基于海鸥优化算法的企业平衡运输问题研究

Research on Enterprise Balanced Transportation Problem Based on Seagull Optimization Algorithm

【目的】智能时代背景下物流需求的运输成本精准预测对于资源调度及管理起着关键作用,本研究是为了丰富当前解决运输问题的方法,简化NP-hard问题的局限性。【方法】针对企业平衡运输成本问题,归纳了当前运输问题分类,以运输总成本最小化为目标,采用了传统运输问题的平衡数学模型,并运用了国外最新智能优化算法——海鸥优化算法来求解,通过迁移、攻击寻找目标函数的最优解。【结果】仿真实验结果证明了海鸥优化算法与传统管理运筹学方法、量子粒子群算法、遗传算法的求解结果相吻合。【局限】海鸥优化算法是新兴元启发式算法,仍在不断发展演变,由于相关文献的局限性,仍有待对其进一步研究。【结论】通过本研究验证了海鸥优化算法的有效性和优越性,为企业平衡运输问题提供了新的智能优化算法解决方案。

[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.