An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem

By Luca Maria Gambardella, Marco Dorigo. INFORMS journal on computing, 2000

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/21d51d11da77c2d0ce831846763411bb4d85416d

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Citations in the corpus

An improvement heuristic framework for the laser cutting tool path problem (2015, methodology): “These solutions are obtained through a modified insertion construction heuristic followed by a Tabu search metaheuristic (Glover and Laguna 1999) using generic thee-opt moves modified to take the relevant precedence constraints into account (Gambardella and Dorigo 2000).”
Time dependent vehicle routing problem with a multi ant colony system (2008, background): “…as discussed in [5], and have been successfully applied to the traveling salesman problem, symmetric and asymmetric ([4], [6], [7], [12]) the quadratic assignment problem ([14]), graph-coloring problem ([11]), job-shop/flow-shop ([10]), sequential ordering ([13]), vehicle routing ([8], [9], [15]).”
Optimal Ordered Problem Solver (2002, methodology): “ invoke variants of Evolutionary Computation (Rechenberg, 1971, Schwefel, 1974), Genetic Algorithms (Holland, 1975), Genetic Programming (Cramer, 1985, Banzhaf et al., 1998), Ant Colony Optimization (Gambardella and Dorigo, 2000, Dorigo et al., 1999), etc. 4. Use oops, which invokes Try, to bias-opt”
Ant colony optimization: Introduction and recent trends (2005, background): “The recent book by Dorigo and Stützle gives a more comprehensive description [36].”
Ant colony optimization theory: A survey (2005, background): “Among other applications, ACO algorithms are currently state-of-the-art for solving the sequential ordering problem (SOP) [28], the resource constraint project scheduling (RCPS) problem [51], and the open shop scheduling (OSS) problem [4].”
Artificial intelligence in supply chain management: theory and applications (2010, methodology): “Due to its past success in handling NP-hard problems, the ant colony optimisation algorithm has been employed to solve TSP, vehicle routing problems, sequential ordering problems, and process plan selection problems within the SC framework (Gambardella and Dorigo 2000, Tiwari et al. 2006).”
The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances (2003, methodology): “In some of thes e applications, ACO algorithms have obtained world class performance, whic h is the case, for example, for quadratic assignment [62, 85], sequential ord ering [43, 44], vehicle routing [45], scheduling [21, 66] or packet-switched netwo rk routing [25].”