Abstract
The Internet of Things integrates lots of capacitated vehicles in smart logistics. The routing for capacitated vehicles is a combinatorial optimization problem which has been widely studied in recent years. This paper proposes an effective order-aware hybrid genetic algorithm for the capacitated vehicle routing problem in the Internet of Things. The method is characterized by an improved initialization strategy and a problem-specific crossover operator. The former combines the sweep algorithm with randomness to harmonize the contradiction between diversity and convergence, while the latter integrates neighborhood search heuristics to find the offspring with the best fitness and check constraints simultaneously. A large number of simulations have been carried out, and the results validated the effectiveness of our algorithm.
Highlights
In the construction of smart cities, Internet of Things and smart logistics have become an emerging topic in recent years [1], [2], which applies a large number of capacitated vehicles to deliver orders
This paper presents an Order-aware Hybrid Genetic Algorithm (OHGA) for Capacitated Vehicle Routing Problem (CVRP) in Internet of Things, which combines two heuristics to address the two impediments mentioned above
THE EXPERIMENT RESULTS AND DISCUSSIONS This section introduces the validation of the effectiveness of the OHGA
Summary
In the construction of smart cities, Internet of Things and smart logistics have become an emerging topic in recent years [1], [2], which applies a large number of capacitated vehicles to deliver orders. Another impediment of genetic algorithm for solving CVRP is the generation of infeasible individuals, which often happens in the crossover process [10]. This paper presents an Order-aware Hybrid Genetic Algorithm (OHGA) for CVRP in Internet of Things, which combines two heuristics to address the two impediments mentioned above. The main contributions of our study include: (1) An improved population initialization strategy combining the sweep algorithm and randomness is presented to accelerate convergence as well as ensure the population diversity; (2) A problem-specific crossover operator integrating neighborhood search heuristics is proposed to find best offspring and check constraints simultaneously; (3) Extensive simulations were carried out to verify the effectiveness of our method.
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