GA-based decision support systems for precast production planning

Ko, Chien–Ho; Wang, Shufan

doi:10.1016/j.autcon.2010.06.004

Cited by 76 publications

(42 citation statements)

References 24 publications

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“…Although production planning and control systems have been widely investigated by researchers [13][14][15], there is still much scope to develop customized systems that are tailored for the unique conditions of the construction industry [16,17]. Traditional techniques for construction planning and control are only able to manage stationary bottlenecks.…”

Section: Research Backgroundmentioning

confidence: 99%

Autonomous production tracking for augmenting output in off-site construction

Arashpour

Wakefield

Blismas

et al. 2015

Automation in Construction

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Problems in existing methods of production tracking in off-site construction result in schedule delays and increased costs. To eliminate these deficiencies, an autonomous production tracking that analyzes real-time production data is proposed. A specific implementation of the proposed production tracking mechanisms has been developed for a large off-site construction plant in Australia, and is in the process of installation. The paper shows that: (i) The production model in off-site construction is always nonlinear in the outcome due to the presence of variability (ii) in systems with a periodic production target, deviation from the schedule converges to zero at the end of production period and the same downward trend should be followed in designing plan buffers and (iii) long-term production performance in off-site construction can autonomously be monitored and controlled by observing critical variables of production. The paper provides those who manage off-site construction with recommendations on effective production tracking and management. The models and propositions in this research are of practical value and can be used to detect impending production shortfalls against periodic targets in the short-term, and adjust capacity parameters and production targets in long-term planning.

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Section: Research Backgroundmentioning

confidence: 99%

Autonomous production tracking for augmenting output in off-site construction

Arashpour

Wakefield

Blismas

et al. 2015

Automation in Construction

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“…DSS in joint with integrated, genetic algorithms can experience behavioral changes and adapt over time in order to provide improved decision support based on previous experience. In this sort of instance, various GA-based decision support systems are developed as a genetic algorithm is being integrated (Ko and Wang 2010;Juan et al 2009). …”

Section: Ga-based Decision Support Systemsmentioning

confidence: 99%

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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“…Poor production control may cause serious problems to a firm's ability to meet production requirements and constraints. Many researches have focused on developing DSS tools to face this problem (e.g., Ko and Wang 2010;Caricato and Grieco 2009;Mok 2009;Farrella and Maness 2005;McKay and Wiers 2003). These tools are concerned as complementary applications to the ERP/MRP software.…”

Section: Literature Reviewmentioning

confidence: 99%

An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case

Saeed

Bashiri

2013

J Ind Eng Int

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Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be inefficient because of daily fluctuations in real factories. Decision support systems can provide productive tools for production planners to offer a feasible and prompt decision in effective and robust production planning. In this paper, we propose a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression. The proposed approach has been used in a real case in Iranian automotive industry. In the presence of existing multisource uncertainties, the results of applying the proposed method in the selected case show that the accuracy of daily production planning increases in comparison with the existing method.

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GA-based decision support systems for precast production planning

Cited by 76 publications

References 24 publications

Autonomous production tracking for augmenting output in off-site construction

Autonomous production tracking for augmenting output in off-site construction

Intelligent Decision Support Systems

An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case

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