New Advances and Applications for Marrying Simulation and Optimization

April, Jay; Better, Marco; Glover, Fred; Kelly, James P.

doi:10.1109/wsc.2004.1371305

Cited by 27 publications

(27 citation statements)

References 7 publications

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“…The high degree of variety in processing of different components, in combination with a fluctuating inflow and several resource constraints, make it virtually impossible to plan the production manually in an efficient way. Automatic optimizations are instead needed, and for this purpose a discrete-event simulation model of the line has been developed by simulation experts at the company using the SIMUL8 software package 1 . The simulation model has a front-end interface developed in Excel, which facilitates the user in entering input parameters to the model without the need to learn the simulation language.…”

Section: Real-world Manufacturing Optimizationmentioning

confidence: 99%

“…Discrete-event simulation combined with optimization, so called simulationbased optimization, is a powerful method to improve real-world systems [1]. While traditional, analytical optimization methods have been unable to cope with the challenges imposed by many simulation-based optimization problems in an efficient way, such as multimodality, non-separability and high dimensionality, so called metaheuristic algorithms have been shown to be applicable to this type of problem [2] [3].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimization of a Real-World Manufacturing Process Using Cuckoo Search

Syberfeldt

2013

Cuckoo Search and Firefly Algorithm

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Section: Real-world Manufacturing Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of a Real-World Manufacturing Process Using Cuckoo Search

Syberfeldt

2013

Cuckoo Search and Firefly Algorithm

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“…Motivated by recent advances in real-time simulation tools [5], in general, and wireless networks simulators in particular [6], [7], along-side with the wide success of policy-based management systems for various network operations [8]- [10], we propose a new framework for policy-controlled future adaptive VHO operations that incorporates the advantages of these technologies to continuously achieve optimal VHO decisions. The framework is comprised of two main components, a real-time simulation manager that hosts a run-time network simulator and a policy-based VHO manager, which in-turn hosts the actual VHO process.…”

Section: Introductionmentioning

confidence: 99%

A Policy-Based Simulator for Assisted Adaptive Vertical Handover

Ridhawi

Samaan

Karmouch

2011

2011 IEEE International Symposium on Policies for Distributed Systems and Networks

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This paper proposes a new framework for future policy-based adaptive vertical handover (VHO) in heterogeneous wireless networks. In contrast to existing, statically configured, schemes that lack the ability to quickly adapt to changing network loads, the proposed framework continuously derives optimal configuration policies with respect to handover initiation and network selection. To achieve this goal, the proposed work relies on the interactions between two components: a policy-based real-time simulator and a VHO manager. Triggered by indications of VHO performance degradation, the former enacts a set of VHO scenarios, selected based on a predefined set of simulation policies, to evaluate possible new VHO configurations before actually applying them to the real network. Based on the outcome of the simulation and the monitored network performance, a set of VHO configuration policies is communicated to the VHO manager to reconfigure and/or fine-tune the running VHO scheme. Preliminary performance evaluation results demonstrate the significant enhancement of the outcome of the proposed VHO framework in terms of reducing the ping-pong phenomenon and the handover delay.Index Terms-4G wireless networks; vertical handover; policybased management; run-time simulation.

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“…In these scenarios, simulation-based optimisation is a powerful tool to determine optimal system settings [1]. Simulation-based optimisation (SO) is the process of finding the best values of some parameters for a system, where the performance of the system is evaluated based on the output from a simulation model.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem

Syberfeldt

John

et al. 2009

Robotics and Computer-Integrated Manufacturing

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Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem. 25(6) Robotics and AbstractMany real-world manufacturing problems are too complex to be modelled analytically. For these problems, simulation can be a powerful tool for system analysis and optimisation. While traditional optimisation methods have been unable to cope with the complexities of many problems approached by simulation, evolutionary algorithms have proven to be highly useful. This paper describes how simulation and evolutionary algorithms have been combined to improve a manufacturing cell at Volvo Aero in Sweden. This cell produces hightechnology engine components for civilian and military airplanes, and also for space rockets. Results from the study show that by using simulation and evolutionary algorithms, it is possible to increase the overall utilisation of the cell and at the same time decrease the number of overdue components.

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New Advances and Applications for Marrying Simulation and Optimization

Cited by 27 publications

References 7 publications

Multi-Objective Optimization of a Real-World Manufacturing Process Using Cuckoo Search

Multi-Objective Optimization of a Real-World Manufacturing Process Using Cuckoo Search

A Policy-Based Simulator for Assisted Adaptive Vertical Handover

Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem

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