Matching Supply With Demand For Online Retailing

Topic:

Matching Supply With Demand For Online Retailing

Time & Date:

10:30am-12:00pm, 2017/10/13

Venue:

Room 502, Daoyuan Building, CUHK(SZ)

Speaker:

Yunfong Lim, Singapore Management University

Biography:

Yun Fong LIM is Associate Professor of Operations Management and Lee Kong Chian Fellow at the Lee Kong Chian School of Business, Singapore Management University (SMU). Yun Fong's research has appeared in Operations Research, Management Science, Manufacturing and Service Operations Management, and Production and Operations Management. He has delivered keynote and plenary speeches in several international conferences. In addition, his work has received funding by MOE and A*STAR and media coverage by The Business Times, Channel 8, Capital 95.8, and 93.8 Live. His current research interests include e-commerce and omni-channel retailing, warehousing and fulfillment, inventory management, workforce management, and sustainable urban logistics.

Yun Fong is a recipient of the SMU Teaching Excellence Innovative Teacher Award. He teaches both undergraduate and postgraduate courses in Operations Management. He has provided consulting and executive development to corporations such as Maersk, McMaster-Carr Company, Resorts World Sentosa, Schneider Electrics, and Temasek Holdings. Yun Fong obtained both his PhD and MSc degrees in Industrial and Systems Engineering from the Georgia Institute of Technology.

Detail:

We consider an online retailer selling multiple products over a multi-period setting. At the start of each period, the retailer replenishes her products from multiple suppliers. After receiving the products, the retailer allocates the inventory to different fulfillment centers. At the end of each period when the demands for the products from different zones are realized, the retailer fulfills them from different fulfillment centers. We formulate a robust optimization model to determine the replenishment and allocation decisions at the start of each period and the fulfillment decisions at the end of the period. Our approach can handle large-scale problem instances with good quality solutions.