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A novel algorithm combining finite state method and genetic algorithm for solving crude oil scheduling problem.

Qian-Qian Duan¹, Gen-Ke Yang¹, Chang-Chun Pan¹

¹Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China.

Thescientificworldjournal

|April 29, 2014

Summary

This summary is machine-generated.

A new hybrid optimization algorithm combines finite state method (FSM) and genetic algorithm (GA) to improve crude oil scheduling. This approach enhances local search and global performance, outperforming existing methods.

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Area of Science:

Operations Research
Artificial Intelligence

Background:

Crude oil scheduling is a complex optimization problem.
Existing methods like genetic algorithms (GA) have limitations in local searching ability.

Purpose of the Study:

To propose a hybrid optimization algorithm combining finite state method (FSM) and GA for crude oil scheduling.
To leverage the strengths of both FSM and GA to overcome individual method deficiencies.

Main Methods:

A hybrid algorithm integrating FSM and GA was developed.
FSM was used to enhance GA's local searching ability and guide it towards better solutions.
FSM ensured uniform coverage of the solution space.

Main Results:

The hybrid FSM-GA algorithm demonstrated superior global performance compared to individual GA or FSM.
Simulations on a real-life crude oil scheduling problem validated the proposed method's effectiveness.
The hybrid approach outperformed the state-of-the-art GA method.

Conclusions:

The hybrid FSM-GA algorithm is an effective approach for crude oil scheduling.
Combining FSM and GA offers significant advantages over using either method alone.
This hybrid strategy provides a robust solution for complex scheduling challenges.