Safe and efficient dismantling of radioactive components in nuclear facility decommissioning requires consideration of both operational constraints and the evolving radiation field. However, existing sequence optimization methods mainly rely on static radiation field evaluations and predetermined cutting positions, which may result in operationally infeasible sequences and inaccurate assessment of personnel radiation exposure. This study develops a computational framework for dismantling sequence optimization by integrating dynamic radiation field simulation with constraint-aware decision-making. A dynamic point-kernel radiation calculation program is coupled with a Python-based optimization framework through Cython to update dose-rate information after each dismantling operation. An AND/OR relationship model is further introduced to represent complex precedence and operational feasibility constraints among radioactive components. Based on the updated radiation field and feasible action set, the framework dynamically determines the dismantling sequence and cutting positions while minimizing cumulative radiation exposure to personnel. The results demonstrate that the proposed framework can simultaneously account for radiation field evolution and operational constraints during sequential dismantling, providing a feasible approach for radiation-aware dismantling planning in complex nuclear facilities.
 

Nuclear Facility Decommissioning,Cutting Sequence Optimization,Precedence Constraints,Dynamic radiation field