A JavaScript-Based Genetic Algorithm for Real-Time Route Optimization: Toward Lightweight Web Integration in Healthcare and Logistics

Abstract

Efficient route optimization is essential in healthcare and logistics systems, where real-time decision-making significantly affects operational effectiveness. This paper introduces a lightweight implementation of a genetic algorithm (GA) in JavaScript, designed to solve the shortest route problem as a variant of the Traveling Salesman Problem (TSP). The algorithm operates entirely in the browser console, demonstrating the potential of client-side computation for fast, portable optimization. The GA framework integrates tournament selection, two-point ordered crossover, and swap mutation to evolve route solutions over 200 generations. Tested on a synthetic 11-city dataset, the algorithm achieved near-optimal performance with an average deviation of 4.28% from the known optimum and an average runtime of 1.26 seconds. Convergence occurred around generation 138 across five independent runs, indicating stable and consistent behavior despite stochastic initialization. While no graphical user interface was developed in this study, the use of native JavaScript allows future integration with interactive web applications and mobile dashboards. Comparative references suggest the algorithm performs competitively with existing metaheuristics under similar problem sizes. These findings highlight the feasibility of browser-based optimization as a foundation for accessible, real-time routing tools in decentralized healthcare and transport settings.