In manufacturing, efficiency is everything. But how do you balance costs, time, and resources without making costly mistakes?

This is where Artificial Intelligence (AI) comes in, specifically a tool called Genetic Algorithms (GAs). These algorithms act like digital problem solvers, testing and improving solutions to save time, cut costs, and boost performance.

The Problem

Imagine you run a factory making luxury faucets. You face tough decisions every day:

• Which metal supplier should you use?
• How much of your production should be automated?
• How many workers should you assign to each assembly line?

The goal is to find the perfect balance between cost, speed, and quality. But testing every possible combination manually would take forever.

What Are Genetic Algorithms?

Genetic Algorithms are like a “survival of the fittest” tool for your business. They test thousands of solutions, keep the best ones, and combine them to create even better strategies. Over time, they evolve to find the most efficient way to run your factory.

Example Solution

For your faucet factory, one possible solution might be:

• Use ABC Brass Suppliers
• Automate 60% of production
• Run two assembly lines

Expected Results:

• Cost: $12 per faucet
• Output: 100 faucets per day
• Quality: 98% pass inspection

This is just one option. There are countless other combinations that could result in higher or lower costs, faster or slower production, and better or worse quality.

How GAs Work

Genetic Algorithms test many solutions like this one. They keep the best-performing ones and combine them to create new solutions. For example, they might take the supplier choice from one solution and the automation level from another to form a new plan.

After testing hundreds of thousands of combinations, the algorithm narrows down to the winning strategy—one that balances cost, speed, and quality perfectly.

It’s like having a super-smart assistant that works tirelessly to find the best way to run your business.

Gory Details for the Technically Inclined

Genetic Algorithms (GAs) solve problems by simulating the process of evolution. Here’s the technical process step-by-step:

Genetic Algorithms (GAs) solve problems by mimicking evolution:

1. Define the Problem as a Population of Solutions
   Each solution is an “individual” in a population, represented as a “chromosome” (a set of variables). For example: “Use Supplier A, automate 50%, allocate 30% to Line 1.”

2. Evaluate Fitness
   Test each solution based on goals (e.g., minimizing cost or time). A solution reducing costs by 30% but increasing time by 10% might score lower than one reducing both.

3. Select the Best Solutions
   Choose the “fittest” solutions (best-performing) to reproduce.

4. Combine Solutions (Crossover)
   Combine parts of two good solutions to create a new one. For example:

   • Solution 1: “Use Supplier A, automate 50%.”

   • Solution 2: “Use Supplier B, automate 30%.”

   • New Solution: “Use Supplier A, automate 30%.”

5. Introduce Random Changes (Mutation)
   Make small random changes to explore new possibilities, e.g., changing “automate 30%” to “automate 40%.”

6. Repeat the Process
   Repeat over multiple “generations,” gradually improving solutions.

After several iterations, the Genetic Algorithm identifies the best solution, e.g., “Use Supplier A, automate 60%, and allocate resources to minimize time and cost.”

Why It’s Useful for Business

Genetic Algorithms are incredibly helpful for solving tough problems with too many variables for traditional methods to handle. In manufacturing, they can:

• Cut costs by finding the best suppliers and improving production processes.

• Save time by optimizing manufacturing schedules and delivery timelines.

• Balance competing goals, like reducing costs while speeding up production.

Real World Example

A study by Alfayoumi et al in the International Journal of Advanced Computer Science and Applications showed how Genetic Algorithms (GAs), a type of AI inspired by evolution, can optimize supply chains and manufacturing processes to save time, cut costs, and improve performance.

In one example, a bicycle manufacturer used GAs to optimize their supply chain and production for custom orders. The results were impressive:

• 20.4% faster production compared to expert solutions.

• 29.8% lower costs than expert estimates.

• 25.5% better overall performance when balancing time and cost.

En AAXIS, we leveraged Genetic Algorithms y Reinforcement Learning, to develop a solution to optimize pallet loading for transportation. The result?

• A dramatic improvement in pallet space utilization, reducing the number of pallets required for shipping while maximizing truck space efficiency.

• This not only cut transportation costs but also reduced the company’s carbon footprint by minimizing the number of trips required.

Key Takeaway