長沙理工大學將于2015年12月19-20日舉辦優(yōu)化與管理科學青年學者論壇。本論壇是“第六屆優(yōu)化與控制及其應(yīng)用國際會議”中的系列會議，由長沙理工大學數(shù)學與計算科學學院承辦，長沙理工大學國際學院、管理學院及浙江大學管理學院協(xié)辦。參加此次論壇的青年學者主要來自于國內(nèi)各大高校的優(yōu)秀青年學者。本次論壇為期兩天，共安排6-8個學術(shù)報告，報告主題聚焦于近年OM領(lǐng)域的熱點問題。我們希望通過這次論壇，為與會青年學者提供高水平的學術(shù)分享平臺，促進彼此之間的交流與合作，推動本領(lǐng)域高水平研究的健康發(fā)展。
2015年12月18日，星期五
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報到: 16: 00-20:00;晚餐:18:00-20:00
地點：長沙卡斯迪曼享酒店
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2015年12月19日，星期六
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8:00-8:30
| 簽到地點：長沙理工大學數(shù)學與計算科學學院學術(shù)報告廳（理科樓A-419）
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8:30-8:50
| 歡迎辭：黃立宏副校長
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8:50-9:35
| 報告1 Daniel Zhuoyu Long, Assistant Professor,Department of System Engineering and Engineering Management,The Chinese University of Hong Kong
Title：Capacity Planning in Project Management by Adaptive Robust Optimization
Abstract: We consider project planning with partially characterized probability distributions for task durations. Many project companies resolve their lack of resources by using outsourcing companies, for example contract manufacturers. However, outsourcing companies typically require reservation in advance, before project performance is known. Decisions about capacity reservation affect the company's ability to respond effectively to delays in task execution. We describe an adaptive robust optimization model for project planning. First, the project company makes capacity reservations with an outsourcing company. Given those decisions, we consider the worst case distribution of task durations, subject to the partial information known about the distribution, and a set of budget of uncertainty constraints. Based on both its reserved capacity and the worst case distribution of task durations, the company makes decisions about fast tracking and outsourced crashing. Based on typical concerns about downside risks in project companies, the objective of the model is to minimize the project's worst case conditional value-at-risk (CVaR) of its total capacity reservation, fast tracking, crashing, and makespan penalty costs. We allow for correlation in task performance, and for piecewise linear costs of crashing and makespan penalties. This discrete, nonlinear optimization problem is solved optimally using column and constraint generation. Computationally efficient optimal solution of the model is possible for practical size projects. Our work provides project managers with a planning tool for effective risk minimization in projects, and helpful insights about how to make capacity reservations decisions.
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9:35-10:20
| 報告2Zhenyu Hu, Assistant Professor, Department of Decision Science, NUS.
Title:Dynamic Pricing with Reference Price Effects: Characterizations and Computations
Abstract：In a market with repeated purchases such as supermarket, inter-temporal changes in prices would have significant impacts on consumers' perception of the price and in turn influence consumers' purchasing decisions. The concept of reference price, developed in the economics and marketing literature, argues that consumers form price expectations and use them to judge the current selling price. Facing reference price effects, firm needs to dynamically adjust its prices so as to maximize its profit over time. In this talk, I will investigate both characterizing the qualitative behavior of the optimal prices as well as the computational issues of finding the optimal prices.
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10:20-10:30
| 茶歇
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10:30-11:15
| 報告3Xiting Gong, Assistant Professor, Department of System Engineering and Engineering Management, The Chinese University of Hong Kong
Title: Approximation Algorithms for Perishable Inventory Systems
Abstract:Perishable products such as meat, fruit, dairy products, and pharmaceuticals are ubiquitous and play an indispensable role in our society. However, the optimal control policies for perishable inventory systems are very complicated due to the finite product lifetime; and their computation is intractable even with a relatively short product lifetime due to the “curse of dimensionality”. To overcome this difficulty, we develop easy-to-compute and near-optimal approximation algorithms with theoretical constant-factor worst-case performance guarantees (WCPG) for several classes of such systems. For the classical periodic review uncapacitated perishable inventory systems with zero lead time, we develop a proportional balancing policy that admits a WCPG less than 3 under general correlated demand processes and a dual balancing policy that admits a WCPG of 2 under independent and stochastically non-decreasing demand processes. For the capacitated perishable inventory systems with positive lead times, we develop a proportional balancing policy that admits a WCPG mostly less than 3 under a broad class of demand processes of practical interest. Furthermore, our numerical studies show that the proposed policies perform consistently close to optimal.
