Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems

By J. Tay, Nhu Binh Ho. Computers & industrial engineering, 2008

447 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/10b5a8451a7b118327d186035805a85ec7159be1

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In Artificial Intelligence

In Industrial and Manufacturing Engineering

Citations in the corpus

An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, methodology): “Tay and Ho (2008) used DRs discovered through genetic programming to solve the multi-objective FJSP. Baykasoglu and Ozbakir (2010) analyzed the effects of DRs on the scheduling performance of job shops with different ﬂ exibility levels.”
Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem (2015, background): “Xia and Wu [2], Tay and Ho [3], and Li et al.”
A research survey: review of flexible job shop scheduling techniques (2016, background): “…et al. (2008) Research GA + variable neighborhood descent Minimum of makespan, workload of most loaded machine, total workload of machines 270 Tay and Ho (2008) Research Genetic programming + dispatching rules Minimum of makespan, mean tardiness, and mean flow time 190 Li et al. (2008)…”
Automated Design of Production Scheduling Heuristics: A Review (2016, methodology): “[44], who tested the dispatching rules from Tay and Ho [62], which have been trained on and shown to be effective for static instances, in a long-term simulation with dynamic job arrivals and find that they perform poorly.”
A Classification of Hyper-heuristic Approaches (2010, methodology): “An alternative idea is to use genetic programming to evolve a program representing a function, which is part of the processing of a given problem specific construction heuristic [9, 8, 11, 22, 31, 59].”