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dbr:Large-scale_capacitated_arc_routing_probem
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Large-scale capacitated arc routing probem
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A large-scale capacitated arc routing problem (LSCARP) is a variant of the capacitated arc routing problem that covers 300 or more edges to model complex arc routing problems at large scales. Yi Mei et al. published an algorithm for solving the large-scale capacitated arc routing problem using a cooperative co-evolution algorithm. LSCARP can be solved with a divide and conquer algorithm applied to route cutting off decomposition. The LSCARP can also be solved with an iterative local search that improves on upper and lower bounds of other methods.
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A large-scale capacitated arc routing problem (LSCARP) is a variant of the capacitated arc routing problem that covers 300 or more edges to model complex arc routing problems at large scales. Yi Mei et al. published an algorithm for solving the large-scale capacitated arc routing problem using a cooperative co-evolution algorithm. LSCARP can be solved with a divide and conquer algorithm applied to route cutting off decomposition. The LSCARP can also be solved with an iterative local search that improves on upper and lower bounds of other methods. An LSCARP algorithm has been applied to waste collection in Denmark with a fast heuristic named FAST-CARP. The algorithm is also often referred to as the Time Capacitated Arc Routing Problem often referred to as TCARP. The TCARP can be solved with a metaheuristic in a reasonable amount of time. The TCARP often arises when volume constraints do not apply for example meter reading Zhang et al. created a metaheuristic to solve a generalization named the large-scale multi-depot CARP (LSMDCARP) named route clustering and search heuristic. An algorithm for the LSCARP named Extension step and statistical filtering for large-scale CARP (ESMAENS) was developed in 2017. The LSCARP can be extended to a large-scale capacitated vehicle routing problem with a hierarchical decomposition algorithm. The LSCVRP can be solved with an evolutionary method based on local search. Solving the LSCVRP can be done by integrated support vector machines and random forest methods. An algorithm to solve LSCARP based on simulated annealing named FILO was developed by Accorsi et al.
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