Literature DB >> 35353696

Supporting Visual Exploration of Iterative Job Scheduling.

Gennady Andrienko, Natalia Andrienko, Jose Manuel Cordero Garcia, Dirk Hecker, George A Vouros.   

Abstract

We consider the general problem known as job shop scheduling, in which multiple jobs consist of sequential operations that need to be executed or served by appropriate machines having limited capacities. For example, train journeys (jobs) consist of moves and stops (operations) to be served by rail tracks and stations (machines). A schedule is an assignment of the job operations to machines and times where and when they will be executed. The developers of computational methods for job scheduling need tools enabling them to explore how their methods work. At a high level of generality, we define the system of pertinent exploration tasks and a combination of visualizations capable of supporting the tasks. We provide general descriptions of the purposes, contents, visual encoding, properties, and interactive facilities of the visualizations and illustrate them with images from an example implementation in air traffic management. We justify the design of the visualizations based on the tasks, principles of creating visualizations for pattern discovery, and scalability requirements. The outcomes of our research are sufficiently general to be of use in a variety of applications.

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Year:  2022        PMID: 35353696     DOI: 10.1109/MCG.2022.3163437

Source DB:  PubMed          Journal:  IEEE Comput Graph Appl        ISSN: 0272-1716            Impact factor:   2.088


  1 in total

1.  Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management.

Authors:  Theocharis Kravaris; Konstantinos Lentzos; Georgios Santipantakis; George A Vouros; Gennady Andrienko; Natalia Andrienko; Ian Crook; Jose Manuel Cordero Garcia; Enrique Iglesias Martinez
Journal:  Appl Intell (Dordr)       Date:  2022-06-06       Impact factor: 5.019

  1 in total

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