Optimization of Production Scheduling Using SAP PP and Heuristic Algorithms in Discrete Manufacturing

Abstract

Discrete manufacturing Production scheduling is a complex operational issue at an operational level that involves satisfying the resource, capacity, precedence and due-date constraints as well as maximizing the performance measures such as makes pan, delays and resource used. Due to its NP-hardness in flow-shop, job-shop and mixed-model settings, it is impossible to optimize it precisely, which makes heuristic and metaheuristic methods necessary. The paper has provided an integrated model that incorporates the use of SAP Production Planning (PP) and a demand-based heuristic scheduling algorithm in closing the gap between the high-level planning and a shop-floor implementation. The proposed method uses real-time demand, inventory viability, machine, workforce and warehouse capabilities to produce attainable and near optimal schedules. Moreover, the framework utilizes SAP S/4HANA, sophisticated analytics, and an adaptive planning loop on a Digital Twin scale to make it more responsive and scalable. The findings reveal a better scheduling flexibility, decreased computation time, increased resource exploitation, and enhanced compliance with the industry 4.0 goals.