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

Kheiri, Ahmed; Ahmed, Leena; Boyacı, Burak; Gromicho, Joaquim; Mumford, Christine L.; Özcan, Ender; Dirikoç, Ali Selim

doi:10.1002/net.21962

Cited by 6 publications

(3 citation statements)

References 66 publications

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“…A similar quality of results was obtained with a set partitioning matheuristic "Columnwise neighborhood search" [59]. Competitive results were also obtained with a population hyper-heuristic [60] and by using an iterated local search using several neighborhood search operators and destroy/repair heuristics [61].…”

Section: Solving Technician Routing Problemsmentioning

confidence: 67%

Matheuristics and Column Generation for a Basic Technician Routing Problem

Dupin

Parize²,

Talbi

2021

Algorithms

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This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

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Section: Solving Technician Routing Problemsmentioning

confidence: 67%

Matheuristics and Column Generation for a Basic Technician Routing Problem

Dupin

Parize²,

Talbi

2021

Algorithms

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“…Therefore, the optimal solution can only be obtained when the number of customers is small, and the transportation network is simple. In order to improve the efficiency and quality of the solution, as well as obtain an approximate or satisfactory solution of the problem at a reasonable computational cost, exact algorithms are generally combined with other methods when solving the problem [154][155][156][157].…”

Section: Exact Algorithmsmentioning

confidence: 99%

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

Tang

2023

Sustainability

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The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP.

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“…The motivation behind the development of a sequencebased selection hyper-heuristic (SSHH) for the nurse rostering problem is to build a more forceful search approach of the solutions' space. SSHH is one of the first hyper-heuristics that employs sequences of heuristics, while its promising performance gains have been verified over a wide set of problem domains, such as school timetabling [13], vehicle routing [14,15], inventory routing [16], wind farm layout optimisation [17], and urban transit routing [18] among others. The proposed SSHH algorithm utilises a specific construction procedure to generate an initial feasible solution and exploit the search power of a set of nine dedicated low-level heuristics to effectively search the optimisation landscape and improve the initial solution while respecting feasibility.…”

Section: Introductionmentioning

confidence: 99%