Lookup NU author(s): Dr Pupong Pongcharoen,
Professor Christian Hicks
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
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.
Author(s): Chansombat S, Musikapun P, Pongcharoen P, Hicks C
Publication type: Article
Publication status: Published
Journal: International Journal of Production Research
Issue: ePub ahead of Print
Online publication date: 09/05/2018
Acceptance date: 26/04/2018
ISSN (print): 0020-7543
ISSN (electronic): 1366-588X
Publisher: Taylor & Francis
Notes: This paper was work conducted in collaboration with colleagues at Naresuan University, Thailand.
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