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Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem
The job-shop scheduling is one of the most studied optimization problems...
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Metaheuristics for the Online Printing Shop Scheduling Problem
In this work, the online printing shop scheduling problem introduced in ...
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The Scheduling Job-Set Optimization Problem: A Model-Based Diagnosis Approach
A common issue for companies is that the volume of product orders may at...
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The cyclic job-shop scheduling problem: The new subclass of the job-shop problem and applying the Simulated annealing to solve it
In the paper, the new approach to the scheduling problem are described. ...
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Bilevel Learning Model Towards Industrial Scheduling
Automatic industrial scheduling, aiming at optimizing the sequence of jo...
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Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling
Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel cons...
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Evaluation of bioinspired algorithms for the solution of the job scheduling problem
In this research we used bio-inspired metaheuristics, as artificial immu...
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Large-Scale Benchmarks for the Job Shop Scheduling Problem
This report contains the description of two novel job shop scheduling benchmarks that resemble instances of real scheduling problem as they appear in industry. In particular, the aim was to provide large-scale benchmarks (up to 1 million operations) to test the state-of-the-art scheduling solutions on problems that are closer to what occurs in a real industrial context. The first benchmark is an extension of the well known Taillard benchmark (1992), while the second is a collection of scheduling instances with a known-optimum solution.
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