An adaptive large neighborhood search for the multi-point dynamic aggregation problem

The multi-point dynamic aggregation (MPDA) problem is a challenging real-world problem. In the MPDA problem, the demands of tasks keep changing with their inherent incremental rates, while a heterogeneous robot fleet is required to travel between these tasks to change the time-varying state of each task. The robots are allowed to collaborate on the same task or work separately until all tasks are completed. It is challenging to generate an effective task execution plan due to the tight coupling between robots’ abilities and tasks’ incremental rates, and the complexity of robot collaboration. For effectiveness consideration, we use the variable length encoding to avoid redundancy in the solution space. We creatively use the adaptive large neighborhood search (ALNS) framework to solve the MPDA problem. In the proposed algorithm, high-quality initial solutions are generated through multiple problem-specific solution construction heuristics. These heuristics are also used to fix the broken solution in the novel integrated decoding-construction repair process of the ALNS framework. The results of statistical analysis by the Wilcoxon rank-sum test demonstrate that the proposed ALNS can obtain better task execution plans than some state-of-the-art algorithms in most MPDA instances.