Journal Article10.1287/IJOC.3.2.149
A Computational Study of the Job-Shop Scheduling Problem
David Applegate,William J. Cook +1 more
894
TL;DR: The optimization procedure, combining the heuristic method and the combinatorial branch and bound algorithm, solved the well-known 10×10 problem of J. F. Thomson in under 7 minutes of computation time on a Sun Sparcstation 1.
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Abstract: The job-shop scheduling problem is a notoriously difficult problem in combinatorial optimization. Although even modest sized instances remain computationally intractable, a number of important algorithmic advances have been made in recent years by J. Adams, E. Balas and D. Zawack; J. Carlier and E. Pinson; B. J. Lageweg, J. K. Lenstra and A. H. G. Rinnooy Kan; and others. Making use of a number of these advances, we have designed and implemented a new heuristic procedure for finding schedules, a cutting-plane method for obtaining lower bounds, and a combinatorial branch and bound algorithm. Our optimization procedure, combining the heuristic method and the combinatorial branch and bound algorithm, solved the well-known 10×10 problem of J. F. Muth and G. L. Thomson in under 7 minutes of computation time on a Sun Sparcstation 1. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
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Citations
An effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem
TL;DR: The computational results validate the enhancements accomplished by the proposed selective exchange of genetic materials, and show the superiority of the proposed algorithm over the compared works in terms of solution quality, and validate its effectiveness.
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Integration of Dataflow-Based Heterogeneous Multiprocessor Scheduling Techniques in GNU Radio
George Zaki,William Plishker,Shuvra S. Bhattacharyya,Charles Clancy,John Kuykendall +4 more
- 01 Feb 2013
TL;DR: This work augments a popular SDR framework with a library that seamlessly allows offloading of algorithm kernels mapped to the GPU without changing the original protocol description, and shows how to utilize Single Instruction Multiple Data units provided in Graphics Processing Units (GPUs) along with vector accelerators implemented in General Purpose Processors (GPPs).
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A Multi-objective Reinforcement Learning Algorithm for JSSP
Beatriz M. Méndez-Hernández,Erick D. Rodríguez-Bazan,Yailen Martínez-Jiménez,Pieter Libin,Ann Nowé +4 more
- 17 Sep 2019
TL;DR: A Multi-Objective Multi-Agent Reinforcement Learning Algorithm that aims to obtain the non-dominated solutions set for Job Shop scheduling problems and is evaluated and compared to other algorithms from the literature using two measures for evaluating the Pareto front.
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