A web-based platform for the simulation–optimization of industrial problems

Syberfeldt, Anna; Karlsson, Ingemar; Ng, Amos H. C.; Svantesson, Joakim; Almgren, Torgny

doi:10.1016/j.cie.2013.01.008

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…A long term goal, for when the scenarios become larger and more complex, is to be able to deploy optimisation experiments over cluster of clients for parallel simulation. This approach has been proved to enable wider solution search space and also increase the speed at which an optimal solution is reached [12].…”

Section: Discussionmentioning

confidence: 99%

Implementing ManPy, a Semantic-free Open-source Discrete Event Simulation Package, in a Job Shop

et al. 2014

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The cost of acquiring commercial simulation packages is considerably high and this might explain why organisations are often reluctant to make further investments on training and retraining of employees on simulation modelling. Ironically, the level of benefit derived from simulation is highly dependent on experimental, analytical and statistical skills of the user. These cost and skill requirements make simulation an unattractive decision support tool to SMEs and small multinational organisations. Proposed in this study is ManPy, a semantic-free opensource approach to discrete event simulation (DES), such that users with different levels of skills can derive considerable benefits from simulation. ManPy eradicates the high investments required for simulation modelling by making it possible for low skilled users to benefit from readily available generic modelling objects which are contributed to an open source platform by highly skilled simulation practitioners, statisticians and academics. Another benefit of ManPy is the ability to integrate with other enterprise planning tools for system knowledge extraction and real time simulation input data. Some of these benefits are demonstrated through the implementation of ManPy in a SME that specialises in rapid prototyping and rapid tooling.

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Section: Discussionmentioning

confidence: 99%

Implementing ManPy, a Semantic-free Open-source Discrete Event Simulation Package, in a Job Shop

et al. 2014

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“…In this study, a multi-period multi-product multi-objective production planning problem under an uncertain condition was presented. In fact, these kinds of production planning models were represented before by Syberfeldt et al (2013) and Karimi-Nasab and Aryanezhad (2011) but they all studied in a deterministic situation. Hence, considering the uncertainty in this study results the plan which is more realistic.…”

Section: Discussionmentioning

confidence: 99%

“…The use of a simulation in combination with an optimisation algorithm, so called simulation-optimisation, is a commonly used technique in industry today, to find the best parameter values for a system or a process (Syberfeldt et al, 2013). In general, the experimentation demonstrates the utility of the simulation optimisation approach to aid decision making in complex manufacturing environments (Melouk et al, 2013).…”

Section: Simulation-optimisationmentioning

confidence: 99%

Optimal production rate in production planning problem with simulation optimisation approach by simulated annealing

Sajadi

Rad

2016

IJISE

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In manufacturing environments, the production planning is at the high level of production and operation management. This paper intends to plan a flow shop manufacturing system producing multi products during multi periods. By planning, the optimal production rate of each product in every period will be determined. In order to specify the optimal production rate, three objectives must be met simultaneously. Planning is done in a stochastic situation and some uncertain parameters are considered for the problem. The simulation-optimisation approach was used and SA algorithm was chosen as the optimiser. Ultimately, after generating 225 new feasible solutions, the optimal production rate was determined. Results showed the trend of searching new solutions reached to a convergence and the average value of objective function decreased from 222,657 to 56,430 and it has been a remarkable reduction. So, the simulation optimisation approach is capable in solving uncertain production planning model even by considering multi objectives simultaneously.

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“…Such questions are concerning the applicability of programming technics [1][2], and technologies, the security [3], and the performance [4][5] [6]. Few examples from the myriad are in the references, they deal with water quality, industrial applications [7] [8]. Further important questions are rising in connection with the computer science background [5] [9][10]of them.…”

Section: Introductionmentioning

confidence: 99%