Balanced multi-objective evolution algorithm for unmanned systems project scheduling with preventive maintenance and order grouping constraints

With the growing deployment of unmanned systems in multi-platform operations, intelligent scheduling has become increasingly critical. These systems are typically organized into distributed mission clusters, executing sequences of mission-critical operations under resource and operational constraints. Inspired by the structural similarity between unmanned systems coordination and manufacturing workflows, this paper reformulates the scheduling problem of unmanned missions as a distributed permutation flowshop scheduling problem. Two domain-specific factors are incorporated into the model: preventive maintenance and order grouping, with the objective of minimizing total tardiness and makespan. To address this problem, a balanced multi-objective evolution algorithm (BMOEA) is proposed. Initially, two improved heuristic algorithms based on NEH2 are used to balance the quality and diversity of initial solutions. Then, four target-specific operators and two crossover operators are designed to improve the search efficiency of the algorithm. Next, three criteria are developed to balance local and global search: a classification-based operator selection criterion, which dynamically adjusts the search direction of operators to optimize local search; a non-periodic evaluation criterion based on Kernel Density Estimation and a non-dominated solution threshold criterion, which accurately determines the timing for switching to global search. These criteria balance exploration and exploitation, allowing the algorithm to optimize both convergence speed and population diversity, expand the feasible domain, and steadily approach the Pareto front. Finally, the experimental results reveal that BMOEA delivers superior performance compared to the most advanced algorithms available.