This report investigates the effectiveness of Simple Temporal Networks with Uncer- tainty (STNUs) for solving the Stochastic Flexible Job-Shop Scheduling Problem with Sequence-Dependent Setup Times (SFJSP-SDST), comparing it against proactive and reactive Constraint Programming (CP) approaches. Using a benchmark dataset with varying noise levels, the study evaluates solution quality, feasibility, and computational cost. Results show that the reactive method achieves the lowest makespan due to its dy- namic rescheduling capability but incurs high online computation time. The proactive method offers fast execution, while the STNU-based approach provides a dynamically controllable schedule, albeit with conservative makespans.

This study investigates the performance of three dynamic scheduling approaches—proactive, reactive, and STNU-based—for solving the Multi-Mode Resource-Constrained Project Scheduling Problem with maximal time-lags and no-wait constraints (MMRCPSP/max) in uncertain environments. The performance of the approaches is validated on three key performance measures: solution quality (makespan), offline computation time, and online computation time. Drawing from the current work on stochastic RCPSP/max, this research introduces a more realistic formulation of the problem with multi-mode execution, generalised time lags, and no-wait constraints, under the PyJobShop library and stochastic duration modelling. Experimental results, based on altered PSPLIB instances, show that the three algorithms yield similar feasibility rates. However, they show distinct trade-offs in terms of solution quality, time offline, and time online. The proactive algorithm requires the shortest offline and online computational times, but provides slightly worse makespans. The STNU-based algorithm produces the best average solution quality, with significantly higher offline time. The reactive algorithm offers competitive offline times and solution quality, but has the largest online computational time. The findings provide insight into the trade-offs in solution quality and computational time in dynamic project scheduling and offer insight for using the strategies in the real world.

This study investigates scheduling strategies for the stochastic duration flexible job-shop problem with no-wait and general time lags constraints (FJSP/NW-GTL). Progress in Constraint Programming (CP) and temporal-networks has renewed interest in assessing the strengths and limitations of different proactive and reactive scheduling approaches. This paper covers the application of a CP-based fully proactive method, a reactive method and STNU-based method on the FJSP/NW-GTL problem comparing results in terms of predetermined objectives and feasibility. In addition, the paper aims to answer how different distributions for task duration affect feasibility and performance. Our results show that strictly proactive methods are infeasible for no-wait constraints and very tight schedules, which lead to adding an online step in the proactive implementation to pursue the comparison between the approaches. With this change, plotting the average makespan across methods by distribution shows that there is not much fluctuation between distribution types in terms of makespan. Moreover, it appears that the proactive method performs the best, followed closely by the reactive method, while the STNU approach results in a notably higher makespan for the same instances. Notably, in terms of feasibility, the proactive and reactive approach have 100% rate of success compared to the STNU approach which is infeasible on 35% of the instances in the dataset.

Modern manufacturing systems must meet hard delivery deadlines while coping with stochastic task durations caused by process noise, equipment variability, and human intervention. Traditional deterministic schedules break down when reality deviates from nominal plans, triggering costly last-minute repairs. This thesis combines offline constraint-programming (CP) optimisation with online temporal-network execution to create schedules that remain feasible under worst-case uncertainty.

First, we build a CP model of the flexible job-shop with per-job deadline tasks and insert an optimal buffer Δ* to obtain a fully pro-active baseline. We then translate the resulting plan into a Simple Temporal Network with Uncertainty (STNU) and verify dynamic controllability, which guarantees that a real-time dispatcher can retime activities for every bounded duration realisation without violating resource or deadline constraints. Extensive Monte-Carlo simulations on the open Kacem 1–4 benchmark suite show that our hybrid approach eliminates 100 % of deadline violations observed in state-of-the-art meta-heuristic schedules, while adding only 3 – 5 % makespan overhead. Scalability experiments confirm that CP solve-times and STNU checks remain sub-second on medium-size instances.

The work demonstrates how temporal-network reasoning can bridge the gap between proactive buffering and dynamic robustness, moving industry a step closer to truly digital, self-correcting factories.