An integer programming model for discovering associations between manufacturing system capabilities and product features

Kashkoush, Mohamed; ElMaraghy, Hoda A.

doi:10.1007/s10845-015-1044-6

Cited by 7 publications

(11 citation statements)

References 38 publications

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“…The association rule discovery model [26] employed in this research is a general model that can extract association relationships between any two corresponding groups of data (e.g., input and output parameters of a given manufacturing process). The model was used before by the authors for discovering association relationships between manufacturing system capabilities (manufacturing domain) and product features (product domain).…”

Section: Scopementioning

confidence: 99%

“…This section summarizes the utilized association rule discovery model [26] and illustrates its application in the assembly context. It is an Integer Programming (IP) model consisting of three main sets of constraints.…”

Section: Association Rule Discovery Ip Modelmentioning

confidence: 99%

“…The first example is a group of belt tensioners/idlers adopted from ElMaraghy and AlGeddawy [28] and the second is a new case study of a group of automobile engine short blocks. The purpose of this section is not to verify the used IP model or to compare it with other association rule discovery methods as this has been already done in the corresponding publication where the model first appeared [26].…”

Section: Automotive Industry Applicationsmentioning

confidence: 99%

“…Given the data related to a set of current and/or legacy products and their corresponding assembly systems, an Integer Programming (IP) association rule discovery model [26], recently developed by the authors, is used to reveal association rules between assembly system components and product features. The discovered rules are utilized in synthesizing the assembly system components required for future products containing new combinations of features.…”

Section: Introductionmentioning

confidence: 99%

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Assembly system synthesis using association rule discovery

ElMaraghy

Kashkoush

2015

Int J Adv Manuf Technol

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Assembly is the final process for product realization in which various parts, modules, and sub-assemblies are put together to build a product. Designing an assembly system typically involves many issues such as assembly equipment selection, assembly sequence planning, system layout, task assignments, and line balancing. Assembly system synthesis, specifically the choice of assembly resources/equipment, is examined in this paper. An association rule discovery model is employed to extract association relationships between existing and/or previous products and the systems used to assemble them. The extracted knowledge is used to synthesize assembly systems for new products that fall within the scope of their predecessors. The developed method is demonstrated using two examples from the automotive industry. System synthesis results that are consistent with the used assembly data instances are obtained in both examples. The presented systematic system synthesis method supports the utilization of existing legacy data in developing assembly systems for new product generations. This will lead to a significant reduction of time and effort needed to design assembly systems, particularly in applications that feature frequent design changes and updates, as in the automotive industry.

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Section: Scopementioning

confidence: 99%

Section: Association Rule Discovery Ip Modelmentioning

confidence: 99%

Section: Automotive Industry Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assembly system synthesis using association rule discovery

ElMaraghy

Kashkoush

2015

Int J Adv Manuf Technol

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“…AlGeddawy and ElMaraghy [16] developed a coevolutionary model using cladistics adopted from biology to discover relations between product features and manufacturing capabilities. Kashkoush and ElMaraghy [17,18] proposed an integer programming model for the synthesis of manufacturing and assembly system, respectively. The proposed model was used to reveal relationships between system components and product features.…”

Section: Introductionmentioning

confidence: 99%

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Kou

2018

Mathematical Problems in Engineering

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An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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Association rules mining between service demands and remanufacturing services

Zhou

Xia

Zhang

et al. 2020

AIEDAM

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The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.

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An integer programming model for discovering associations between manufacturing system capabilities and product features

Cited by 7 publications

References 38 publications

Assembly system synthesis using association rule discovery

Assembly system synthesis using association rule discovery

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Association rules mining between service demands and remanufacturing services

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