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Performance trajectory-based optimised supply chain dynamics

机译：基于绩效轨迹的优化供应链动力学

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This research presents a multi-objective policy design based on simulating system dynamics, a simulation technique capable of explicitly modelling the feedback loops of decision rules and evaluating the dynamics of complex processes and systems. The novel feature of our approach is that performance is not measured by a single value, but rather performance measures are optimised based on their trajectories, such as the degree of inventory oscillation and the amplification ratio between the order rates of two parties over time (e.g. the bullwhip effect). A multi-objective genetic algorithm termed NSGA-II is employed to generate a set of nondominated solutions. In order to demonstrate the performance of our approach, we adapt and evaluate the dynamic method for a well-known case study: the beer game model of a two-stage supply chain.View full textDownload full textKeywordsgenetic algorithm, multi-objective optimisation, performance trajectory, supply chain, system dynamicsRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/09511920903440305
机译：这项研究提出了一种基于模拟系统动力学的多目标策略设计，该仿真技术能够显式地建模决策规则的反馈回路并评估复杂过程和系统的动力学。我们的方法的新颖之处在于，绩效不是通过单个值来衡量的，而是根据绩效的轨迹来优化绩效指标，例如库存波动的程度以及两方订单率之间随时间的放大率（例如牛鞭效应）。使用称为NSGA-II的多目标遗传算法来生成一组非支配解。为了证明我们的方法的有效性，我们针对一个著名的案例研究采用了动态方法，并对其进行了评估：两阶段供应链的啤酒博弈模型。查看全文下载全文关键词遗传算法，多目标优化，性能轨迹，供应链，系统动力学相关变量var addthis_config = {ui_cobrand：“泰勒和弗朗西斯在线”，servicescompact：“ citeulike，netvibes，twitter，technorati，delicious，linkedin，facebook，stumbleupon，digg，google，更多”，pubid：“ ra -4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/09511920903440305

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