Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic
By Imed Kacem, S. Hammadi, P. Borne. Mathematics and Computers in Simulation, 2002
582 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/327fd0500a4c85dcedcac9384e8440e50077963e
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Citations in the corpus
- Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems (2008, methodology): “The classical JSP and FJSP (in single or multi-objectives) have been solved by many stochastic local search methods, such as Simulated Annealing (Kolonko, 1999), Tabu Search (Brandimarte, 1993; Mastrolilli & Gambardella, 2000; Nowicki & Smutnicki, 1996), Genetic Algorithms (Ho & Tay, 2004; Kacem, Ha”
- Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem (2015, methodology): “[5] have proposed a localization approach to solve the resource assignment problem and an evolutionary approach controlled by the assignment model to solve the mono-objective and multi-objective FJSP.”
- An effective genetic algorithm for the flexible job-shop scheduling problem (2011, methodology): “…of the idle time interval on machine Mx Ex the end time of the idle time interval on machine Mx L ¼ PN i¼1Jio the sum of all operations of all jobs Kacem, Hammadi, and Borne (2002) classified the FJSP into P-FJSP and T-FJSP as follows: If Xij X, then it has partial flexibility, it is partial…”
- An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem (2009, methodology): “A middle scale instance which is taken from Kacem et al. (2002b) is shown in Table 5.”
- Mathematical modeling and heuristic approaches to flexible job shop scheduling problems (2007, background): “Other literatures of this approach were represented by Kacem, Hammadi, and Borne (2002) , Xia and Wu (2005) and Low, Yip, and Wu (2006).”
- Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems (2002, background): “Also, the study of the other considerations in the multiobjective optimization (such as Pareto optimization [22] [23] or fuzzy evaluation [ 24 ]) seems an interesting subject that can enrich the proposed approach and give scientific benefits.”
- A research survey: review of flexible job shop scheduling techniques (2016, methodology): “The method was first developed by Kantorovich (1940) for use during World War II to plan expenditures and returns in order to reduce costs to the army and increase losses to the enemy.”