Ant colony optimization techniques for the vehicle routing problem

By J. Bell, Patrick R. McMullen. Advanced Engineering Informatics, 2004

770 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/d2af1253d2529b9406eced6e2dfc51fb2cb12226

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Citations in the corpus

Smart Charging Strategy for Electric Vehicle Charging Stations (2018, methodology): “Using the Little law at each charging station for three types of charging options, the mean waiting time at each queue can be given as [37]–[39]”
Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food (2014, methodology): “These metaheuristics include Genetic Algorithms (GA), Tabu Search (TS), Simulated Annealing (SA), Ant Colony Optimization (ACO), Scatter Search (SS), and so forth (Bell and McMullen, 2004; Ho et al., 2008; Kuo and Wang, 2012; Tang et al., 2010; Tavakkoli-Moghaddam et al., 2006).”
Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, methodology): “ACO was applied to the CVRP by Bullnheimer et al. (1999), Bell and Mc Mullen (2004), Doerner et al. (2004), Reimann et al. (2004) and Yu et al. (2009), among others.”
A Human-Centered Approach to Algorithmic Services: Considerations for Fair and Motivating Smart Community Service Management that Allocates Donations to Non-Profit Organizations (2017, background): “They also optimize how resources are allocated in public services and urban planning, determining the locations of shared bicycle racks and electronic car charging stations or the routes of self-driving vehicles [2, 4, 23].”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “The performance may be worse with other methods, say SA [27].”