2018
DOI: 10.1007/s10100-018-0548-5
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Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case study

Abstract: This paper describes a case study in an automobile assembly plant about a facility layout problem (FLP), where several cells have to be located in an industrial plant of reduced dimensions. The main objective was to support the decision-making process for managers. These cells are in charge of sorting and sequencing parts and components in trolleys to be delivered to the final assembly line. Each cell has an inbound and outbound logistic associated, which generates hundreds of material handling equipment (MHE)… Show more

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Cited by 6 publications
(3 citation statements)
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References 31 publications
(35 reference statements)
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“…Simulation techniques can be used as a decision support method for process improvement of intermittent production systems [23]. A hybrid approach of discrete event simulation integrated with location search algorithm was used to solve a cells assignment problem in an assembly facility [24]. An ontology-driven, component-based framework shows the application of Jellyfish-type simulation models [25].…”
Section: Literature Reviewmentioning
confidence: 99%
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“…Simulation techniques can be used as a decision support method for process improvement of intermittent production systems [23]. A hybrid approach of discrete event simulation integrated with location search algorithm was used to solve a cells assignment problem in an assembly facility [24]. An ontology-driven, component-based framework shows the application of Jellyfish-type simulation models [25].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Rosin et al, 2020 [1] Application of principles and tools of I4.0 in lean management Skapinyecz et al, 2018 [2] Optimal selection of logistics service providers in Industry 4.0 Tchoffa et al, 2019 [3] Extension of federated interoperability framework in I4.0 Alcácer et al, 2019 [4] Information and communication technologies in I4.0 Dastjerdi et al, 2016 [5] Impact of fog computing on IoT solutions Huang et al, 2013 [6] Additive manufacturing and sustainability Wu et al, 2010 [7] Magnetic magnesium for data storage in gentelligent products Guo et al, 2019 [8] Modular based flexible digital twin for factory design Tao et al, 2018 [9] Digital twin-enabled product design, manufacturing, and service Ding et al, 2019 [10] Digital twin-based cyber-physical production system Cui et al, 2020 [11] Big data applications Schahinian, 2020 [12] Concept of matrix production Bányai et al, 2019 [13] Real time optimization of matrix production systems Azarm et al, 1991 [14] Production priorities in the heuristic optimization of rough-mill yield Kops et al, 1994 [15] Optimum allocation of jobs on machine tools Hidaka et al, 1997 [16] Facility location for large-scale logistics using heuristics Chitsaz et al, 2019 [17] Joint optimization of production and distribution Eydi et al, 2020 [18] Decision making for supplier and carrier selection Feng et al, 2018 [19] Integrated production and transportation planning Ghomi et al, 2019 [20] Optimization in cloud manufacturing Sadati et al, 2018 [21] Identification of significant control variables in manufacturing Haberer et al, 2016 [22] Optimization of a crawler track unit Tamás, 2017 [23] Simulation-enabled decision making in manufacturing processes Saez-Mas et al, 2020 [24] Hybrid approach for cell assignment problems Bohács et al, 2017 [25] Ontology-driven framework for Jellyfish-type simulation Ghomi et al, 2019 [26] Optimization of queueing problems in cloud manufacturing Hong et al, 2018 [27] Multi-stage supply chain optimization Khalilpourazari et al, 2019 [28] Analysis of impact of defective supply batches…”
Section: Cyberphysicalmentioning
confidence: 99%
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