Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using Google Maps API

• Published in: Engineering applications of artificial intelligence 2015-02, Vol.38, p.122-130
• Main Authors: Király, András, Abonyi, János
• Format: Article
• Language: English
• Subjects: Algorithms; Combinatorial analysis; Computing time; Demand; Digital mapping; Genetic algorithm; Genetic representation; Google Maps; Materials handling; Matlab; mTSP; Optimization
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2014.10.015

If a mobile mechanic has to travel for material, productive time is lost. This paper presents a novel method to reduce activities regarding material handling with extending of serving locations. The design of the supply system can be considered as a complex combinatorial optimization problem, where the goal is to find a route plan with minimal route cost, which services all the demands from the central warehouses while satisfying the capacity and other constraints. We present a multi-chromosome technique for solving the multiple Traveling Salesman Problem (mTSP). The new operators based on a problem-specific representation proved to be more effective in terms of flexibility, complexity and transparency, and also in efficiency than the previous methods. The proposed optimization algorithm was implemented in MATLAB and integrated with Google Maps to provide a complete framework for distance calculation, definition of the initial routes, and visualization. This integrated framework was successfully applied in the solution of a real logistic problem, in the supply of mobile mechanics at one of Hungary׳s biggest energy providers. [Display omitted] •Novel genetic algorithm for multiple Traveling Salesman Problem.•Improved genetic operators to solve mTSP by a novel genetic algorithm.•Faster and more accurate method than previous approaches.•Novel automated Google Maps-based framework for the optimization of mTSP.