Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jack S. Cook

dblp:28/6573 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › integer programming › binary optimization
0-1 integer programming
0.011994
Optimal robot selection and workstation assignment for a CIM system · IEEE Trans. Robotics Autom. 1994

Methods — techniques the papers use, named apart from their topics

three-phase optimization algorithm · 0.00-1 mathematical programming · 0.0
YearPublicationVenuePosition
1994 Optimal robot selection and workstation assignment for a CIM system
abstract
In this paper, a mathematical program and solution algorithm is developed for optimal robot selection and workstation assignment for a CIM system. In specific, our model considers selection of a proper mix of multiple-type robots such that operational requirements from a given number of work- stations are satisfied at minimal system cost. Each robot is characterized by its fixed charge, and subject to limits on machine time and work envelope. Each workstation has known demands on both robot machine time and work space. The model is formulated as a pure 0-1 mathematical program and is shown harder than two-dimensional bin packing, a well-known NP-hard problem. A three-phase optimization algorithm is implemented and tested by solving 450 randomly generated problems. Computational results indicate the solution algorithm is effective in solving problems of a practical size (i.e., 50 workstations and a maximum of 20 robots) within acceptable computational times.>
Jack S. Cook, Bernard T. Han
IEEE Trans. Robotics Autom.1