Aggregate production planning for shipbuilding with variation-inventory trade-offs

Liang, Zhuo; Chua, David K. H.; Yeoh, K. W.

doi:10.1080/00207543.2010.527388

Cited by 33 publications

(25 citation statements)

References 24 publications

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“…After sales forecasts and resource requirements, the various alternate production plans are generated Liu et al, 2011;Mirapour et al, 2011;Li et al, 2013). Additionally, SCA provides useful insights to problems related to operations scheduling problems, which can be formulated as mixed integer linear programming problems (Wang and Lei, 2012;.…”

Section: Productionmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,115

750

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The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) -that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

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Section: Productionmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,115

750

View full text Add to dashboard Cite

show abstract

“…Lastly, the NSGA-II has been used to effectively solve a variety of complex optimisation problems, including those of the maritime industry. For example, this algorithm has been used to automatically generate general arrangements of complex ships (van Oers and Hopman 2010), perform aggregate production planning in shipbuilding (Liu, Chua, and Yeoh 2011), develop a decision support system for bulk material handling ports (Pratap, Nayak et al 2015), schedule joint production and maintenance (Berrichi et al 2010), solve the generation expansion planning problem (Murugan, Kannan, and Baskar 2009) and for hydro-thermal power scheduling (Deb and Karthik 2007).…”

Section: Methodsmentioning

confidence: 99%

Automatic generation of a section building planning for constructing complex ships in European shipyards

Rose

Coenen

2016

International Journal of Production Research

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Efficient planning of the section building process is important for European shipyards since delays in this process can disrupt the on-time delivery of a ship. Automatically generating production schedules of the section building process can result in higher quality schedules compared to those created manually. Recently, the production processes of European shipyards have shifted to focus heavily on outsourcing and outfitting, yet existing automatic planning methods for section building fail to sufficiently consider these factors. This paper develops a mathematical model of the section building process which includes the effects of outfitting and outsourcing. The objective of this model is to simultaneously minimise the fluctuations in workload and the number of outsourced man-hours. The mathematical model was solved by implementing the non-dominated sorting generic algorithm-II (NSGA-II) using a custom heuristic as the fitness function. Due to the multi-objective nature of the problem definition and solution approach, a Pareto front of optimal solutions is created instead of a single, best solution. A test case showed that gains in both objectives are achievable compared to the planning developed manually. Implementing the Section Building Planning methodology developed in this paper could potentially improve the efficiency and controllability of the overall shipbuilding process.

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“…This mixed strategy is typically formulated as cost-driven optimization model determining the production amounts, inventory, and human and equipment resources simultaneously (cf. Buxey (2005), Liu et al (2011), Nam and Logendran (1992)). Modeling techniques and solution methods for APP have a long tradition in academia.…”

mentioning

confidence: 96%

“…Hsieh et al(2000), for instance, propose a possibilistic linear programming model for APP. See Liu et al (2011) for a more comprehensive list of APP techniques. The issue of disaggregation, i.e.…”

mentioning

confidence: 99%

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Aggregate planning and forecasting in make-to-order production systems

Gansterer

2015

International Journal of Production Economics

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Aggregate production planning for shipbuilding with variation-inventory trade-offs

Cited by 33 publications

References 24 publications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Automatic generation of a section building planning for constructing complex ships in European shipyards

Aggregate planning and forecasting in make-to-order production systems

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