Topic 1: Managing Stochastic Inventory Systems with Scarce Resources

报告人: Rowan Wang

Singapore Management University (SMU)

 

报告内容摘要

---

We consider the problem of managing production in a production-inventory system where a ?rm is subject to an allowance (a limit) on either the amount of input it can use or the amount of output it can produce over a speci?ed compliance period. Examples of such settings are numerous and include those where limits are placed on the use of scarce natural resources as input or on the amount of waste or harmful pollution generated by production. We formulate the problem as a stochastic dynamic program with a two-dimensional state space: on-hand inventory level and remaining allowance. To simplify the analysis, we consider an extended state-space version of the problem and show that this modi?ed version of the problem reduces to a one-dimensional problem. We show the optimal policy for this modi?ed version and then use it to characterize the structure of the optimal policy for the original problem. We draw several managerial insights. In particular, we show that it is possible to signi?cantly reduce the allowance amount without signi?cantly increasing cost.

报告人简介

---

Rowan Wang is an Assistant Professor of Operations Management in the Lee Kong Chain School of Business at the Singapore Management University (SMU). Rowan’s research inter?ests include supply chain management, manufacturing and service operations, production and inventory systems, and queueing systems. His work has been published in Manufacturing & Service Operations Management.

 

Topic 2: Managing Suppliers: Joint Audit and Shared Supplier Information

报告人: Xin Fang

Singapore Management University (SMU)

 

报告内容摘要

---

Product safety incidents in recent years have compelled many manufacturers to rethink approaches to manage product quality of their suppliers. In this paper, we investigate two cooperative approaches that are used in practice: auditing common suppliers jointly ("joint audit") and sharing independently collected information with other manufacturers ("shared supplier information"). We develop a model that captures both competitive and cooperative interactions among manufacturers. Our analysis reveals that cooperation does not necessarily improve product safety. The effectiveness of these approaches depends crucially on the externality of product safety of one manufacturer on other manufacturers. Further, we find that, when the risk of product safety failure is high, shared supplier information is more effective than joint audit; otherwise, joint audit is more effective. We also investigate the incentives of competing manufacturers to cooperate by analyzing a cooperative game in partition function form. We find that, in some cases, manufacturers may voluntarily cooperate with each other. This is true even when some manufacturers have better information about product safety than others. However, in other cases, manufacturers with better information need to be compensated properly, and we design an allocation that is ideal for motivating sharing. Since product safety has a significant impact on the well-being of consumers, industries and governments should assess the level of externality and risk of product safety failure in specific market settings in order to design effective and stable cooperative programs.

报告人简介

---

Xin Fang is an assistant professor of Operations Management at Lee Kong Chian School of Business, Singapore Management University. He earned his Ph.D. in Operations Management from Carnegie Mellon University in 2014, and his B.S. in Information Systems from Fudan University in 2008. Xin studies the problems related to competition and coopetition in global supply chain networks. His research applies cooperative game theory, non-cooperative game theory and theory of social and economic networks to the area of supply chain management, especially supply chain security, decentralized distribution systems and supplier management.

    

Topic 3: Least Squares Approximation to Stochastic Optimization Problems

报告人：Zhichao Zheng

Singapore Management University (SMU)

 

报告内容摘要

---

This paper is motivated by the following question: How to construct good approximation for the distribution of the solution value to linear optimization problem when the objective function is random? More generally, we consider any mixed zero-one linear optimization problem, and develop an approach to approximate the distribution of its optimal value when the random objective coefficients follow a multivariate normal distribution. Linking our model to the classical Stein's Identity, we show that the least squares normal approximation of the random optimal value can be computed by solving the persistency problem, first introduced by Bertsimas et al. (2006). We further extend our method to construct a least squares quadratic estimator to improve the accuracy of the approximation, in particular, to capture the skewness of the objective. We use this approach to construct good estimators for (a) the fill rate of an inventory system in a finite horizon; (b) the waiting time distribution of the nth customer in a G/G/1 system when the arrival rate equals the service rate; and (c) the project completion time distribution.

报告人简介

---

Zhichao Zheng is an Assistant Professor of Operations Management at the Singapore Management University. His current research interests lie in the area of prediction and planning under uncertainty. He applies his research in various industrial domains, including healthcare operations management, spare parts logistics service planning and contracting, maritime scheduling, etc. He received his BS (First Class Honors) in Applied Mathematics from the National University of Singapore in 2009, and Ph.D. in Management from the Department of Decision Science in the National University of Singapore in 2013.

   

Topic 4: Inventory Management Based on Target-Oriented Robust Optimization

报告人：LIM Yun Fong

Singapore Management University (SMU)

 

报告内容摘要

---

We propose a target-oriented robust optimization approach to solve a multi-product, multi-period inventory management problem subject to ordering capacity constraints. We assume the demand for each product in each period is characterized by an uncertainty set, which depends only on the demand's mean and bounds. Under a pre-specified cost target, we determine an ordering policy that maximizes the sizes of all demand uncertainty sets. We prove that a static policy is optimal for our formulation, which significantly reduces the computational burden. By tuning the cost target, the resultant policy can achieve a balance between the expected cost and the associated cost variance. Numerical experiments suggest that, although only limited demand information is used, the proposed approach performs comparably to traditional methods based on dynamic programming and stochastic programming. More importantly, our approach significantly outperforms the traditional methods if the latter assume inaccurate demand distributions. We demonstrate the applicability of our approach using two case studies from different industries.

报告人简介

---

LIM Yun Fong is an Associate Professor of Operations Management at the Lee Kong Chian School of Business, Singapore Management University (SMU). His research centers on workforce management and he is especially interested in boosting productivity of work teams in manufacturing, distribution, and services. He has served as Cluster Chair of Workforce Management for INFORMS Annual Meetings. His other research interests include analytics for warehousing and fulfillment in supply chains. Yun Fong’s work has appeared as a keynote paper in INCOM 2009 at Moscow, Russia and in top journals including Operations Research and Management Science. His research has been funded by A*STAR and he was named NOL Fellow in 2012.

Yun Fong teaches courses in Operations Management at executive, graduate, and undergraduate levels. He won the 2010 SMU Teaching Excellence Innovative Teacher Award, and his teaching case has been selected as Finalist for the INFORMS Case Competition 2014. He has consulted and is constantly engaged in executive development for corporations such as Building and Construction Authority, KTP Hospital, McMaster-Carr Company, Resort World Sentosa, Schneider Electrics, SingHealth, and YCH Group. Yun Fong obtained both his PhD and MSc degrees in Industrial and Systems Engineering from the Georgia Institute of Technology.

Your participation is warmly welcomed!
 
欢迎广大师生光临！

工业工程与管理系
秘书：解菲
电话：10-82524866
Email: faye@coe.pku.edu.cn