This article is a part of a series of articles. Links to previous parts can be found in the appendix section of the article.
Grey Wolf Pack Optimization
“If you live among wolves, you have to act like a wolf”- Nikita Khrushchev
You are, at this point, very much familiar with the fact that wolf packs have an order and hierarchy, thanks to numerous memes shared on LinkedIn highlighting the collaboration. While most of them are factually wrong, wolf packs indeed have strict social hierarchies and processes. Like most wolves, the grey wolf is also a highly social animal that lives in packs with complex social hierarchies. In their hierarchical system, wolves are ranked according to strength and power (“dominance hierarchy”). The hierarchical system is not just about dominance and aggression. There is a social implication as well since the hierarchy also assists vulnerable members of the pack who cannot hunt for themselves to survive. The hierarchy has essentially four layers: Alphas, Betas, Deltas, and Omegas.
- Alpha males and females lead the pack at the top of the hierarchy and their orders should be followed by the pack.
- Next in the hierarchical layer is the Beta wolf. Beta supports the Alpha wolf’s decisions and helps Alpha to keep discipline within the pack.
- The Delta wolf falls below the Beta in the hierarchy. Deltas are often intense and aggressive but lack the leadership skills or confidence to take on leadership responsibilities.
- The Omega wolf is at the bottom of the hierarchy and has no power. Omega wolf is also responsible for watching over younger wolves.
Besides social hierarchy, grey wolves have a particular way of hunting with a unique strategy. They hunt in packs and work together in groups to separate the prey from the herd; then, a few wolves will chase and attack the prey while the others chase off stragglers. The logic, from an algorithm perspective, is:
- One: Approach, track, and chase the prey
- Two: Consistent pursuit, harassment, and encircling maneuver around the prey
- Three: Attack the prey when it is exhausted
Applications
Like Firefly Algorithms, the best use of grey wolf optimization algorithms in the supply chain world is in solving scheduling problems. Like the examples below, plenty of research papers can use this algorithm for supply chain and operations, specifically in manufacturing. However, the marginal improvement over existing approaches might not be worth overhauling current applications.
Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment
Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments
Gray Wolf Optimization for Scheduling Irrigation Water
Solving software project scheduling problem using grey wolf optimization
What is interesting, as you can see from the example papers above, is numerous papers in the area of cloud computing workflow scheduling. This same approach can be extrapolated to leverage this algorithm in an end-to-end supply chain tactical planning tool. I have mentioned “tool”, not “optimization” since realistically, an optimizer is not going to work in real-time settings. A feasible tool will be a deep learning tool, trained with optimizer data. More on this in a separate article.
The last article in this series will be published on 12/15. It has been an interesting journey, reading hundreds of papers in this area but as Robert Frost once said:
“The woods are lovely, dark and deep,
But I have promises to keep,
And miles to go before I sleep,
And miles to go before I sleep.”
Appendix
This article is part of a series of articles. Previous parts of this article series can be found here:
Cuckoo and Crow Search Algorithms
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