RLC_ACS: An Improved Ant Colony Algorithm for VRPSDP

Zhang, Tao; Tian, Wen-xin; Zhang, Yuejie; Zheng, Xue-Chao

doi:10.1109/icmlc.2007.4370284

Cited by 1 publication

(1 citation statement)

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“…Different types of metaheuristics have been used to solve a wide range of stochastic COPs. The following examples illustrate the use of different metaheuristics for solving stochastic VRPs: Ant Colony Optimization (Dorigo, 1992;Bianchi et al, 2006;Zhang, 2007); Evolutionary Computation and Genetic Algorithms (Holland, 1973;Bianchi et al, 2006;Tan et al, 2007;Ismail, 2008;Shanmugam et al, 2011); Iterated Local Search (Lourenço et al, 2003;Bianchi et al, 2006); Large Neighbourhood Search (Shaw, 1998;Lei et al, 2011); Particle Swarm Optimization (Kennedy and Eberhart, 1995;Shanmugam et al, 2011;Moghaddam et al, 2012;Marinakis et al, 2013); Scatter Search (Glover, 1977;Zhang et al, 2012); Simulated Annealing (Kirkpatrick et al, 1983;Teodorovic and Pavkovic, 1992;Bianchi et al, 2006); and Tabu Search (Glover, 1986;Gendreau et al, 1996a, b;Bianchi et al, 2006;Ismail, 2008;Shen et al, 2009). The reader is referred to Bianchi et al (2009) for a thorough survey of metaheuristics applied to other stochastic COPs.…”

Section: Solving Stochastic Cops Through Metaheuristicsmentioning

confidence: 99%

SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization

Grasas

Juan

Lourenço

2016

Journal of Simulation

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Iterated Local Search (ILS) is one of the most popular single-solution-based metaheuristics. ILS is recognized by many authors as a relatively simple yet efficient framework able to deal with complex combinatorial optimization problems (COPs). ILS-based algorithms have been successfully applied to provide near-optimal solutions to different COPs in logistics, transportation, production, etc. However, ILS is designed to solve COPs under deterministic scenarios. In some real-life applications where uncertainty is present, the deterministic assumption makes the model less accurate since it does not reflect the real stochastic nature of the system. This paper presents the SimILS framework that extends ILS by integrating simulation to be able to cope with Stochastic COPs in a natural way. The paper also describes several tested applications that illustrate the main concepts behind SimILS and give rise to a new brand of ILS-based algorithms.

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