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发布日期:2020-07-11 浏览次数:213 作者: 编辑:

主题:workshop of Management of Science and Engineering

时间:2020-07-16 14:00-16:00

地点:腾讯会议:719 476 937




Title:A General Inverse DEA Model for Non-radial DEA


Abstract: Traditional inverse DEA models could be called inverse radial DEA because they are based on radial efficiency measures. Due to the neglect of slacks in evaluating the efficiency score, inverse radial DEA may mislead decision-making in some cases where slacks play important roles. In this paper, we proposed an integrated framework of inverse DEA called inverse non-radial DEA since it is based on non-radial DEA by multi-objective programming, which covers existing inverse DEA models. To further illustrate the inverse non-radial DEA, we construct the concrete mathematical formula of inverse SBM and some properties. In contrast to the radial approach, inverse non-radial DEA can overcome the error caused by ignoring slacks and provides more valuable information about inputs and outputs for decision-making by considering slacks. Although inverse non-radial DEA models are usually non-linear, we can convert it into a one-dimensional search problem about efficiency score, which can be solved by many existing efficient algorithms. A practical example is provided to demonstrate the advantages of inverse non-radial DEA models over inverse radial DEA models.


Title:Target-oriented Robust Location-Transportation Problem


Abstract: This research studies a target-oriented, multi-period location-transportation problem under uncertain customer demands. The objective is to determine the location of facilities, their production quantities, and the amount of shipment from each facility to each customer to achieve the target profit or the desired fill rate. To strike a balance between sales profit and customer satisfaction, the target profit and desired fill rate are imposed as the objective or a constraint respectively in two different models. Additionally, to improve the performance of our solution methodology, an affine decision rule is incorporated into the model to adapt the solutions to realized scenarios. We develop our solution methodology based on a distributionally robust optimization framework and derive tractable conservative approximations of the models for more effective solutions and efficient computations. A Benders decomposition approach is developed to speed up computations for large-scale instances. Finally, the performance of the solution methodology is demonstrated through computational experiments.


Title:Pricing and entry strategies for competitive firms with managerially optimistic entrant


Abstract: The entrepreneurs are usually optimistic when making business decisions, especially when entering an existing market. We introduce the entrant’s optimism into the game models of entry deterrence to study the entry strategy of the potential entrant and the pricing (deterrence) strategy of the incumbent. Further, we examine the impacts of the entrant’s optimism on the optimal decisions for both firms in the post-entry game (Stackelberg price competition). In the price ex-post setting, our analysis shows that in the post-entry game, the entrant’s optimism increases both firms’ prices and could benefit both firms. For the entry strategy, we find that the high optimism of the entrant induces an incorrect entry decision when her fixed entry cost is medium, leading to a loss. Then, our results reveal that the entrant’s low optimism benefits her entry. As for the pricing strategy, the incumbent will blockade the low optimistic entrant by keeping the normal price, impede the moderate optimistic entrant by lowering price, or accommodate the high optimistic entrant by setting the post-entry price. Further, we consider the asymmetric information (about the entrant’s type) setting and the price ex-ante setting, respectively. We find that the asymmetric information makes the entrant couldn’t benefit from her optimism, which is inconsistent with the symmetric information setting; and the entrant may miss the opportunity to enter the market in the price ex-ante setting.


1)张国军, 香港理工大学应用数学系博士生候选, 中国科学院硕士、365be体育工业工程学士。主要研究方向为运筹学、最优化理论与应用。他曾在JORSC,C&IE上发表过两篇论文,并担任《应用于中国的数学学报》《环境、发展与可持续性》《运筹学学报》等多个期刊的审稿人。 

2)王鑫, 加拿大HEC Montréal博士生候选, 南京大学硕士、365be体育工业工程专业学士。研究方向:不确定优化、供应链管理等。她已经在Industry Engineering and Management、POMS-HK Conference、China Management Science发表多篇文章。

3)徐鹏, 现为南京大学博士生。他于2018年获365be体育硕士学位,2015年获天津工业大学学士学位。他的研究方向包括:行为决策分析、市场进入分析、冲突分析。目前主要研究管理乐观行为和消费惰性行为对市场进入阻止问题的影响,具体包括对阻止策略、进入策略、进入后博弈、进入渠道选择等问题的影响。他曾在Group Decision and Negotiation, Journal of Systems Science and Systems Engineering,Transactions of Nanjing University of Aeronautics & Astronautics, Open Cybernetics & Systemic Journal等期刊上发表论文,现有1篇EJOR在审。



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