Su Nguyen

TL;DR: The state-of-the-art approaches are summarized, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling are summarized and suggested.

TL;DR: Four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments are developed.

TL;DR: In this paper, an optimization model is proposed to maximize the economic benefits for rooftop PV-battery DG in a peer-to-peer (P2P) energy trading environment, which is illustrated in a simulation framework for a local community with 500 households under real-world constraints encompassing PV systems, battery storage, customer demand profiles and market signals including the retail price, feed-in tariff and P2P energy trading mechanism.

TL;DR: Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP).

TL;DR: A unified framework for automated design of production scheduling heuristics with genetic programming is developed and shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.