A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search
By G. Moslehi, M. Mahnam. 2011
344 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/df5f5df8f4916c84b5273ab48eafd21bc09a2006
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In Artificial Intelligence
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In Industrial and Manufacturing Engineering
Citations in the corpus
- A research survey: review of flexible job shop scheduling techniques (2016, background): “…local search embedding a VNS-based self-adaptive strategy Minimum of makespan, workload of most loaded machine, total workload of machines 12 Moslehi and Mahnam (2011) Research PSO + local search Minimum of makespan, workload of most loaded machine, total workload of…”
- A modified shifting bottleneck heuristic and disjunctive graph for job shop scheduling problems with transportation constraints (2014, background): “Other researchers (like Moslehi and Mahnam 2011; Lee et al. 2012) studied flexible job shop problems by using artificial intelligence tools.”
- A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems (2019, methodology): “More and more swarm intelligence (SI) and evolutionary algorithms (EA) have thus been employed to solve FJSP in recent years, including genetic algorithm (GA) [6]–[48], particle swarm optimization (PSO) [49]–[62], ant colony optimization (ACO) [63]–[70], tabu search (TS) [71]–[81], artificial bee co”
- An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, methodology): “…and guided LS to solve the multi-objective FJSP. Li et al. (2011b) proposed a hybrid TS algorithm with an ef fi cient neighborhood structure for FJSP. Moslehi and Mahnam (2011) presented a Pareto approach based on PSO and local search for multi-objective FJSP. Zhang et al. (2012) hybridized GA and…”