Title :
A hybrid genetic algorithm for solving the economic lot scheduling problem (ELSP)
Author :
Qiu, Xuan ; Chang, Hui-you
Author_Institution :
Sch. of Software, Sun Yat-sen Univ., Guangzhou, China
Abstract :
In this paper, a hybrid genetic algorithm(HGA) is proposed to solve the ELSP. The ELSP is formulated using basic period(BP) approach. In the proposed HGA, the design of basic setups of GA is largely adopted from previous contributions. Four heuristic strategies are introduced in the proposed HGA, aiming at reaching feasible space easier, testing feasibility and avoiding falling into local optimum. To evaluate the performance of the proposed HGA, we apply it to Bomberger´s classical problem under 88% and 66% utilizations. Our experiments indicate that the proposed HGA outperforms traditional GA in getting minimum total cost and faster convergence rate. More importantly, our proposed HGA is showed to be capable of converging to global optimal solutions.
Keywords :
genetic algorithms; industrial economics; lot sizing; Bomberger classical problem; ELSP; HGA; basic period approach; economic lot scheduling problem; hybrid genetic algorithm; Costs; Cybernetics; Genetic algorithms; Job shop scheduling; Machine learning; Machine learning algorithms; Production; Scheduling algorithm; Software algorithms; Sun; Basic Period; Economic Lot Scheduling Problem; Genetic algorithm; Heuristics;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212265