The The Logistics Optimizer is a solution to an NP-hard problem in logistics involving optimal space allocation for warehouses and distribution centers. This project implements an Enhanced Cuckoo Search Algorithm with Gaussian Mixture Clustering (ECS-GMC) to efficiently and effectively allocate logistical resources, maximizing space utilization and minimizing costs.
In logistics, effective space allocation is crucial for optimizing operations, reducing costs, and improving service levels. Traditional methods often struggle with the complexity and variability inherent in this NP-hard problem. Our approach leverages meta-heuristic algorithms to find near-optimal solutions in a reasonable time frame.
The Cuckoo Search Algorithm is a nature-inspired optimization technique that mimics the brood parasitism of some cuckoo species. This implementation enhances it with:
- Adaptive Step Size: Dynamically adjusts the step size based on the search progress.
- Levy Flights: Incorporates Levy flights to improve exploration capabilities.
Gaussian Mixture Models (GMM) are employed to cluster the data points. By integrating GMM with the cuckoo search process, we allow for:
- Efficient grouping of similar allocation needs.
- Improved initialization of potential solutions.
- Better representation of diverse solution spaces.
- Robust Multi Optimization: Addresses complexities of NP-hard space allocation problems.
- Meta-Heuristic Approach: Combines the strengths of cuckoo search and Gaussian mixture clustering.
- Flexibility: Easily adaptable to various logistics scenarios and constraints.
- Performance: Efficiently handles large datasets and provides near-optimal solutions.
- Clone the repository:
git clone https://github.com/yourusername/logistics-space-allocator.git