Abstract

Motivated by the sales force operations of a B2B payment services company in Belgium, we develop a data-intensive model to schedule customer appointments for a team of sales agents. The appointments are made by an outbound call center and subject to uncertain customer approval. The objective of the model is to maximize the number of fields appointments per agent by minimizing the travel and idle times between appointments. We formally define a Customer Preference-based Field Visit Appointment Scheduling problem (CPFVAS) and formulate the model as a Markov decision process, demonstrate the existence of an optimal policy, develop an upper bound, and analytically derive sensitivity properties with regard to several design parameters. Because of the large amount of continually incoming data, the problem suffers from the curse of dimensionality, even for small instances. We thereby find that the existing policies cannot address the problem directly. Therefore, we develop a time efficient scheduling policies that dynamically decide which clients to call and which time slots to propose to each client. We perform numerical experiments based on a real data set to show that the designed policy computes fast and achieves rather competitive performance even for large scale instances.

Keywords (3-5 keywords): Customer preference, Appointment scheduling, Field visit, Markov Decision Process

Time

12月10日（星期二）16:00-17:30

Speaker

Yanlu Zhao is a PhD candidate of Operations Management at ESSEC Business School, France.  His research interests focus on the intersection between business analytics and operations management, and his dissertation is supervised by Prof. Felix Papier and Prof. Laurent Alfandari. In 2019, he also spent six months to visit Prof. Chung Piaw Teo at the Institute of operations research and analytics, National University of Singapore. Yanlu holds an MSc of Industrial Engineering from Tsinghua University.

Venue

信息管理与工程学院104室

上海财经大学（第三教学楼西侧）

上海市杨浦区武东路100号