Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207543.2010.527388
Title: Aggregate production planning for shipbuilding with variation-inventory trade-offs
Authors: Liu, Z. 
Chua, D.K.H. 
Yeoh, K.-W.
Keywords: aggregate production planning
genetic algorithm
multi-objective optimisation
shipbuilding
Issue Date: 15-Oct-2011
Source: Liu, Z., Chua, D.K.H., Yeoh, K.-W. (2011-10-15). Aggregate production planning for shipbuilding with variation-inventory trade-offs. International Journal of Production Research 49 (20) : 6249-6272. ScholarBank@NUS Repository. https://doi.org/10.1080/00207543.2010.527388
Abstract: Shipbuilding is a complex production system characterised by a complicated work and organisation structure, prolonged production lead time, and heterogeneous resource requirements. Thus, effectively planning all involved activities presents a challenging task and requires the timely coordination between the successive production stages at the plant level and effective resource allocation at the workshop level. With the work breakdown structure of all projects and their corresponding building strategies, the aggregate production planning (APP) is to address two important issues, namely, workforce level and inventory usage so that the fluctuating demands from downstream processes can be satisfied in a cost-effective manner. To achieve this, a novel APP model is proposed for ship production to minimise the variation of aggregate man-hour over the planning horizon and simultaneously minimise the logistic demands of the interim products. In view of the combinatorial nature and computational complexity, a directed genetic algorithm based solver has been developed to solve the two-conflicting-objective optimisation problem. The proposed approach has been applied to a case study and preliminary results have shown certain effectiveness in handling various situations with different planning strategies. © 2011 Taylor & Francis.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/58938
ISSN: 00207543
DOI: 10.1080/00207543.2010.527388
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