Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling


Güleç N., Kabak Ö.

Processes, vol.12, no.5, pp.926-953, 2024 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 12 Issue: 5
  • Publication Date: 2024
  • Doi Number: 10.3390/pr12050926
  • Journal Name: Processes
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Page Numbers: pp.926-953
  • Ankara Yıldırım Beyazıt University Affiliated: No

Abstract

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.