Dynamic Scheduling with Uncertain Job Types

50 Pages Posted: 28 Sep 2022

See all articles by Zuo-Jun Max Shen

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR)

Jingui Xie

Technische Universität München (TUM) - TUM School of Management

Zhichao Zheng

Singapore Management University - Lee Kong Chian School of Business

Han Zhou

Technical University Munich

Date Written: September 17, 2022

Abstract

Uncertain job types can arise as a result of predictive or diagnostic inaccuracy in healthcare or repair service systems and unknown preferences in matching service systems. In this paper, we study systems with multiple types of jobs, in which type information is imperfect and will be updated dynamically. Each job has a prior probability of belonging to a certain type which may be predicted by data, models, or experts. A job can only be processed by the right machine, and a job assigned to the wrong machine must be rescheduled. More information is learned from the mismatch, and job type probabilities are updated. The question is how to dynamically schedule all jobs so that they can be processed in a timely fashion. We use a novel coupling and inductive method to conduct optimality analysis. We obtain the near-optimal policy regarding completion time, named the less-uncertainty-first policy, when there are two types of jobs; the insights it yields are used to develop heuristic algorithms for more general cases. We also consider other objectives, including the number of mismatches and the total amount of time jobs spend in the system. In our numerical study, we examine the performance of the proposed heuristics when there are more than two types of jobs under two learning schemes: dedicated learning and exclusive learning. In the extension, we also analyze the problem when jobs are assigned online and find similar insights. It is essential that managers dynamically schedule services by leveraging predictive information and mismatches. Our proposed less-uncertainty-first policy can be used to improve system efficiency in a variety of contexts, because the policy accounts for system dynamics to avoid mismatches and resource idling.

Keywords: Scheduling; Uncertain Job Types; Predictive Information; Learning; Mismatch and Rescheduling

Suggested Citation

Shen, Zuo-Jun Max and Xie, Jingui and Zheng, Zhichao and Zhou, Han, Dynamic Scheduling with Uncertain Job Types (September 17, 2022). Available at SSRN: https://ssrn.com/abstract=4222017 or http://dx.doi.org/10.2139/ssrn.4222017

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR) ( email )

IEOR Department
4135 Etcheverry Hall
Berkeley, CA 94720
United States

Jingui Xie

Technische Universität München (TUM) - TUM School of Management ( email )

Freising
Germany

Zhichao Zheng (Contact Author)

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
Singapore, 178899
Singapore
(65) 6808 5474 (Phone)
(65) 6828 0777 (Fax)

HOME PAGE: http://www.zhengzhichao.com

Han Zhou

Technical University Munich

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