Abstract:

[Objective] Under the background of the intelligent era, accurate prediction of transportation cost of logistics demand plays a key role in resource scheduling and management. This paper aims to enrich the current methods to solve transportation problems and simplify the limitations of NP-hard problems. [Methods] Concerning the problem of balancing enterprise transportation costs, this paper summarizes the classification of current transportation problems. Taking the minimization of total transportation cost as the goal, this paper adopts the balanced mathematical model of traditional transportation problems and uses the latest intelligent optimization algorithm called seagull optimization algorithm to find the optimal solution through migration and attack. [Results] The simulation results show that the seagull optimization algorithm is consistent with the traditional management operations research method, quantum particle swarm optimization algorithm and genetic algorithm. [Limitations] The Seagull optimization algorithm is a new meta-heuristic algorithm which is still evolving. Due to the limitations of related literature, it still needs to be further studied. [Conclusions] This study verifies the effectiveness and superiority of the seagull optimization and provides a new intelligent optimization algorithm solution for enterprise balanced transportation problems.

Key words: transportation problem, cost minimization, seagull optimization algorithm, Meta-heuristic algorithm, MATLAB