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A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

Lookup NU author(s): Dr Pupong Pongcharoen, Professor Christian Hicks



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Taylor & Francis, 2019.

For re-use rights please refer to the publisher's terms and conditions.


Capital goods companies produce high value products such as power plant or ships, which have deep and complex product structures, with components having long process routings. Contracts usually include substantial penalties for late delivery. The high value of items can lead to substantial holding costs. Efficient schedules minimise earliness and tardiness costs and need to satisfy assembly and operation precedence constraints as well as finite capacity. This paper presents the first advanced planning and scheduling (APS) tool for the capital goods industry that uses a Discrete Bat Algorithm (DBA), modified DBA (MDBA) and hybrid DBA with Krill Herd algorithm (HDBK) to optimise schedules. The tool was validated using four datasets obtained from a collaborating capital goods company. A sequential experimental strategy was adopted. The first experiment identified appropriate parameter settings for the DBA. The second experiment evaluated and compared the performance of the proposed HDBK algorithm with an Artificial Bee Colony, Krill Herd (KH), Modified KH, DBA and MDBA metaheuristics. The experimental results revealed that the HDBK performed best in terms of the minimum penalty cost for all problem sizes and achieved up to a 47.837% reduction in mean total penalty costs of extra-large problem size.

Publication metadata

Author(s): Chansombat S, Musikapun P, Pongcharoen P, Hicks C

Publication type: Article

Publication status: Published

Journal: International Journal of Production Research

Year: 2019

Volume: 57

Issue: 21

Pages: 6705-6726

Online publication date: 09/05/2018

Acceptance date: 26/04/2018

Date deposited: 27/04/2018

ISSN (print): 0020-7543

ISSN (electronic): 1366-588X

Publisher: Taylor & Francis


DOI: 10.1080/00207543.2018.1471240

Notes: This paper was work conducted in collaboration with colleagues at Naresuan University, Thailand.


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