Data mining in manufacturing: a review based on the kind of knowledge

Choudhary, Anshul; Harding, Jennifer; Tiwari, Manoj Kumar

doi:10.1007/s10845-008-0145-x

Cited by 415 publications

(189 citation statements)

References 136 publications

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“…Table 1 lists a few of the selected works that employed learning in scheduling. For detailed reviews on the subject, refer to [14,22,23] and an updated review by [24]. …”

Section: Learning In Shop Scheduling: a Concise Reviewmentioning

confidence: 99%

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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Abstract:A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

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“…Table 1 lists a few of the selected works that employed learning in scheduling. For detailed reviews on the subject, refer to [14,22,23] and an updated review by [24]. …”

Section: Learning In Shop Scheduling: a Concise Reviewmentioning

confidence: 99%

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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“…However, applying data mining methods is less frequently done in manufacturing environments than in other areas such as finance or business. Several authors also point out that the use of accumulated data in manufacturing firms has been very limited, although the collected data embody valuable insights and knowledge [29,30]. Many studies in recycling and remanufacturing that have employed historical data mostly focus on forecasting the expected outcome using statistical analyses [31,32].…”

Section: Reuse and Reprocessing Of Drossmentioning

confidence: 99%

Data Mining Toward Increased Use of Aluminum Dross

Chang

Olivetti

Fjeldbo³

et al. 2015

J. Sustain. Metall.

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Recycling provides a key strategy to move toward a more sustainable society by partially mitigating the impact of fast-growing material consumption. One main barrier to increased recycling arises from the fact that in many real world contexts, the quality of secondary (or scrap) material is unknown and highly variable. Even if scrap material is of known quality, there may be finite space or limited operational flexibility to separate or sort these materials prior to use. These issues around identification and grouping given the operational constraints create limitations to simply developing an appropriate sorting strategy, let alone implementing one. This study suggests the use of data mining as a strategy to manage raw materials with uncertain quality using existing data from the recycling industry. A clustering analysis is used to recognize the pattern of raw materials across a broad compositional range in order to provide criteria for grouping (binning) raw materials. This strategy is applied to an industrial case of aluminum recycling to explore the benefits and limitations in terms of secondary material usage. In particular, the case investigated is around recycling industrial byproducts (termed dross for the case of the aluminum industry). The binning strategy obtained by the clustering analysis can significantly reduce material cost by increasing the compositional homogeneity and distinctiveness of uncertain raw materials. This result suggests the potential opportunity to increase low-quality secondary raw material usage before investment in expensive sorting technology.

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“…More detailed reviews of data mining research are given by Choudhary et al (2009), Wang (2007 and Harding et al (2006). Choudhary et al (2009a) consider the use of text mining applications to extract knowledge from post-project reviews.…”

Section: Knowledge Discovery and Data Miningmentioning

confidence: 99%