Evaluating Hyperheuristics and Local Search Operators for Periodic Routing Problems

Chen, Yujie; Mourdjis, Philip; Polack, Fiona; Cowling, Peter I.; Remde, Stephen

doi:10.1007/978-3-319-30698-8_8

Cited by 11 publications

(13 citation statements)

References 22 publications

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“…Also the CVRP with time windows is one of HyFlex (hyper-heuristic flexible framework) problem domains, for which newly developed approaches in hyper-heuristics are tested [65]. Other VRP problems solved using selection hyper-heuristics include the periodic VRP [17], dial a ride [64], urban transit routing [1], and inventory routing [34].…”

Section: Selection Hyper-heuristicsmentioning

confidence: 99%

“…Constraints 7and (17) establish the relationship between the routing (x t ijk and z t ijs ) and service, that is, delivery (w t i ) and installation (y t i ) variables for the vehicles and technicians respectively. According to constraints (7), if the location of a request is visited by a vehicle, then the machines ordered by this request are delivered between the first (e i ) and the last (l i ) days the delivery can be done.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…According to constraints (7), if the location of a request is visited by a vehicle, then the machines ordered by this request are delivered between the first (e i ) and the last (l i ) days the delivery can be done. Similarly, according to constraints (17), if the location of a request is visited by a technician, then the installation that is ordered by this request is done at least 1 day after the earliest day that the request can be delivered.…”

Section: Mathematical Modelingmentioning

confidence: 99%

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Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

et al. 2020

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This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper-heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real-world size revealed the success of the hyper-heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper-heuristic framework.

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Section: Selection Hyper-heuristicsmentioning

confidence: 99%

Section: Mathematical Modelingmentioning

confidence: 99%

Section: Mathematical Modelingmentioning

confidence: 99%

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Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

et al. 2020

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show abstract

“…Recently, Drake et al studied the high‐level results using domain‐independent knowledge and domain‐specific knowledge for a multidimensional knapsack problem (MKP). Chen et al also introduced the advantages of hyperheuristic when solving periodic routing problems compared with meta‐heuristic and hybrid heuristic algorithms. However, an open problem is how to easily apply this idea in practice for new domains, for example, in solving problems in software engineering.…”

Section: Related Workmentioning

confidence: 99%

“…It is a conceptual framework where different solutions in the low level and selection strategy in the high level should be designed based on problem details. The hyperheuristic technique has been used to solve various optimization problems, such as the machine assignment problem and the periodic routing problem . In 2002, Cowling et al integrated GA into the hyperheuristic strategy, which combines different global search algorithms to solve optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Concrete hyperheuristic framework for test case prioritization

Bian

Zheng

Guo

et al. 2018

J Software Evolu Process

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Test case prioritization (TCP), which aims to find the optimal test case execution sequences for specific testing objects, has been widely used in regression testing. A wide variety of search methodologies and algorithms have been proposed to optimize test case execution sequences, namely, search‐based TCP. However, different algorithms perform differently and have different implementation costs and specific situations where an algorithm usually performs with high effectiveness and efficiency. When facing a new testing scenario, it is actually difficult to decide which algorithm is suitable. In this paper, to address the algorithm selection problem for different test scenarios, a more generally applicable algorithm based on a hyperheuristic strategy is proposed for search‐based TCP. This includes a range of multiobjective algorithms with a variety of crossover strategies and a learning agent strategy to evaluate and select the appropriate algorithm execution sequence dynamically for different scenarios. The concrete hyperheuristic framework for multiobjective TCP is presented with an algorithm's repository in the low level and the learning agent strategy in the higher level. Experiments show that the proposed learning agent strategy can accurately evaluate algorithms in multiobjective problems and select the appropriate algorithm in each iteration.

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An improved hybrid genetic search with data mining for the CVRP

Maia,

Plastino,

Souza

2024

Networks

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The hybrid genetic search (HGS) metaheuristic has produced outstanding results for several variants of the vehicle routing problem. A recent implementation of HGS specialized to the capacitated vehicle routing problem (CVRP) is a state‐of‐the‐art method for this variant. This paper proposes an improved HGS for the CVRP obtained by incorporating a new solution generation method into its (re‐)initialization process to guide the search more efficiently and effectively. The solution generation method introduced in this work combines an approach based on frequent patterns extracted from good solutions by a data mining process and a randomized version of the Clarke and Wright savings heuristic. As observed in our experimental comparison, the proposed method significantly outperforms the original algorithm regarding the final gap to the best known solutions and the primal integral.

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Evaluating Hyperheuristics and Local Search Operators for Periodic Routing Problems

Cited by 11 publications

References 22 publications

Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

Concrete hyperheuristic framework for test case prioritization

An improved hybrid genetic search with data mining for the CVRP

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