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11:15-12:00
| 報告4：Jie Song, Associate Professor, Department of Industrial Engineering and Management, Peking University
Title: Integrating Optimal Simulation Budget Allocation and Genetic Algorithm to Find the Approximate Pareto Patient Flow Distribution
Abstract: The imbalanced development among different levels of healthcare facilities has become a major social issue in China’s urban healthcare system, which has raised the irrational patient flow distribution on the levels of both intra-hospital and inter-healthcare facilities. In this research, we develop a methodology to find the optimal macrolevel patient flow distribution in terms of multidimension inputs and outputs for the two-level healthcare system. The proposed method integrates the discrete event simulation (DES), the multiobjective optimization and the simulation budget allocation together to comprehensively improve the overall system performances by finding the approximate Pareto patient flow distribution in the hierarchical healthcare system. The multiobjective optimal computing budget allocation (MOCBA) is applied to improve the efficiency, where the non- dominated probability is functioned as the fitness measurement to each design. A case study based on the real data is carried out to validate and implement the proposed method. The results of the case study show the recommended Pareto optimal patient flow distribution can improve the overall hierarchical system performances and our methodology are qualified as a quantitative decision tool for decision makers.
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12:00-13:50
| 午餐：12:00在理科樓下坐車去卡斯迪曼享酒店用餐，13:50坐車去理科樓
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14:00-14:45
| 報告5：Zhan Pang, Lecture, Lancaster University Management School
Title:The Newsvendor Problem under Taxation
Abstract: Corporation income tax (CIT), which directly depends on profit during a fiscal year, can be one of the largest cash outflows that a firm experiences. Therefore, contributing to tax avoidance, inventory cost may no longer play negative role all the time. In our work, we discuss the impact of CIT consideration on operational decisions with a newsvendor model. After obtaining optimal decisions under different CIT structures, the influence of tax factors is analyzed. Since companies usually pay CIT once a fiscal year, which is combined with multiple ordering periods, we extent basic results with a dynamic newsvendor model within a fiscal year. Eventually, we discuss the scenario that the loss of previous period can be carried to next fiscal year.
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14:45-15:30
| 報告6：Long He, Assistant Professor, Department of Decision Science, NUS.
Title:Service Region Design for Urban Electric Vehicle Sharing Systems
Abstract:Emerging collaborative consumption business models have shown promise in both generating business opportunities and enhancing efficient use of resources. In the transportation domain, car sharing models are adopted at mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond significant potential to reduce car ownership, car sharing shows promise in supporting adoption of fuel efficient vehicles, such as electric vehicles (EVs), due to these vehicles’ special cost structure with high purchase but low operating costs. Recently, it has become a trend for key players in the car sharing business, such as Car2Go and Autolib, to employ EVs in an operations model that accommodates one-way trips. On the one hand, the one-way model brings about significant improvements in coverage of travel needs and therefore adoption potential, compared with the conventional round-trip-only model (advocated by ZipCar, for example). On the other hand, it poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing the geographical service region in which to operate the service. This decision encompasses the trade-off between maximizing customer catchment by covering travel needs, and controlling fleet operations costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) operations under spatially-imbalanced and time-varying travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally-robust optimization framework that informs robust decisions to avoid possible ambiguity (or lack) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally-tractable with practical scale data. Applying this approach to the case of Car2Go's service in San Diego, California, with real operations data, we address a number of planning questions and suggest potential for future development of the service.
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15:30-15:45
| 茶歇
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15:45-17:30
| Panel Discussion Organized By Jeff Hong.
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17:30-19:30
| 晚宴17:30在理科樓下坐車去卡斯迪曼享酒店用餐，
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聯(lián)絡(luò)人
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李秋桂（電話：13975152018，郵箱：1294381639@qq.com）
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李 嬌（電話：18670335485，郵箱：1294381639@qq.com）
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