IoT-ML-enabled multipath traveling purchaser problem using variable length genetic algorithm

Abstract

The Internet of Things (IoT), a modern technology, and machine learning (ML) are used to make immediate decisions. Due to the massive development of roadside infrastructure and increasing digitalization, current procurement planning is based on primary data, and there are several paths connecting markets and cities for travel. Integrating physical and cyber systems within the framework of Industry 4.0 through intelligent metaheuristic methods is more useful. Accordingly, we propose IoT-enabled and ML-based multipath traveling purchaser problems (IoT-ML-MPTPPs) for minimum cost or time and develop an ML-based variable-length genetic algorithm (ML-VLGA) to solve the proposed problems. To purchase an item, a purchaser starts from the depot with a vehicle, visits the markets for purchase until the prespecified demand is satisfied, and returns to the depot. Thus, the present investigation aims to select the appropriate markets and optimal routing route design for minimum cost or time. In developing tropical countries, travel costs and time depend on weather and key road features such as road surfaces and congestion. In real-life scenarios, the proposed IoT-ML-MPTPPs provide insights for optimizing procurement planning and transportation logistics amid dynamic factors such as weather conditions, congestion, and road surfaces. Here, the IoT supplies the above real-time parameters during the purchaser’s journey, which are used to predict the vehicle’s velocity and per unit travel and transportation costs by applying an ML method, which enhances the intelligent decision-making process. To solve the above IoT-ML-MPTPPs, an efficient problem-specific ML-VLGA with probabilistic selection and ML-based crossover is developed and applied. Comprehensive numerical experiments are performed rigorously evaluate and validate the performance of the developed ML-VLGA. These experiments demonstrate its effectiveness in both simulated scenarios and real-world applications. Managerial insights are drawn that support the use of the model.