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- Andersson, Marcus, et al.
(författare)
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A web-based simulation optimization system for industrial scheduling
- 2007
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Ingår i: Proceedings of the 39th conference on Winter simulation. - : IEEE Press. - 1424413060 ; , s. 1844-1852
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Konferensbidrag (refereegranskat)abstract
- Many real-world production systems are complex in nature and it is a real challenge to find an efficient scheduling method that satisfies the production requirements as well as utilizes the resources efficiently. Tools like discrete event simulation (DES) are very useful for modeling these systems and can be used to test and compare different schedules before dispatching the best schedules to the targeted systems. DES alone, however, cannot be used to find the "optimal" schedule. Simulation-based optimization (SO) can be used to search for optimal schedules efficiently without too much user intervention. Observing that long computing time may prohibit the interest in using SO for industrial scheduling, various techniques to speed up the SO process have to be explored. This paper presents a case study that shows the use of a Web-based parallel and distributed SO platform to support the operations scheduling of a machining line in an automotive factory.
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| 2. |
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| 3. |
- Andersson, Marcus, et al.
(författare)
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Simulation Optimization for Industrial Scheduling Using Hybrid Genetic Representation
- 2008
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Ingår i: Proceedings of the 2008 Winter Simulation Conference. - : IEEE conference proceedings. - 9781424427086 ; , s. 2004-2011
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Konferensbidrag (refereegranskat)abstract
- Simulation modeling has the capability to represent complex real-world systems in details and therefore it is suitable to develop simulation models for generating detailed operation plans to control the shop floor. In the literature, there are two major approaches for tackling the simulation-based scheduling problems, namely dispatching rules and using meta-heuristic search algorithms. The purpose of this paper is to illustrate that there are advantages when these two approaches are combined. More precisely, this paper introduces a novel hybrid genetic representation as a combination of both a partially completed schedule (direct) and the optimal dispatching rules (indirect), for setting the schedules for some critical stages (e.g. bottlenecks) and other non-critical stages respectively. When applied to an industrial case study, this hybrid method has been found to outperform the two common approaches, in terms of finding reasonably good solutions within a shorter time period for most of the complex scheduling scenarios.
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| 6. |
- Ng, Amos, et al.
(författare)
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OPTIMISE : An Internet-Based Platform for Metamodel-Assisted Simulation Optimization
- 2008
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Ingår i: Advances in Communication Systems and Electrical Engineering. - Boston, MA : Springer Science+Business Media B.V.. - 9780387749372 - 9780387749389 ; , s. 281-296
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Bokkapitel (refereegranskat)abstract
- Computer simulation has been described as the most effective tool for de-signing and analyzing systems in general and discrete-event systems (e.g., production or logistic systems) in particular (De Vin et al. 2004). Historically, the main disadvantage of simulation is that it was not a real optimization tool. Recently, research efforts have been focused on integrating metaheuristic algorithms, such as genetic algorithms (GA) with simulation software so that “optimal” or close to optimal solutions can be found automatically. An optimal solution here means the setting of a set of controllable design variables (also known as decision variables) that can minimize or maximize an objective function. This approach is called simulation optimization or simulation-based optimization (SBO), which is perhaps the most important new simulation technology in the last few years (Law and McComas 2002). In contrast to other optimization problems, it is assumed that the objective function in an SBO problem cannot be evaluated analytically but have to be estimated through deterministic/ stochastic simulation.
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| 10. |
- Persson, Anna, et al.
(författare)
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Metamodel-assisted Global Search Using a Probing Technique
- 2007
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Ingår i: The IAENG International Conference on Artificial Intelligence and Applications (ICAIA'07). - : International Association of Engineers. - 9789889867140 ; , s. 83-88
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Konferensbidrag (refereegranskat)abstract
- This paper presents a new metamodel-assisted metaheuristic algorithm for optimisation problems involving computationally expensive simulations. The algorithm, called Global Probing Search, is a population-based algorithm designed for global optimisation. The main idea of the algorithm is to introduce a probing phase in the creating of the new generation of the population. In this probing phase, a large number of candidate solutions are generated and a computationally cheap metamodel function is used for choosing the most promising candidates to transfer to the next generation. This approach could significantly enhance the efficiency of the optimisation process by avoiding wasting valuable evaluation time on solutions that are likely to be inferior. During the optimisation, the accuracy of the metamodel is constantly improved through on-line updating. The proposed algorithm is implemented on a real-world optimisation problem and initial results indicate that the algorithm show good performance in comparison with a standard Genetic Algorithm and an existing metamodel-assisted metaheuristic.
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