Mixed neighbourhood local search for customer order scheduling problem
File version
Author(s)
Polash, MMA
Newton, MA Hakim
Sattar, Abdul
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Geng, X
Kang, BH
Date
Size
File type(s)
Location
Nanjing, PEOPLES R CHINA
License
Abstract
Customer Order Scheduling Problem (COSP) is an NP-Hard problem that has important practical applications e.g., in the paper industry and the pharmaceutical industry. The existing algorithms to solve COSP still either find low quality solutions or scramble with large-sized problems. In this paper, we propose a new constructive heuristic called repair-based mechanism (RBM) that outperforms the best-known heuristics in the literature. We also propose a mixed neighbourhood local search (MNLS) algorithm. MNLS embeds a number of move operators to diversify the local exploitation making different areas around the current solution accessible. Moreover, we also propose a greedy diversification method to keep the search focussed even when it is in a plateau. Our experimental results on 960 well-known problem instances indicate statistically significant improvement obtained by the proposed MNLS over existing state-of-the-art algorithms. MNLS has found new best solutions for 721 out of the 960 problem instances.
Journal Title
Conference Title
PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I
Book Title
Edition
Volume
11012
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
© 2018 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), volume 11012, PRICAI 2018: PRICAI 2018: Trends in Artificial Intelligence pp 296-309. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.
Item Access Status
Note
Access the data
Related item(s)
Subject
Artificial intelligence