Drone scheduling optimization for shore-to-ship delivery and waste recycling

Abstract

Traditional shore-to-ship delivery and waste recycling predominantly rely on replenishment vessels, facing critical challenges such as low operational efficiency, high costs, and prolonged waiting times during multi-vessel operations. To address these challenges, this paper addresses the Drone Scheduling Problem in Shore-to-Ship Delivery and Recycling (DSP-SSDR). Specifically, we develop a mixed-integer programming (MIP) model to minimize the total completion time of all required tasks while accounting for practical constraints including drone load capacity, time windows, battery consumption, multiple trips, and dynamic base station return. To efficiently solve this problem, an adaptive large neighborhood search (ALNS) algorithm is proposed and improved, which dynamically adapts removal and repair operators and incorporates a two-layer local search to improve the solution quality. Numerical experiments using practical realistic test cases demonstrate that the proposed ALNS algorithm achieves superior performance compared to the commercial solver Gurobi, particularly in computational efficiency for large-scale instances.