Minimum latency problem (MLP) is an NP-Hard combinatorial optimization problem. Metaheuristics use perturbation and randomization to arrive at a satisfactory solution under time constraints. The current work uses deterministic local search heuristic (DLSH) to identify a satisfactory solution without setting up metaheuristic parameters. A move evaluation procedure is proposed for the swap approach which computes a move in O(1) order. Additionally, GPU-based parallel deterministic local search heuristic (PDLSH) is also proposed. PDLSH parallelizes the solution improvement phase and solves MLP for larger instances than the state-of-the-art. The DLSH and PDLSH implementations are tested on the TRP and TSPLIB standard instances. DLSH reaches new best solutions for five TSPLIB instances, namely eil51, berlin52, pr107, rat195, and pr226. The proposed PDLSH achieves a speedup of up to 179.75 for the instances of size 10–11,849 nodes compared to its sequential counterpart.

最小等待时间问题（MLP）是NP-Hard组合优化问题。元启发法使用摄动和随机化在时间限制下获得令人满意的解决方案。当前的工作使用确定性局部搜索启发式（DLSH）来确定令人满意的解决方案，而无需设置元启发式参数。针对交换方法提出了移动评估程序，该交换方法以O（1）顺序计算移动。此外，还提出了基于GPU的并行确定性本地搜索启发式（PDLSH）。PDLSH并行解决方案改进阶段，并针对比现有技术更大的实例解决MLP。DLSH和PDLSH实现在TRP和TSPLIB标准实例上进行了测试。DLSH为5个TSPLIB实例（即eil 51，柏林52，PR 107，大鼠195和PR 226相比，其顺序对应所提出PDLSH实现了一个加速到179.75为大小的实例10-11,849节点。