复杂约束下单集装箱装载问题的改进元启发式算法

Improved Metaheuristics for Single Container Loading Problem with Complex Constraints

三维单集装箱装载问题(Three-dimensional Single Container Loading Problem,3D-SCLP)因其在制造业和物流业中有着广泛的应用,已成为最优化领域中最经典的工程问题之一。然而,目前的优化方案主要从算法优化改进与局部约束调整等角度考虑,没有充分考虑实际装载过程中的复杂约束需求,如重量限制、负载平衡、货物稳定性、堆叠约束以及人因工程因素,导致现有方法理论装载率虽高,但实用性低。在充分考虑实际多重复杂约束的基础上,提出了一种基于天鹰座优化器的改进元启发式算法。该算法基于种群优化策略,并将差分变异和高斯扰动与潜在点策略相结合,实现复杂约束情况下的快速收敛。在中等规模工业实例数据上进行了算法验证,与传统启发式优化方法相比,所提方法能够解决中等规模复杂约束下的三维装箱优化问题,在实际空间利用率、生成效率等方面优于现有的解决方案。对物流运输行业减少人工成本,实现装箱标准化与智能化具有重要意义。

Three-dimensional single container loading problem(3D-SCLP)has become one of the most classic engineering problems in the field of optimization because of its wide application in manufacturing and logistics.However,the current optimization scheme mainly considers the optimization and improvement of algorithm and local constraints,but fails to fully consider the actual complex constraints,such as weight limit,load balance,cargo stability,stacking constraints and human factors,which leads to the problem that the theoretical loading rate of the existing methods is high,but the practicality is low.In order to solve this problem,this paper proposes an improved meta-heuristic algorithm based on the Aquila optimizer on the basis of fully considering the complex constraints of multiple realities.It is based on population optimization strategy,and combines differential mutation and Gaussian disturbance with potential point strategy to achieve rapid convergence under complex constraints,and it is verified on the data of a medium-scale industrial example.Compared with the traditional heuristic optimization method,the proposed method can solve the three-dimensional packing optimization problem under complex constraints,and is superior to the existing solutions in terms of actual space utilization and generation efficiency,thus embodies intelligent packing,realizes standardization and intelligence of packing,and reduces manual participation.