管科系系列讲座第172/173期预告

管科系系列讲座第172期预告

时间：2016年8月1日（周一）10:00

地点：史带楼303

主持人： 吴肖乐副教授

演讲嘉宾：Siqian Shen,Assistant Professor, Department of Industrial and Operations Engineering, University of Michigan at Ann Arbor

Title：Optimizing the Profitability and Quality of Service in Carshare Systems

Abstract：In this talk, we apply stochastic integer programming to optimizing allocation of a carshare fleet to service zones under the uncertainty of one-way and round-trip rental demand. We minimize the cost of purchasing parking lots/parking permits, in reservation-/free-float-based systems, in addition to the cost of allocating vehicles. We employ the Sample Average Approximation (SAA) method, where we construct a spatial-temporal network for each sample to capture the total profit, vehicle relocation cost, and penalties from unsatisfied demand. We minimize the expected cost minus profit, and also consider a risk-averse model variant that penalizes the conditional value-at-risk of unsatisfied demand. For each model, we develop a branch-and-cut algorithm with mixed-integer rounding-enhanced Benders cuts. We test instances generated from Zipcar data in Boston to demonstrate the insights of stochastic carshare system management. We will discuss several extensions of our models in various applications, including vehicle-to-grid integration and building shared service networks for underserved communities. 

管科系系列讲座第173期预告

时间：2016年8月1日（周一）11:00

地点：史带楼303

主持人： 吴肖乐副教授

演讲嘉宾：Ruiwei Jiang, Assistant Professor, Department of Industrial and Operations Engineering, University of Michigan at Ann Arbor

Title：Integer Programming Approaches for Appointment Scheduling with Random No-shows and Service Durations 

Abstract：We consider a single-server appointment scheduling problem given a fixed sequence of arrivals with random no-shows and service durations. The joint probability distribution of the uncertain parameters is assumed to be ambiguous and only the support and first moments are known. We formulate a class of distributionally robust optimization models that incorporate the worst-case expected cost of appointment waiting, server idleness, and overtime. We obtain exact mixed-integer nonlinear programming (MINLP) reformulations that facilitate decomposition algorithms, and derive valid inequalities to strengthen the reformulations. In particular, we derive the convex hulls for special cases of no-show beliefs, yielding polynomial-size linear programming reformulations for the least and the most conservative supports of no shows. We test various instances to demonstrate the computational efficacy of our approaches and provide insights for appointment scheduling under distributional ambiguity of multiple uncertainties. 

管理科学系

2016-7-22