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Applying Reinforcement Learning to Plan Manufacturing Material Handling Part 2: Experimentation and Results

Published: 18 April 2019 Publication History

Abstract

Applying machine learning to improve the efficiency of complex manufacturing processes, particularly logistics and material handling, can be a challenging problem. The interconnectedness of the multiple components that compose such processes and the typically large number of variables required to specify procedures and plans within those processes combine to make it very difficult to map the details of real-world manufacturing processes to an abstract mathematical representation suitable for machine learning methods. In this paper, we report on the application of machine learning methods, in particular reinforcement learning, to generate increasingly efficient plans for material handling to satisfy temporally varying product demands in a representative manufacturing facility. The essential steps in the research included defining a formal representation of a realistically complex material handling plan, defining a set of suitable two-stage plan change operators as reinforcement learning actions, implementing a simulation-based multi-objective reward function that considers multiple components of material handling costs, and abstracting the many possible material handling plans into a state set small enough to enable reinforcement learning. Extensive experimentation with multiple starting plans showed that the reinforcement learning process could consistently reduce the material handling plans' costs over time. This work may be one of the first applications of reinforcement learning with a multi-objective reward function to a realistically complex material handling process. This paper first provides an explanation of how the material handling plans and rewards were abstracted into a manageable state set. It then details the various initial plans and experimental trials used to test the plans. Finally, it reports the results of those experimental trials, including the plan change policies learned and the reductions in material handling costs achieved.

References

[1]
S. Govindaiah and M. D. Petty. 2019. Applying Reinforcement Learning to Plan Manufacturing Material Handling Part 1: Background and Formal Problem Specification. In 2019 ACM Southeast Conference (ACMSE 2019), April 18-20, 2019, Kennesaw, GA, USA. ACM, New York, NY, USA.
[2]
R. S. Sutton and A. G. Barto. 1998. Reinforcement Learning: An Introduction MIT Press, Cambridge, MA.
[3]
P. Vamplew, R. Dazeley, A. Berry, R. Issabekov, and E. Dekker. 2011. Empirical Evaluation Methods for Multiobjective Reinforcement Learning Algorithms. Machine Learning, vol. 84, no. 1--2, pp. 51--80, 2011.
[4]
S. Govindaiah and M. D. Petty. 2019. A Discrete Event Simulation-based Multiobjective Reinforcement Learning Reward Function for Optimizing Manufacturing Material Handling. In Proceedings of the 2019 Simulation Innovation Workshop, Orlando FL, February 2019.
[5]
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. 20019. Introduction to Algorithms, Third Edition. MIT Press, Cambridge, MA.
[6]
R. Bellman. 1957. Dynamic Programming. Princeton University Press, Princeton, NJ.
[7]
R. S. Sutton and A. G. Barto. 2018. Reinforcement Learning: An Introduction, Second Edition. MIT Press, Cambridge, MA.

Cited By

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  • (2021)Applying reinforcement learning to plan manufacturing material handlingDiscover Artificial Intelligence10.1007/s44163-021-00003-31:1Online publication date: 22-Sep-2021
  • (2019)Applying Reinforcement Learning to Plan Manufacturing Material Handling Part 1Proceedings of the 2019 ACM Southeast Conference10.1145/3299815.3314451(168-171)Online publication date: 18-Apr-2019

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Published In

cover image ACM Conferences
ACMSE '19: Proceedings of the 2019 ACM Southeast Conference
April 2019
295 pages
ISBN:9781450362511
DOI:10.1145/3299815
  • Conference Chair:
  • Dan Lo,
  • Program Chair:
  • Donghyun Kim,
  • Publications Chair:
  • Eric Gamess
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2019

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Author Tags

  1. Material handling
  2. machine learning
  3. multi-objective learning
  4. planning
  5. reinforcement learning

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ACM SE '19
Sponsor:
ACM SE '19: 2019 ACM Southeast Conference
April 18 - 20, 2019
GA, Kennesaw, USA

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all
  • (2021)Applying reinforcement learning to plan manufacturing material handlingDiscover Artificial Intelligence10.1007/s44163-021-00003-31:1Online publication date: 22-Sep-2021
  • (2019)Applying Reinforcement Learning to Plan Manufacturing Material Handling Part 1Proceedings of the 2019 ACM Southeast Conference10.1145/3299815.3314451(168-171)Online publication date: 18-Apr-2019

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