Efficient Production Scheduling Under Uncertainty

Abstract

Efficient production scheduling is a critical challenge in modern manufacturing, especially within complex environments characterised by strict operational constraints and inherent uncertainty. This thesis addresses the development and integration of a modular scheduling engine for Occator's OccaSee production management platform, a system previously dependent on manual or external scheduling tools. The challenge lies in balancing the computational complexity of industrial constraints—such as sequence-dependent changeovers, alternative resources, and distinct batch types—with the need for reliable decision support under stochastic disruptions.

To tackle this, a comprehensive scheduling framework was developed. First, a heuristic split-schedule-join paradigm is introduced to handle lot sizing, eliminating combinatorial explosion while preserving order-splitting flexibility. Second, a modular Constraint Programming (CP) model is formulated to optimise batch assignments and sequencing using a multi-step lexicographic approach prioritising lateness, transition costs, and total completion time. To ensure practical industrial viability and scalability, a custom greedy constructive heuristic is integrated as a warm-start mechanism for the CP solver. Finally, operational uncertainty is addressed through a Monte Carlo simulation engine that models historical duration deviations, forecasting schedule robustness and quantifying the value of reactive rescheduling.

Empirical evaluation across diverse static and disrupted instances demonstrates the framework's efficacy. The CP model assisted by the greedy warm-start (CP-WS) guarantees 100% feasibility on highly constrained instances, accelerates early convergence by a factor of 2.3, and reduces lateness and transition costs by approximately 50% compared to the greedy baseline. Furthermore, stochastic experiments reveal that while minor disruptions are best absorbed by existing schedule structures, targeted reactive rescheduling under severe delays recovers an average of over 800 minutes of accumulated lateness. By combining constraint programming, heuristic acceleration, and simulation-based risk analysis, this thesis transforms OccaSee into a dynamic decision support system capable of robust industrial scheduling.