Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (5): 185-198.doi: 10.3969/j.issn.1000-5641.2021.05.016

• Data Analysis and Applications • Previous Articles    

Research on multi-objective cargo allocation based on an improved genetic algorithm

Ping YU, Huiqi HU*(), Weining QIAN   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2021-07-27 Online:2021-09-25 Published:2021-09-28
  • Contact: Huiqi HU


In this paper, we propose a mathematical model to solve the multi-objective cargo allocation problem with greater stability and efficiency; the model for cargo allocation maximizes the total cargo weight, minimizes the total number of trips, minimizes the number of cargo loading and unloading points, and offers fast convergence based on the elitism genetic algorithm (FEGA). First, a hierarchical structure with the Pareto dominance relation and an elitism retention strategy were added on the basis of the genetic algorithm. This helped to improve the population diversity while accelerating the local search ability of the algorithm. Then, the random structure of the initial population was modified, and a double population strategy was designed. An adaptive operation was subsequently added to sequentially improve the global search ability of the algorithm and accelerate the convergence speed of the population. Based on the new algorithm, real cargo data were used to demonstrate the feasibility and optimization potential of the new method. The results show that compared with the traditional genetic algorithm, the proposed algorithm has a better optimization effect in solving the cargo allocation process with strong constraints and a large search space; the search performance and convergence, moreover, are also improved.

Key words: multi-objective optimization, cargo allocation, genetic algorithm

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