route-optimization

Optimize delivery routes and solve vehicle routing problems with MIP and heuristics.

56|16|Updated Oct 18, 2025

Search Tags:#optimization #logistics #delivery planning #VRP #transportation #route optimization

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps solve complex vehicle routing problems (VRP) to minimize transportation costs, reduce travel distance, and improve fleet efficiency.

Core Features & Use Cases

Route Planning: Generates optimal routes for single or multiple vehicles.
Constraint Handling: Accounts for time windows, vehicle capacities, and delivery/pickup requirements.
Algorithm Variety: Supports exact methods (MIP) and various heuristics (Nearest Neighbor, Clarke-Wright, Sweep) and metaheuristics (Simulated Annealing, Genetic Algorithms).
Use Case: A logistics company needs to plan daily delivery routes for 50 stops across the city, ensuring all deliveries are made within their specified time windows while minimizing fuel consumption.

Quick Start

Use the route-optimization skill to find the most efficient delivery routes for the given customer locations and constraints.

Frequently Asked Questions about route-optimization

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

FAQPage Schema

How do I optimize delivery routes with time windows and vehicle capacity constraints?▼

To optimize delivery routes with constraints, this skill solves vehicle routing problems (VRP) using Mixed-Integer Programming and heuristics to minimize transportation costs. It handles time windows, capacities, and multiple vehicles efficiently.

What is the best way to solve a multi-depot vehicle routing problem for a logistics fleet?▼

Solving a multi-depot vehicle routing problem (MDVRP) requires allocating stops to depots and optimizing paths. This skill supports MDVRP by applying exact methods and metaheuristics like Genetic Algorithms to maximize fleet utilization.

Can I use Python to plan routes using the Clarke-Wright Savings algorithm?▼

Yes, you can use Python to plan routes with the Clarke-Wright Savings algorithm. The skill implements this heuristic alongside Nearest Neighbor and Sweep algorithms to generate efficient delivery sequences for logistics operations.

When should I use Simulated Annealing versus Mixed-Integer Programming for transportation optimization?▼

Use Mixed-Integer Programming for exact optimization on smaller vehicle routing problems, and Simulated Annealing for larger, complex transportation networks. This skill provides both approaches to balance computational cost and route accuracy.

Does this route optimization tool support dynamic vehicle routing?▼

Yes, this route optimization tool supports dynamic vehicle routing (Dynamic VRP). It adjusts planned delivery sequences to accommodate real-time changes, minimizing travel distance and improving fleet efficiency.

What Python dependencies are required to run vehicle routing problem solvers?▼

Running vehicle routing problem solvers requires the Python dependencies pulp, numpy, scipy, and matplotlib. These libraries support mathematical modeling, matrix operations, and visualizing the optimized delivery routes.