Adaptive large neighbourhood search is steered by high-level control decisions, yet its standard adaptation reacts to a narrow reward signal that conveys little about the state of the search. Recent work has strengthened this control with machine-learning and reinforcement-learning policies, but these generally require task-specific training. This paper proposes a framework in which a large language model acts as a bounded, training-free runtime supervisor of the search. The central contribution is a structured telemetry interface that compresses the evolving search into a compact, reproducible state representation over which the model can reason. The supervisor adjusts only search-control parameters and never modifies the solution, so construction, feasibility, and objective evaluation remain inside the metaheuristic. The framework is evaluated on a multi-depot capacitated vehicle routing problem with time windows on real city street networks, against conventional variants and two dedicated solvers. Under an equal iteration budget, the supervised algorithm reduces the mean gap to the best per-instance result from 2.93% to 0.18%, and its difference from the strongest dedicated solver is not statistically significant, although it runs at a higher computational cost. These results indicate that a language model can control metaheuristic search effectively when supported by a structured interface between the algorithm and the model.

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality.