p.add_activity(activity_name='C',activity_duration=4,activity_precedence =['B'],activity_resources=[3,2,2])
p.add_activity(activity_name='D',activity_duration=3,activity_precedence =['A'],activity_resources=[4,5,6])

Activities can be also deleted.

p.delete_activity('D')

Once the activities are created the user, can add the terminal dummy nodes. "Start" and "End" by using the following methods.

p.add_dummies_create_project_network()

from_csv or from_excel

A project can be created from a .csv file or .xlsx file. Imagine that we have an MS EXCEL file with the data below

Activity	Description	Duration	Precedence	Cost	Bulbasaur	Charizard	Squirtle
A	F.House	5		1000	1	0	0
B	F.Pool	2		2000	1	0	0
C	Walls	5	A	3500	0	1	0
D	Pool	6	B	4500	0	0	1
E	Roof	5	C	2600	0	1	0
F	Windows	2	C	7000	0	1	0
G	Electricity	3	C	8000	0	0	1
H	S.Panels	2	E	1000	0	0	1
I	Plumbing	4	F	5600	0	0	1
J	Finishings	3	H, I	12000	0	0	1

A project can be created using the following methods:

.csv file

p = Project.from_csv(filename='data_project.csv',rcpsp_format=True,n_resources= 3,max_resources=[1,1,1])

.xlsx file

p = Project.from_excel(filename='data_project.xlsx',rcpsp_format=True,n_resources= 3,max_resources=[1,1,1])

CPM

This method allows the generation of project schedule using the critical path method

p.CPM()

the user can ask for the critical path

p.get_critical_path()

priority_list

This method allows the user to generate a priority list from a given priority rule:

• LPT: Longest processing time
• SPT: Shortest processing time
• LIS: Least immediate successors
• MIS: Most immediate successor
• LTS: Least total successors
• MTS: Most total successors
• sEST: Smallest Earliest Start Time
• gEST: Greatest Earliest Start Time
• sEFT: Smallest Earliest Finish Time
• gEFT: Greatest Earliest Finish Time
• sLST: Smallest Latest Start Time
• gLST: Greatest Latest Start Time
• sLFT: Smallest Latest Finish Time
• gLFT: Greatest Latest Finish Time
• MINF: Minimum float
• MAXF: Maximum float
• GRPW: Greatest GRPW
• LRPW: Lowest GRPW
• FCFS: First comes first served
• LCFS: Last comes first served
• GRD: Greatest resource demand
• LRD: Lowest resource demand
• GCRD: Greatest cumulative resource demand
• LCRD: Lowest cumulative resource demand

The user can ask for an individual priority list.

PL = p.get_priority_list(priority_rule= 'FCFS',verbose=True,save=True)

Scheduling_methods

Once the user has a priority list it can decide to produce an schedule from it. The user can generate a serial schedule (SSG) or a parallel one (PSG)

Serial (SSG)

ssg = p.SSG(PL,max_resources=[1,1,1],verbose=False)

Parallel (PSG)

psg = p.PSG(PL,max_resources=[1,1,1],verbose=False)

The user can also decide to run all priority list heuristics, and schedule each one via the two methods. This method will return the best schedule obtained.

p.run_all_pl_heuristics()

Visualizations of schedules

Once an schedule is produced, the user can ask for a datetime schedule by providing the date for the project start.

w_sche = p.generate_datetime_schedule(solution = ssg,start_date="2023-09-03",weekends_work=False,max_resources=[1,1,1],verbose=True)

The user can then ask for a gantt chart of this datetime schedule

p.plot_date_gantt(w_sche)

The user can ask for the critical chain of this schedule

p.get_critical_chain(ssg,max_resources=[1,1,1])

The user can perform resource vistualizations

p.plot_resource_levels(ssg)

p.RCPSP_plot(ssg,resource_id=0)

Genetic_Algorithm

The user can use the genetic algorithm optimization method to find the optimal schedule.

p.genetic_algorithm_optimization(popSize = 40, elite_percentage = 0.2, crossover_rate = 0.5, mutationRate = 0.5, generations = 100,show = True)

Risk analysis

These methods offers a collection of monte carlo simulation methods, that can be used to evaluate project makespan uncertainty as well as the effectivenes of activity buffers.

Simple Monte Carlo simulation

p.monte_carlo_cpm_simple(optimistic=0.25,pessimistic=1)

Detailed Monte Carlo simulation

pessimistic = {'A':7.5,'B':3.5,'C':7.5,'D':9,'E':7.5,'F':3,'G':4.5,'H':3,'I':6,'J':4.5}
optimistic = {'A':2.5,'B':1,'C':2.5,'D':3,'E':2.5,'F':1,'G':1.5,'H':1,'I':2,'J':1.5}

p.monte_carlo_cpm_detailed(optimistic=optimistic,pessimistic=pessimistic, NumberIterations=1000)

Buffer analysis

pessimistic = {'A':7.5,'B':3.5,'C':7.5,'D':9,'E':7.5,'F':3,'G':4.5,'H':3,'I':6,'J':4.5}
optimistic = {'A':2.5,'B':1,'C':2.5,'D':3,'E':2.5,'F':1,'G':1.5,'H':1,'I':2,'J':1.5}
buffer = {'A':2,'C':2,'E':3,'H':2,'F':2,'I':1,'J':2}

p.monte_carlo_detail_buffer_analysis(optimistic=optimistic,pessimistic=pessimistic,buffer=buffer, NumberIterations=2000)

Contributors

Author: Luis Fernando Perez Armas

Email: [e-mail]

LinkedIn: LinkedIn Profile

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2023, Luis Fernando Pérez Armas

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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History

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0.1.0 (2023-09-03)

• First release on PyPI.

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0.1.4 (2023-09-04)

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0.1.5 (2023-09-04)

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0.1.6 (2023-09-29)

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0.1.7 (2023-09-29)

Bugfixes

• Error with MTS,LTS, MIS and LIS priority rules fixed.
• Bug related to incorrect choosing of activities under the condition of a priority rule tie, was fixed.

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0.1.8 (2023-10-03)

• Weak forbidden set function added
• Minimum forbidden set function added
• Simple monte carlo buffer analysis added
• Possibility to create a project from a .rcp file and from instances of the CV dataset

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0.1.9 (2023-10-03)

• Extra information added to the monte carlo buffer analysis methods
• Critical Path Method returns a dataframe with the basic outputs ES,EF,LS,LF,F

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0.1.10 (2023-10-03)

• Bug fix with the monte carlo buffer analysis methods

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0.1.11 (2023-10-03)

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0.1.12 (2023-10-08)

• Feasible subset function added
• Function that calculate naive bounds for each activity, based on a time horizon

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0.1.13 (2023-10-08)

• Bug fix with the outputs of forbidden sets and feasible set.

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