A generalised framework for simulation-based decision support for manufacturing

AlDurgham, Mohammed M.; Barghash, Mahmoud A.

doi:10.1080/09537280802187626

Cited by 27 publications

(16 citation statements)

References 21 publications

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“…(3) Another human performance model considers age and circadian rhythm effects on productivity [5]. (4) The approach presented in [6] allows the evaluation of so-called 'soft' human factors in terms of 'hard', quantified, productivity levels to address 'high-level' factors such as autonomy at work or accommodation of injured or otherwise reduced-capacity operators.…”

Section: B Humans In Simulation Modelsmentioning

confidence: 99%

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Integrating human behaviour into factory simulation- a feasibility study

Riedel

Mueller

Weth

et al. 2009

2009 IEEE International Conference on Industrial Engineering and Engineering Management

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As the environment of manufacturing enterprises is becoming more complex and dynamic this requires powerful tools for the support of decisions in manufacturing and production planning. Simulation as one of those tools has been widely applied in manufacturing and logistics. However, the models used for simulation are incomplete because the human factor in terms of its decision making and behaviour is considered only rudimentary. This leads to suboptimal decisions and to a gap when implementing solutions in practice. The paper presents an approach for enriching the common (technical) simulation models with need controlled human agents. The agent is based on a formalised motivation theory. In a feasibility study several typical settings, like ramp up curves and learning effects, were tested. The approach is unique in the field of simulation with a big potential for higher quality in analysing and designing production systems.

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Section: B Humans In Simulation Modelsmentioning

confidence: 99%

“…The role that simulation plays has been increasing due to the increase of computational power accompanied by cost reduction [2,4]. Discrete event simulation quantitatively represents the real world, simulates its dynamics on an event-by-event basis, and generates detailed performance reports [1].…”

Section: A Simulationmentioning

confidence: 99%

Integrating human behaviour into factory simulation- a feasibility study

Riedel

Mueller

Weth

et al. 2009

2009 IEEE International Conference on Industrial Engineering and Engineering Management

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“…Corrections of products in the plans were made according to the indicators of cost effectivness (8) and correction (9)…”

Section: Selection Of Models For Testingmentioning

confidence: 99%

“…d. simulation as a model of production optimization in cases of make-to-order production and in conditions of instability ([5], [6], [7], [8], [9]) e. optimization in make-to-order production with selection of orders according to deadlines and capacities ( [10]) f. production plans optimization model with the application of the simulation method to include additional orders into the plans and to synchronize the sales and production ( [11]) It can be also stated that there is virtually no operational planning and production control model that can be generalized for a larger number of conditions and different production types and it is therefore very difficult to compare precisely the effectiveness of actual models of operational planning and production control.…”

Section: Introductionmentioning

confidence: 99%

Model of Adaptable Production Planning and Control

Gordic¹

2014

SJBM

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In research of a model of production planning and control that can adapt to changes, disturbances and risks a model of adaptable optimization, called discrete corrective dynamizing optimization, was created. The model is created on the basis of dynamic programming to which is added the model of corrective optimization by simulation with the criteria defined in the initial and corrective part of the optimization. The effectiveness of a model of discrete corrective dynamizing programming was tested in relation to three other models of production programming. Testing has shown that the smallest deviations of the product quantities were obtained by applying the model of discrete corrective dynamizing optimization. It was also shown that the difference in the realized profit rate as the optimization criterion in relation to actual results was negligible in all testing conditions-variants. This is also a proof that with the use of corrective optimization a possible optimum can be achieved, with maximum adjustment to changes.

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“…In these studies the ESO tool is used in order to find the optimal operation strategy, improve energy management systems and investigate how different boundary conditions, such as changes in electricity and fuel prices, influence the system. Simulation in general and DES tool in particular has been widely used in many manufacturing applications and have proven to be an excellent decision support tool for manufacturing system applications [16]. For example, Kursun & Kalaoglu [17] eliminated the bottlenecks and suggested different decision alternatives in apparel manufacturing using the DES tool.…”

Section: Introductionmentioning

confidence: 99%

Combining optimisation and simulation in an energy systems analysis of a Swedish iron foundry

Mardan

Klahr

2012

Energy

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To face global competition, and also reduce environmental and climate impact, industrywide changes are needed, especially regarding energy use, which is closely related to global warming. Energy efficiency is therefore an essential task for the future as it has a significant impact on both business profits and the environment. For the analysis of possible changes in industrial production processes, and to choose what changes should be made, various modelling tools can be used as a decision support. This paper uses two types of energy analysis tool: Discrete Event Simulation (DES) and Energy Systems Optimisation (ESO). The aim of this study is to describe how a DES and an ESO tool can be combined. A comprehensive five-step approach is proposed for reducing system costs and making a more robust production system. A case study representing a new investment in part of a Swedish iron foundry is also included to illustrate the method's use. The method described in this paper is based on the use of the DES program QUEST and the ESO tool reMIND. The method combination itself is generic, i.e. other similar programs can be used as well with some adjustments and adaptations.The results from the case study show that when different boundary conditions are used the result obtained from the simulation tools is not optimum, in other words, the result 2 shows only a feasible solution and not the best way to run the factory. It is therefore important to use the optimisation tool in such cases in order to obtain the optimum operating strategy. By using the optimisation tool a substantial amount of resources can be saved. The results also show that the combination of optimisation and simulation tools is useful to provide very detailed information about how the system works and to predict system behaviour as well as to minimise the system cost.

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A generalised framework for simulation-based decision support for manufacturing

Cited by 27 publications

References 21 publications

Integrating human behaviour into factory simulation- a feasibility study

Integrating human behaviour into factory simulation- a feasibility study

Model of Adaptable Production Planning and Control

Combining optimisation and simulation in an energy systems analysis of a Swedish iron foundry

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