This article explores how simulating natural phenomena can be used for supply chain design and optimisation. It showcases techniques like Ant Colony Optimisation and genetic algorithms, and how mimicking nature can lead to innovative solutions in supply chain management. It urges professionals to explore these ideas to find innovative and elegant solutions for typical supply chain challenges
Contents
Introduction to Bio-Inspired Algorithms
Bio-inspired algorithms mimic natural processes to solve complex problems. They draw inspiration from biological systems, such as evolution, swarm behaviour, and neural networks. Replicating these scalable algorithms could help optimise supply chain operations in logistics by creating a model that can be applied to various problems.
These algorithms adapt to changing conditions in real-time. They can quickly process vast amounts of data, making them ideal for dynamic logistics environments. By leveraging bio-inspired approaches, companies can achieve more innovative inventory management, better demand forecasting, and more effective resource allocation. This leads to streamlined operations and improved customer satisfaction.
The idea is to create a digital model of the dynamics we see in nature and use these algorithms or models to test scenarios or find solutions more effectively. Here is an analogy:
- Imagine you want to manage a famous football team – Obviously, not everyone is in a position to do so
- Instead, you build a game like Football Manager 2024
- This game (or model) does its best to simulate the real-life mechanics of managing a football team
- Let’s you try things that you couldn’t try in real life
Let’s look at a couple of algorithms based on natural phenomena and see how this can be applied to supply chain challenges:
Ant Colony Optimisation (ACO)
Have you ever noticed how ants can always find something sweet in your house, no matter how hidden it is? Ants don’t have a logistic analyst planning their routes to the nearest food source, so how do they do it?
The answer is that they communicate indirectly through pheromones
- When ants forage for food, they deposit pheromones on the ground to mark the path.
- If other ants travel along a path and find food, they deposit a short-lasting pheromone that triggers a much stronger recruitment from the colony, reinforcing more ants to the path to a confirmed food source.
- Ants leaving the colony can detect these pheromones and are likelier to follow trails with a more pungent scent.
- Shorter routes accumulate pheromones more quickly because ants can travel back and forth faster, reinforcing these paths more frequently.
- This makes shorter routes increasingly attractive.
- Pheromones on longer routes weaken due to slower reinforcement and faster evaporation, and these paths are eventually abandoned.
- Studies have also shown that the pheromones can contain information on the quality and type of the food source, which helps optimise the colony’s logistics system.
This completely natural process guides the colony toward optimal routes. This natural process can be mirrored in logistics and routing problems.
Below:
- A simulated ant colony (Bottom right), finds its way through a maze to the food source (Yellow) by following pheromones left by other ants (red)
- Note that the “best” path dynamically adapts to the changing shape of the food source
This creates a form of collective intelligence which allows ACO to converge on optimal solutions over iterations. The algorithm balances exploration and exploitation: it explores new routes while exploiting known good ones.
In the real world a logistics company might use ACO to minimise fuel costs and delivery times by optimizing vehicle routes. The algorithm simulates thousands of possible paths and continuously refines them based on key performance indicators, such as distance, traffic, or delivery constraints.
Beyond routing, ACO can also be applied to inventory management and warehouse layout design. By treating inventory items as nodes in a network, it can identify optimal storage configurations that reduce retrieval times and improve overall warehouse efficiency.
Inspired by nature, ACO’s strength lies in its ability to learn and adapt over time. This iterative improvement makes it a highly effective approach for enhancing supply chain performance across multiple domains.
Evolution in Nature: Genetic Algorithms
Genetic Algorithms (GAs) are inspired by the principles of natural selection and evolution.
Do you remember how Genetic Algorithms work?
In nature, organisms adapt to their environment through survival of the fittest—genetic traits that confer advantages are passed on and refined over generations. GAs replicate this process computationally to solve complex optimisation problems, including those found in logistics and supply chain management.
Here is the algorithm:
For those who learn visually (Like myself), this is an excellent example explaining how a GA might solve a problem:
How would you apply this to your supply chain?
Instead of a snake trying to find a rat like the above Youtube, imagine using a GA to optimise supply chain parameters.
A company could ask a simulation engineer to create a digital twin (a model) of their supply chain using discrete event simulations (DES)
- A DES model attempts to simulate what happens to the bigger, broader model when a discrete event (zB, a customer order being placed) occurs many times.
- DES often contains a degree of randomness built into the model to simulate realistic conditions.
- For example, the order lead time might be randomly selected from a distribution of historical lead times from a real-world supplier.
- If modelled well enough, this allows the engineer to test many different scenarios, such as changing key parameters that affect the simulation
- See this visualisation comparing a decentral and centralised supply chain configuration to get a sense for DES
Okay so you’ve built a close-to-reality model of your supply chain, how would you answer the following?:
- Minimizing inventory and maximising service level:
- What is the optimal MOQ?
- How frequently should we be reordering and what lot sizes?
Instead of manually adjusting the parameters to find the best supply chain parameters, imagine combining the SC simulation with a Genetic algorithm.
- Follow the algorithm above, but for the fitness test, run each candidate configuration through the simulation a number of times to get a score for that configuration
- This would allow you to assess the performance under realistic conditions, such as order delays, lead time variability, and equipment breakdowns.
- The fitness function would reflect outcomes from the simulation, allowing the algorithm to prioritise solutions that perform best in a dynamic, uncertain environment.
Although computationally intensive, this approach allows optimisation engineers to uncover non-intuitive solutions that traditional linear models might miss.
Genetic algorithms provide a flexible and adaptive toolset in scenarios where trade-offs are complex and constraints are nonlinear. A follow-up post will explore this application in more depth.
Conclusions
Bio-inspired algorithms present a transformative approach to logistics optimisation. By adopting these nature-inspired methods, businesses can leverage the power of optimisation and adaptive strategies, setting new benchmarks for operational excellence.
Research/Reference Material
- Dussutour, A., Nicolis, S. C., Shephard, G., Beekman, M., & Sumpter, D. J. T. (2009). The role of multiple pheromones in food recruitment by ants. Journal of Experimental Biology, 212(15), 2337–2348.
- Dorigo, M., & Stützle, T. (2004). Ant colony optimization. MIT Press.
