Every organization schedules, factories, hospitals, airlines, schools, cloud platforms. Most do it by hand or in spreadsheets. This book shows you how to build software that does it well.
Scheduling Optimization with Python is a practical, code-first guide for software engineers, data scientists, and industrial engineers. Every idea is turned into Python you can run, every model is solved with free, open-source tools, Google OR-Tools CP-SAT, PuLP, HiGHS, and Pyomo, and every result in the book was produced by the code that comes with it.
You will learn to:
- Describe any scheduling problem precisely and recognize the classic problem hidden inside it
- Build MILP models with PuLP and HiGHS, and understand why some formulations are a hundred times faster than others
- Model machines, crews, buffers, berths, and changeovers with CP-SAT interval variables and global constraints
- Solve single-machine, parallel-machine, flow-shop, job-shop, and project-scheduling problems, including famous benchmarks such as ft10 and Taillard's ta001, solved to proven optimality
- Build nurse rosters, exam and school timetables, and technician routes with time windows
- Write fast heuristics and metaheuristics, ATC, NEH, iterated greedy, simulated annealing, tabu search, large neighborhood search - for instances too large for exact methods
- Handle uncertainty with simulation, robust schedules, and rescheduling with stability
- Deploy a validated scheduling engine as a web service with FastAPI and test it properly
No background in operations research is needed: if you can write a Python function and remember some high-school algebra, you are ready. Practitioners with an OR background will find a fast, modern tour of today's tools, honest benchmarks, and many case studies.
The complete, tested source code is free on GitHub: github.com/xain1999/scheduling-optimization-python
