applied sciences Article  
A Multiproject and Multilevel Plan Management Model Based on a Hybrid Program Evaluation and Review Technique and Reinforcement Learning Mechanism  
[PERSON], [PERSON],*(c), [PERSON], [PERSON] and [PERSON] College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (M.L.) Capital Aerospace Machinery Co., Ltd., Beijing 100076, China; [EMAIL_ADDRESS] (M.X. ); [PERSON]_ADDRESS] (Y.W.) Correspondence: [EMAIL_ADDRESS]  
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Footnote  
department knows the time margin of a project. However, to obtain more resources, the actual margin is generally not disclosed to personnel outside the project team. Some new manufacturing enterprises or production workshops will try their best to avoid the mixed-line production of production projects when implementing the industrial layout. By constructing independent production units and lines, the abovementioned problems can be effectively avoided. However, most manufacturing enterprises have already formed enormous fixed assets. To undertake more production projects and respond to diversified market demands, mixed-line production must be carried out, and the products of different projects must be processed on each piece of processing equipment. Moreover, considering the cost of fixed asset investment, enterprises generally find it difficult to carry out large-scale transformation and upgrading of existing production resources. In such a situation, it is necessary for enterprise managers to organize the overall arrangement of project production plans and pursue the overall interests of the enterprise. Objective management is necessary for project surplus, project schedule impact, and project priority. In reality, multiproject management relies on the experience of managers, resulting in a lack of lean management in enterprises and a loss of overall benefits. Based on the current situation of multiproject and multilevel planning management in manufacturing firms, this paper comes up with a planning model that combines PERT and reinforcement learning algorithms, namely, PERT-RP-DDPGAO. PERT is a technique that utilizes network analysis to develop and quantitatively evaluate plans. This paper decomposes the project plan through the PERT model and incorporates a feedback mechanism, which enables the PERT model to have dynamic optimization capabilities, complete the decomposition of the project plan, and distribute the decomposed enterprise project-level plan to various production units. After these plans arrive at the production unit, they are preliminarily decomposed into various pieces of processing equipment based on process documents, equipment resources, and working hours. This paper uses a manufacturing execution system (MES) to extract resource demand plans from processing equipment plans at 7-day intervals. The resource demand plans are used as intelligent agents, cleverly using matrix calculation methods to achieve intelligent agent actions. In PERT-RP-DDPG, RP refers to resource planning.