Leveraging AI for Enhancing Robustness and Optimization in Constraint-Based Job-Shop Scheduling with Operators

in complex scheduling scenarios, such as the Job Scheduling Problem (JSP) faced by human operators, the challenge is to optimize the total time mandatory to complete all jobs and the reliability of the schedule. This paper presents a unified approach that combines constraint satisfaction (CSP) techniques with heuristic algorithms to resolve JSP using operators called JSO(n, p), where n is the number of tasks and p is the number of data. The main aims are to minimize the total time required to complete all tasks and to maximize system consistency under uncertainties such as equipment failure or operator unavailability. We analysis branch-and-bound (BB) methods, neural networks, and hybrid algorithms to achieve optimal software solutions. This paper also assesses the impact of bus constraints on traditional scheduling models and suggests solutions for reliability. The evaluation of various algorithmic strategies and extensive experiments on many sampling problems demonstrate the balance between robustness and optimality.