Applying a Genetic Algorithm to a m-TSP: Case Study of a Decision Support System for Optimizing a Beverage Logistics Vehicles Routing Problem

Abstract

Route optimization has become an increasing problem in the transportation and logistics sector within the development of smart cities. This article aims to demonstrate the implementation of a genetic algorithm adapted to a Vehicle Route Problem (VRP) in a company based in the city of Covilhã (Portugal). Basing the entire approach to this problem on the characteristic assumptions of the Multiple Traveling Salesman Problem (m-TSP) approach, an optimization of the daily routes for the workers assigned to distribution, divided into three zones: North, South and Central, was performed. A critical approach to the returned routes based on the adaptation to the geography of the Zones was performed. From a comparison with the data provided by the company, it is predicted by the application of a genetic algorithm to the m-TSP, that there will be a reduction of 618 km per week of the total distance traveled. This result has a huge impact in several forms: clients are visited in time, promoting provider-client relations; reduction of the fixed costs with fuel; promotion of environmental sustainability by the reduction of logistic routes. All these improvements and optimizations can be thought of as contributions to foster smart cities.

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

“…Suppose there are m robots corresponding to the set R = {R i , 1 ≤ i ≤ m}, R i denotes the i-th robot in R. T = T j , 1 ≤ j ≤ n is the set of task points, T j denoting the j-th task point of T. Therefore, the task assignment problem of m robots starting from a fixed starting point to complete n task points and then returning to the original starting point can be converted to a multi-travel merchant model for solving [30][31][32].…”

Enhancing Logistic Efficiency in Product Distribution through Genetic Algorithms (GAs) for Route Optimization

“…This approach signifies the alignment of GA applications with the evolving logistics needs of e-commerce [13]. In practical applications, Gomes et al (2021) used GA to optimize the routing of beverage logistics vehicles, resulting in a significant reduction in total distance traveled, underscoring the practical benefits of the algorithm in real-world scenarios [14]. Li et al (2020) explored the optimization of environmentally friendly fresh food logistics using an improved GA, addressing the problem of heterogeneous fleet vehicle routing.…”

Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants

“…In the context of agro-related industries, from agriculture to retail, and due to the evolution of computational resources, there has been the development of decision support systems (DSS) based on mathematics, statistics and artificial intelligence, to support energy efficiency, production optimization, environmental impact and sustainable management [13]. Some DSS have been developed for irrigation decision-making and water management [14][15][16][17][18][19][20][21], crop yield estimation [22], fruit diseases [23], energy consumption and performance of agri-food facilities [24][25][26][27], food logistics and distribution [28,29], commercialization time of perishable food products [30] and their pricing [31,32].…”