A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem
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DOI: 10.1016/j.asoc.2017.12.04https://www.researchgate.net/profile/Boris_Goldengorin/publication/322228276_A_hybrid_method_of_2-TSP_and_novel_learning-based_GA_for_job_sequencing_and_tool_switching_problem/links/5ab3bdffa6fdcc1bc0c35053/A-hybrid-method-of-2-TSP-and-novel-learning-based-GA-for-job-sequencing-and-tool-switching-problem.pdf
http://hdl.handle.net/11603/20352
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Show full item recordDate
2018-01-03Type of Work
214-229 ppText
journal article
Department
Information System and Decision SciencesProgram
Information System and Decision SciencesCitation of Original Publication
Ahmadi, E., Goldengorin, B., Süer, G. A., & Mosadegh, H. (2018). A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem. Applied Soft Computing, 65, 214-229. DOI: 10.1016/j.asoc.2017.12.04Rights
Public Domain Mark 1.0http://creativecommons.org/publicdomain/mark/1.0/
Subjects
Combinatorial optimizationJob Scheduling
Tool switches
Genetic algorithm
Q-learning
Reinforcement learning
Abstract
One of the well-known problems in a single machine scheduling context is the Job Sequencing and Tool Switching Problem (SSP). The SSP is optimally sequencing a finite set of jobs and loading a restricted subset of tools to a magazine with the aim of minimizing the total number of tool switches. It has been proved in the literature that the SSP can be reduced to the Job Sequencing Problem (JSeP). In the JSeP, the number of tool switches from the currently processed job to the next job depends on the sequencing of all predecessors. In this paper, the JSeP is modeled as a Traveling Salesman Problem of Second Order(2-TSP).
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Except where otherwise noted, this item's license is described as Public Domain Mark 1.0