Often when we make a choice we face an optimisation problem.

Imagine a manufacturing process where 20 jobs have 10 timed operations. For each operation there is a corresponding machine on which it will be processed. You are responsible for a permutation of job order that completes manufacturing in the shortest possible time. This is known as the Flow Shop Scheduling Problem (FSSP).

In this 20jx10m scenario there are 2,432,902,008,176,640,000 possible permutations of job sequence! Know that how well you can optimize will directly impact the supply chain as a whole, so no pressure! It’s going to take a little while to evaluate every possible solution, right?

Metaheuristics can be applied to scheduling problems to propose solutions that may only be near-optimal, but considered “good enough”, at reasonable computational cost. Today I’d like to share the code and supporting academic paper for a Python-based framework I developed to address the real-world problem of scheduling optimisation. 5 nature-inspired metaheuristic algorithms including Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Differential Evolution Algorithm (DEA) and Evolution Strategy (ES) are implemented.

The framework also implements a hyper-heuristic feature that dynamically switches between low-level metaheuristics to deterministically use the most appropriate algorithm according to current problem state, or select one stochastically.

Enjoy!

Master’s Degree Intelligent Systems paper grade 100% | Source code