Scheduling reentrant jobs on parallel machines with a remote server

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Bibliographic Details
Title: Scheduling reentrant jobs on parallel machines with a remote server
Authors: Chakhlevitch, K. Konstantin.Chakhlevitch.1@city.ac.uk, Glass, C.A.1
Source: Computers & Operations Research. Sep2009, Vol. 36 Issue 9, p2580-2589. 10p.
Subjects: Heuristic programming, Web server software, Benchmarking (Management), Operations research, Microbiology, Problem solving, Remote access networks, Computer software, Computer network resources
Abstract: Abstract: This paper explores a specific combinatorial problem relating to reentrant jobs on parallel primary machines, with a remote server machine. A middle operation is required by each job on the server before it returns to its primary processing machine. The problem, which is new to the literature, is inspired by the logistics of a semi-automated microbiology laboratory. We establish the NP-hard nature of the problem, and demonstrate various structural properties. A heuristic is developed and tested on randomly generated benchmark data. No alternative heuristics are available in the literature for comparison, but results indicate solutions reliably within 1.5% of optimum. Moreover, tests of our heuristic on real-life data from the microbiology laboratory provide a 20% improvement in throughput relative to current practice. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: This paper explores a specific combinatorial problem relating to reentrant jobs on parallel primary machines, with a remote server machine. A middle operation is required by each job on the server before it returns to its primary processing machine. The problem, which is new to the literature, is inspired by the logistics of a semi-automated microbiology laboratory. We establish the NP-hard nature of the problem, and demonstrate various structural properties. A heuristic is developed and tested on randomly generated benchmark data. No alternative heuristics are available in the literature for comparison, but results indicate solutions reliably within 1.5% of optimum. Moreover, tests of our heuristic on real-life data from the microbiology laboratory provide a 20% improvement in throughput relative to current practice. [Copyright &y& Elsevier]
ISSN:03050548
DOI:10.1016/j.cor.2008.11.007