Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm

Yan Hou, Nai Qi Wu, Meng Chu Zhou, Zhi Wu Li

Research output: Contribution to journalArticlepeer-review

229 Scopus citations


With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

Original languageEnglish (US)
Article number7366610
Pages (from-to)517-530
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number3
StatePublished - Mar 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Crude oil operations
  • discrete-event and hybrid system
  • genetic algorithm (GA)
  • short-term scheduling


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