Job and tool group scheduling for a machining center

Mauergauz, Yuri

doi:10.1080/17509653.2016.1255858

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…The SA with multiple starts outperforms the conventional SA at each generation. Mauergauz (2017) presents a multi-objective approach for the tool switching problem with tool wear and different fixture types and job due dates. The article will be discussed in the following section due to its specific tool wear characteristic.…”

Section: Multi-objective Approachesmentioning

confidence: 99%

“…The approaches with tool wear assume either deterministic Dadashi, Moslemi, and Mirzazadeh 2016;Mauergauz 2017) or stochastic tool lifetimes (Hirvikorpi et al 2007;Farughi et al (2017). The Weibull distribution is used for both studies with stochastic tool life.…”

Section: Approaches With Tool Wearmentioning

confidence: 99%

“…The tool is sent for maintenance once the tool wear limit is reached. Mauergauz (2017) also integrates different fixture types and job due dates. A multi-criteria dynamic grouping method is proposed based on the set-up expenditure and the average orders utility.…”

Section: Approaches With Tool Wearmentioning

confidence: 99%

“…Their variation is based on the fact that, in FMSs, required tools that are not in the magazine will be transferred from a tool crib to the magazine and back by an automatic tool transporter that can transport a certain number of tools. They present an INLP for this GA Hirvikorpi et al (2007) 1 ST si SO Tool hom L k ,Seq k |T j |≤C TW yes SUM-policy Dadashi, Moslemi, and Mirzazadeh (2016) 1 ST si SO Tool hom L k ,Seq u |T j |≤C TW yes MILP/GA Farughi et al (2017) 1ST si SO Tool hom L k ,Seq u |T j |≤C TW yes NLP/GA Mauergauz (2017) 1ST si MO Tool hom L k ,Seq u |T j |≤C TW yes greedy…”

Section: Other Researchmentioning

confidence: 99%

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The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

Calmels

2018

International Journal of Production Research

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The job sequencing and tool switching problem is a combinatorial optimisation problem that appears in various industries, mainly in the manufacturing sector. Although tool switching is only part of a much larger decision-making process in manufacturing systems, it has a major impact on the overall performance of the system by affecting the total set-up time as well as the machine and tool utilisation. Over the past few decades, various approaches have been applied to tool switching problems. This study provides a comprehensive review of the literature on the job sequencing and tool switching problem. Studies are identified and compared through a structured literature review and classified according to a new framework which allows the identification of problem perspectives and solution methods. The results show that current research does not fully exploit real-world situations and studies are often limited to a single-machine and uniform tool size and set-up time. Finally, this literature review summarises the current research results and provides directions for future research.. comparatively review, in an organised fashion, the existing unclassified tool switching literature with respect to problem formulation development and applied solution methods, . develop a classification scheme that may help to identify aspects of the tool switching problem that have been insufficiently explored and show how far the existing modelling efforts have progressed towards real-world needs, . synthesise the past trends of tool switching studies, and . offer some suggestions and directions for future research.The remainder of this paper is structured as follows. Section 2 introduces the generic tool switching problem, its subproblems, and related optimisation problems. Subsequently, the research approach is described in section 3, and the classification framework is described in section 4. The literature review is presented in section 5. Section 6 outlines the research gaps and implications for future research. Section 7 concludes the paper with a short summary.

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Section: Multi-objective Approachesmentioning

confidence: 99%

Section: Approaches With Tool Wearmentioning

confidence: 99%

Section: Approaches With Tool Wearmentioning

confidence: 99%

Section: Other Researchmentioning

confidence: 99%

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