Optimal Production Ramp-Up in the Smartphone Manufacturing Industry

发布者:周瑾茹发布时间:2019-05-14浏览次数:10

主讲人:邓天虎副教授

时间:2019年5月18日上午10:00

地点:管工学院524会议室


主讲人简介:

  邓天虎,清华大学工业工程系副教授,国家自然基金委优秀青年基金获得者,国际运筹与管理科学学会Franz Edelman Laureates。兼任中国运筹学会随机服务与运作管理分会青年理事(2014年至今)和中国运筹学会随机服务与运作管理分会青年理事(2014年至今)。研究领域包括天然气管道运输优化和供应链管理。获得中国运筹学会随机服务与运作管理分会优秀青年学者奖(2017)和北京市运筹学学会2016年青年优秀论文奖。负责执行的中石油天然气管网优化项目入围INFORMS设立的管理科学应用界最高奖项弗兰茨·厄德曼奖 (Franz Edelman Award)2018年决赛。主持国家自然科学基金的优秀青年基金项目1项,海军军队项目2项,参与完成国家重点自然科学基金1项。研究成果发表于Operations Research、Manufacturing & Service Operations Management以及Interfaces等国际学术期刊。


摘要:

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production rampup model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.