The large-scale Multi-Agent Path Finding (MAPF) problem presents a significant challenge in combinatorial optimization. Currently, one of the advanced, near-optimal algorithms is Large Neighborhood Search (LNS), which can handle instances with thousands of agents. Although a basic portfolio parallel search based on multiple independent LNS solvers enhances speed and robustness, it encounters scalability issues with increasing CPU cores. To address this limitation, we propose the Cooperative Parallel LNS (CPLNS) algorithm, aimed at boosting parallel efficiency. The main challenge in cooperative parallel search lies in designing suitable portfolio and cooperative strategies that balance search diversification and intensification. To address this, we first analyze the characteristics of LNS. We then introduce a flexible group-based cooperative parallel strategy, where the current best solution is shared within each group to aid intensification, while maintaining diversification through independent group computations. Furthermore, we augment search diversification by integrating a simulated annealing-based LNS and bounded suboptimal single-agent pathfinding. We also introduce a rule-based methodology for portfolio construction to simplify parameter settings and improve search efficiency. Finally, we enhance communication and memory efficiency through a shared data filtering technique and optimized data structures. In benchmarks on 33 maps with 825 instances, CPLNS achieved a median speedup of 21.95 on a 32-core machine, solving 96.97% of cases within five minutes and reducing the average suboptimality score from 1.728 to 1.456. Additionally, tests with up to 10,000 agents verify CPLNS's scalability for large-scale MAPF problems.

Parallel-discrete-event-simulation-based analytic simulation (PAS) is an effective approach to studycomplex issues and analyze complex systems.But the complexity and high demand for credibility of PAS make its development and experimentation quite different from traditional information systems. Firstly, this article briefly introduces analytic simulation concept and the difference with training simulation. And then five computational characteristics which cause the huge computation demand are summarized: multi-sample, multi-entity, as fast as possible, synchronization for constraint of causality and complex model calculation. According to these characteristics, a "Sample, Entity, Model" three-level-Parallelization solution(SEMP) is introduced for PAS.The solution can be used to fully exploit the parallelization of PAS and utilize the computing resources in different levels, which is able to meet the growing computation demand of PAS. Finally, in order to improve the development efficiency and credibility of PAS application, based on the accumulation of several years' R&D, we conclude a summary of development and experimentation flow of PAS, and propose four additional VV&A principles to improve credibility, which can be used to guide the development and experimentation of PDES-based analytic simulation.