Numerous optimization problems in production systems can be considered as decision-making
processes that determine the best allocation of resources to tasks over time to optimize one or more
objectives in concert with big data. Among the optimization problems, production scheduling and
routing of robots for material handling are becoming more important due to their impacts on
system performance. However, the development of efficient algorithms for scheduling or routing
faces several challenges. While the scheduling and vehicle routing problems can be solved by
mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems.
Thus, further research on machine learning applications to those problems is a significant step
towards increasing the possibilities and potentialities of field application. In order to create truly
intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine
learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization
of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules.
In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem
with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution
approach, a random-forest-based approach called Random Forest for Obtaining Rules for
Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules
automatically. Finally, an optimization problem for routing of autonomous robots for minimizing
total tardiness of transportation requests is analyzed by decomposing it into three sub-problems.
In order to solve the sub-problems, a comprehensive framework with consideration of conflicts
between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search
algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression
(COBALT) that incorporates the contextual bandit into operator selection is developed. The
findings from my research contribute to suggesting a guidance to practitioners for the applications
of ML to scheduling and control problems, and ultimately to lead the implementation of smart
factories.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12668501 |
| Date | 05 August 2020 |
| Creators | Sungbum Jun (9136835) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Relation | https://figshare.com/articles/thesis/SCHEDULING_AND_CONTROL_WITH_MACHINE_LEARNING_IN_MANUFACTURING_SYSTEMS/12668501 |
Page generated in 0.0025 seconds