metaheuristic-optimization

Solves large-scale optimization problems using metaheuristic algorithms in Python.

56|16|Updated Oct 18, 2025

Search Tags:#optimization #operations research #simulated annealing #metaheuristics #genetic algorithm #tabu search

Ready to run

npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill metaheuristic-optimization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metaheuristic-optimization
Source: https://github.com/kishorkukreja/awesome-supply-chain/tree/main/skills/metaheuristic-optimization
Command: npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill metaheuristic-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, matplotlib, networkx, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill tackles complex, large-scale optimization problems where exact methods are too slow or impractical, providing high-quality solutions using nature-inspired algorithms.

Core Features & Use Cases

Metaheuristic Algorithms: Implements Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search.
Problem Types: Solves Vehicle Routing Problems (VRP), Job Shop Scheduling (JSSP), and Facility Location problems.
Use Case: Optimize delivery routes for a fleet of vehicles to minimize total distance, or schedule manufacturing jobs on machines to minimize overall completion time.

Quick Start

Use the metaheuristic-optimization skill to solve a vehicle routing problem with the provided customer locations and demands.

Frequently Asked Questions about metaheuristic-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I solve a vehicle routing problem when exact methods are computationally infeasible?▼

To solve a vehicle routing problem when exact methods are infeasible, use metaheuristic algorithms like Genetic Algorithms or Tabu Search. These provide high-quality solutions for large-scale optimization by exploring solution spaces efficiently.

What is the best way to optimize job shop scheduling for manufacturing?▼

The best way to optimize job shop scheduling is using metaheuristic optimization techniques like Simulated Annealing. This approach minimizes overall completion time for manufacturing jobs by finding near-optimal schedules when exact methods are too slow.

Can I use Python and numpy to implement simulated annealing for facility location problems?▼

Yes, you can use Python with numpy and scipy to implement simulated annealing for facility location problems. This Skill leverages these libraries to execute deterministic task solving and find high-quality solutions for complex optimization scenarios.

When should I use genetic algorithms instead of tabu search for operations research problems?▼

Use genetic algorithms for broad exploration of solution spaces in operations research, while tabu search is effective for intensive local search avoiding cycles. Both address large-scale optimization when exact methods are impractical, depending on the problem structure.

What types of large-scale optimization problems can metaheuristics solve?▼

Metaheuristics can solve large-scale optimization problems including vehicle routing, job shop scheduling, and facility location. These nature-inspired algorithms provide high-quality solutions for scenarios where exact methods are computationally too slow or impractical.

How do I visualize optimized delivery routes generated by metaheuristic algorithms?▼

You can visualize optimized delivery routes using matplotlib, which is a core dependency. This allows you to plot the high-quality solutions generated by the genetic algorithm or tabu search for your vehicle routing problem.