A developmental solution to (dynamic) capacitated arc routing problems using genetic programming

Weise, Thomas; Devert, Alexandre; Tang, Ke

doi:10.1145/2330163.2330278

Cited by 31 publications

(29 citation statements)

References 21 publications

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“…Weise et al [22] proposed a GPHH for evolving heuristics for static CARP and stochastic CARP with variable task sets. In their approach, GP was used to evolve a heuristic function that will be used to rank the tasks in the waiting list of a vehicle while deciding which task the vehicle should serve next.…”

Section: Related Workmentioning

confidence: 99%

“…A GPHH consists of two key components [22]: (1) a training set and (2) a meta-algorithm. We develop a new framework of GPHH, as described as follows:…”

Section: Genetic Programming Hyper-heuristic For Ucarpmentioning

confidence: 99%

“…Due to the computational e ciency, it is particularly e ective in dynamic problems where immediate response is required. Weise et al [22] proposed a Genetic Programming Hyper-Heuristic (GPHH) to evolve routing policy for static CARP and tested on the uncertain CARP with unknown task set, i.e. stochastic presence of tasks.…”

Section: Introductionmentioning

confidence: 99%

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Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem

Liu¹,

Yi²,

Zhang³

et al. 2021

Preprint

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Uncertain Capacitated Arc Routing Problem (UCARP) is a variant of the well-known CARP. It considers a variety of stochastic factors to reflect the reality where the exact information such as the actual task demand and accessibilities of edges are unknown in advance. Existing works focus on obtaining a robust solution beforehand. However, it is also important to design effective heuristics to adjust the solution in real time. In this paper, we develop a new Genetic Programming-based Hyper-Heuristic (GPHH) for automated heuristic design for UCARP. A novel effective meta-algorithm is designed carefully to address the failures caused by the environment change. In addition, it employs domain knowledge to filter some infeasible candidate tasks for the heuristic function. The experimental results show that the proposed GPHH significantly out performs the existing GPHH methods and manually designed heuristics. Moreover, we find that eliminating the infeasible and distant tasks in advance can reduce much noise and improve the efficacy of the evolved heuristics. In addition, it is found that simply adding a slack factor to the expected task demand may not improve the performance of the GPHH. © 2017 ACM . This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/3071178.3071185.

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Section: Related Workmentioning

confidence: 99%

“…A GPHH consists of two key components [22]: (1) a training set and (2) a meta-algorithm. We develop a new framework of GPHH, as described as follows:…”

Section: Genetic Programming Hyper-heuristic For Ucarpmentioning

confidence: 99%

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Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem

Liu¹,

Yi²,

Zhang³

et al. 2021

Preprint

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“…It is shown that the evolved MAS significantly outperforms the hyper-heuristic. In [38] genetic programming is used to as a developmental solution to dynamic capacitated arc routing problems. A heuristic is evolved that indirectly controls a vehicle that has to travel over the road network.…”

Section: Evolutionary Designed Multi-agent Systemsmentioning

confidence: 99%

“…1) Improved validation: We believe that the work that was presented in [36]- [38] would benefit greatly from an improved evaluation. We plan to extend the work of [37] and validate it thoroughly on one or more benchmarks.…”

Section: B Future Workmentioning

confidence: 99%

Evolved multi-agent systems and thorough evaluation are necessary for scalable logistics (position paper)

Lon

Holvoet

2013

2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS)

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We consider logistics problems that are both dynamic and potentially large scale. A common way to create a scalable solution for logistics problems is to use multi-agent systems. In this paper we take two positions of a different nature: (1) evolutionary designed multi-agent systems are a promising approach to create scalable and performant solutions for logistics problems, (2) the multi-agent systems field does not prioritize evaluation enough, which hinders thorough scientific comparisons and prevents adoption in industry. We present arguments and refute common counterarguments for our position. Further, we discuss our present and upcoming efforts to realize our position.

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An Improved Genetic Programming Hyper-Heuristic for the Uncertain Capacitated Arc Routing Problem

MacLachlan

Mei

Branke

et al. 2018

AI 2018: Advances in Artificial Intelligence

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A developmental solution to (dynamic) capacitated arc routing problems using genetic programming

Cited by 31 publications

References 21 publications

Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem

Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem

Evolved multi-agent systems and thorough evaluation are necessary for scalable logistics (position paper)

An Improved Genetic Programming Hyper-Heuristic for the Uncertain Capacitated Arc Routing Problem

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