Modelling methodology for the simulation of the manufacturing systems

Tajini, Reda; Elhaq, Saâd Lissane; Rachid, A.

doi:10.1504/ijspm.2014.066372

Cited by 7 publications

(5 citation statements)

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“…Thus, the need to provide companies with methods capable of improving the performance of their warehouses arises. Some of these methods include simulation, analytical methods and benchmarking, the former being the most used whether in literature or in practice in problems related to logistics (Gu et al, 2010, Bottani et al, 2014, despite some of its flaws that can be identified, as Tajini et al (2014) did.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rather than individually validating models, this process should focus on the generator. Therefore, by using these generators, a reduction in the validation process complexity, durability and subjectivity is expected (Popovics et al, 2016, Tajini et al, 2014. In addition, more time would be available for more addedvalue tasks, such as results analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

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Simulation model generation for warehouse management: case study to test different storage strategies

Vieira

Dias

Pereira

et al. 2018

IJSPM

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A simulation model generator was developed to help a company of the Bosch Group to reduce costs in time and space with its warehouse. Particularly, the automatically created simulation models can simulate pickers riding milk runs to collect containers from the warehouse, to satisfy the needs of production lines, enabling warehouse management by testing different storage strategies. Thus, the purpose of this paper is to present the developed generator and to use it in a case study, to test different storage policies for the organization. The generator was validated, as was the simulation model automatically created. With this tool, some suggestions could be made to the organization in question. Namely, it was shown that it would not be possible to maintain the FIFO rule and, at the same time, eliminate the upper floor of all racks for ergonomic reasons. To allow this, the rate of replacing containers should be synchronized with the needs of production lines. Alternatively, it was also shown that the performance of the warehouse would improve by dividing it in zones affected for each milk run.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Simulation model generation for warehouse management: case study to test different storage strategies

Vieira

Dias

Pereira

et al. 2018

IJSPM

View full text Add to dashboard Cite

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“…Manufacture problems were classical and already intensively studied in the litterature [DY00, LMN12,TER14]. This problem consists on a set of thousand processes which are executed on several batches of products.…”

Section: A Manufacture Chainmentioning

confidence: 99%

DEv-PROMELA: an extension of PROMELA for the modelling, simulation and verification of discrete-event systems

Yacoub

Hamri

Frydman

et al. 2017

IJSPM

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PROMELA is a well-known formalism for the modeling and the verification of concurrent systems. PROMELA deals with high-level specifications. As a result, PROMELA models are expressed in a high-level abstraction which not considers explicit representation of time or events for example. But, the efficiency of the processes of Verification and Validation relies on the accuracy of the models. That is why we propose in this paper work to develop a new extension of PROMELA for the modeling of discrete-event systems. The verification of these models is then done by combining formal verification and simulation-based verification using SPIN and the tool DEv-PROMELA Studio, or using any existing DEVS simulators.

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“…A typical flexible manufacturing system configuration is chosen for detailed study and analysis. Ramaswami and Jeyakumar (2014) studied non-Markovian bulk queueing system with state dependent arrivals and multiple vacations using a simulation approach, Korytkowski and Wisniewski (2012) examined a multi-product production systems with in-line quality control, Rad et al (2014) gave an analysis of a manufacturing system using simulation and multi-criteria decision-making tools were applied, Hasan et al (2014) considered reconfigurable manufacturing systems to be one of the newer technologies which cannot only meet stochastic product demand but can also produce products having customised variety, Tajini et al (2014) developed a flexible modelling environment for the simulation and analysis of different production systems, Boualem et al (2015) focused on flexible production system modelled by re-entrant queueing network, where several performance measures have been investigated through expanded Monte Carlo simulations.…”

Section: Introductionmentioning

confidence: 99%