Comprehensive Unit Price Estimation for Temporary ShipRepair Based on an LSTM–PPO Algorithm

Cost settlement for temporary repair of ship equipment is characterized by lengthy ex post audits and the lack of a directly quotable pricing benchmark. Given the intertwined effects of tight schedules, holiday wage premiums, and fluctuating resource availability—and the consequent need for historical memory and foresight in the model—an LSTM–PPO comprehensive unit price estimation model is developed. Because a standard Markov decision process cannot distinguish different historical paths or exploit forward-looking information, the problem is formulated as a finite-horizon partially observable Markov decision process (FH-POMDP), with a corresponding observation vector and a composite reward function. To capture time-varying holiday rates and the path dependence of historical trajectories, an LSTM encodes the full observation sequence and, through its gating mechanism, fuses historical trajectories with temporal changes in holiday windows in the hidden state, enabling the policy to anticipate rate shifts and allocate labor input in advance. For the hybrid action space of daily mode selection and intensity adjustment, hybrid entropy regularization is introduced to discourage premature collapse onto a single mode preference early in training and to improve robustness across diverse scenarios. Experiments show that the proposed method produces benchmark unit prices with smaller deviations from actual settlement prices than the baselines on the test set, and that it can discriminate holiday windows with different rate multipliers and resource conditions, thereby providing technical support for the ex post settlement of emergency support funds.