主題：workshop of Management of Science and Engineering
地點：騰訊會議: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 pro?t or the desired fill rate. To strike a balance between sales pro?t and customer satisfaction, the target pro?t and desired ?ll 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）張國軍, 香港理工大學應用數學系博士生候選, 中國科學院碩士、南京航空航天大學工業工程學士。主要研究方向為運籌學、最優化理論與應用。他曾在JORSC，C&IE上發表過兩篇論文，并擔任《應用于中國的數學學報》《環境、發展與可持續性》《運籌學學報》等多個期刊的審稿人电竞竞猜官网。
2）王鑫, 加拿大HEC Montréal博士生候選, 南京大學碩士、南京航空航天大學工業工程專業學士。研究方向：不確定優化、供應鏈管理等。她已經在Industry Engineering and Management、POMS-HK Conference、China Management Science發表多篇文章。
3）徐鵬, 現為南京大學博士生。他于2018年獲南京航空航天大學碩士學位电竞竞猜官网，2015年獲天津工業大學學士學位。他的研究方向包括：行為決策分析、市場進入分析、沖突分析电竞竞猜官网。目前主要研究管理樂觀行為和消費惰性行為對市場進入阻止問題的影響，具體包括對阻止策略、進入策略、進入后博弈电竞竞猜官网、進入渠道選擇等問題的影響。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等期刊上發表論文，現有1篇EJOR在審。
版權所有：南京航空航天大學 ALL RIGHTS RESERVED 蘇ICP備05070685號