<p>This study aims to enhance the manufacturing reliability of forging machines in the steel forging process by integrating order specifications and resource types, including order weight, ingot size, machine conditions, and die design. To achieve this, a two-phase integrated information flow is developed to consolidate available resources and generate feasible process plans. The mission time estimated for each forging machine in the first phase is then incorporated into a proposed mission time-adapted continuous-time Markov chain (MTA-CTMC) in the second phase, enabling optimized order-resource allocation and improved forging machine reliability, particularly during die replacement. To effectively solve this many-objective optimization framework, a performance indicator-based many-objective artificial bee colony algorithm (π-ABC) is introduced, supporting transparent and efficient decision-making. Experiments using real-world forging process data validate the effectiveness and practical applicability of the proposed approach</p>