Solving dynamic TSP with evolutionary approach in real time

Zhou, Aimin; Kang, Lishan; Yan, Zhen

doi:10.1109/cec.2003.1299769

Cited by 25 publications

(2 citation statements)

References 6 publications

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“…Dynamics in this family of problems arise then mainly on travel and service times (Eyckelhof and Snoek, 2002), although in some variants, not all customers need to be visited (those unvisited imply penalties for the deliverer instead). To handle those dynamics (i.e., on arrival and departure times at customers, and then on the capability to respect all time constraints), various methods are proposed, evolutionary algorithms being the most popular (Zhou et al, 2003;Liu and Kang, 2003;Osaba et al, 2012). Also, ant colonies optimization shows a high number of works, learning procedures being used to handle real-time optimization to reduce computation times (Strak et al, 2019).…”

Section: Unexpected Eventsmentioning

confidence: 99%

Operations research for planning and managing city logistics systems

Crainic¹,

González-Feliu²,

Ricciardi³

et al. 2023

Handbook on City Logistics and Urban Freight

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Section: Unexpected Eventsmentioning

confidence: 99%

Operations research for planning and managing city logistics systems

Crainic¹,

González-Feliu²,

Ricciardi³

et al. 2023

Handbook on City Logistics and Urban Freight

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show abstract

“…A lot of algorithms have been proposed to solve TSP [3][4][5][6][7][8]. Some of them (based on dynamic programming or branch and bound methods) provide the global optimum solution.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Genetic Algorithm and its Application in Traveling Salesman Problem

Liu

Yan

2012

AEF

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Abstract. The traveling salesman problem (TSP) is one of the most widely studied NP-hard combinatorial optimization problems. Its statement is deceptively simple, and yet it remains one of the most challenging problems and traditional genetic algorithm trapped into the local minimum easily for solving this problem. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of adaptive orthogonal local search to prevent local convergence to form a new orthogonal genetic algorithm. The new algorithm shows great efficiency in solving TSP with the problem scale under 300 under the experiment results analyze.

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A New Approach to Solving Dynamic Traveling Salesman Problems

Yang

Kang

2006

Lecture Notes in Computer Science

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Solving dynamic TSP with evolutionary approach in real time

Cited by 25 publications

References 6 publications

Operations research for planning and managing city logistics systems

Operations research for planning and managing city logistics systems

Orthogonal Genetic Algorithm and its Application in Traveling Salesman Problem

A New Approach to Solving Dynamic Traveling Salesman Problems

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