A tailored adaptive large neighborhood search algorithm for the air cargo partitioning problem with a piecewise linear cost function

Motivated by a leading Chinese multinational manufacturer's practical air cargo logistics activities, we investigate an air cargo partitioning problem with a piecewise linear cost function (ACPP-PLC). In this problem, the charging policy chooses the higher value between the actual weight and the volumetric weight of the cargo as the chargeable weight, where the volumetric weight is calculated from the cargo's volume according to a particular conversion factor. The variable transportation cost is then computed using a piecewise linear cost function of the chargeable weight. We formulate the ACPP-PLC as a mixed-integer linear programming model and propose a tailored adaptive large neighborhood search (TALNS) algorithm, combining the adaptive large neighborhood search algorithm and the tabu search heuristic in a single framework. Numerical experiments show that the proposed TALNS can generate high-quality solutions for all the instances in a much shorter time than the off-the-shelf solver CPLEX. Additional experiments are also conducted to analyze the effects of critical ingredients of the TALNS algorithm.

Automated summary

Motivated by practical air cargo logistics activities of a Chinese multinational manufacturer, this paper investigates an air cargo partitioning problem with a piecewise linear cost function. The problem involves choosing the higher value between the actual weight and the volumetric weight as the chargeable weight, and using a piecewise linear cost function to compute the variable transportation cost. The ACPP-PLC is formulated as a mixed-integer linear programming model, and a tailored adaptive large neighborhood search algorithm is proposed to generate high-quality solutions in a shorter time than the CPLEX solver.