Abstract
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 language | English (US) |
|---|---|
| Article number | 7366610 |
| Pages (from-to) | 517-530 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
| Volume | 47 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2017 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Crude oil operations
- discrete-event and hybrid system
- genetic algorithm (GA)
- short-term scheduling