New benchmark instances for the Capacitated Vehicle Routing Problem

Uchôa, Eduardo; Pecin, Diego; Pessoa, Artur Alves; Poggi, Marcus; Vidal, Thibaut; Subramanian, Anand

doi:10.1016/j.ejor.2016.08.012

Cited by 329 publications

(287 citation statements)

References 27 publications

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“…In particular, the dataset used is from Uchoa et al [13]. This repository contains 100 data files with the number of cities and various demands required by each city.…”

Section: Resultsmentioning

confidence: 99%

“…Design of experiment (DOE) will be carried out to identify the parameters and the levels for each parameter to reduce the bias. The data for the algorithms will be obtained from CVRPLib [13], an online benchmark repository of VRP instances. CVRPLib contains data that is prominently used in VRP literature with number of cities or customers that request goods to be delivered to them and the location and the size of the demand.…”

Section: Research Approachmentioning

confidence: 99%

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Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Cooray

Rupasinghe

2017

Journal of Industrial Engineering

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During the last decade, tremendous focus has been given to sustainable logistics practices to overcome environmental concerns of business practices. Since transportation is a prominent area of logistics, a new area of literature known as Green Transportation and Green Vehicle Routing has emerged. Vehicle Routing Problem (VRP) has been a very active area of the literature with contribution from many researchers over the last three decades. With the computational constraints of solving VRP which is NPhard, metaheuristics have been applied successfully to solve VRPs in the recent past. This is a threefold study. First, it critically reviews the current literature on EMVRP and the use of metaheuristics as a solution approach. Second, the study implements a genetic algorithm (GA) to solve the EMVRP formulation using the benchmark instances listed on the repository of CVRPLib. Finally, the GA developed in Phase 2 was enhanced through machine learning techniques to tune its parameters. The study reveals that, by identifying the underlying characteristics of data, a particular GA can be tuned significantly to outperform any generic GA with competitive computational times. The scrutiny identifies several knowledge gaps where new methodologies can be developed to solve the EMVRPs and develops propositions for future research.

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“…In particular, the dataset used is from Uchoa et al [13]. This repository contains 100 data files with the number of cities and various demands required by each city.…”

Section: Resultsmentioning

confidence: 99%

Section: Research Approachmentioning

confidence: 99%

Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Cooray

Rupasinghe

2017

Journal of Industrial Engineering

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“…We considered the 100 recent benchmark instances of Uchoa et al (2017), as these instances remain highly challenging for metaheuristics and cover a larger variety of instance size and characteristics: demand and customer distribution, depot location, and route length. For each instance, we ran the method 20 times with different random seeds.…”

Section: Preliminary Experiments With a Simple Local Searchmentioning

confidence: 99%

“…It max = 50000 to evaluate UHGS-BS in the same conditions as UHGS on this set of instances (Uchoa et al 2017). A hard runtime limit of 24 hours was imposed for each run.…”

Section: Comparison With Recent State-of-the-art Algorithmsmentioning

confidence: 99%

“…For the particular case of the instance X-n256-k16, one interesting characteristic was observed: only 16 vehicles are used, with a total capacity usage of 99.6%. Table 2: Results for the instances with up to 331 customers from Uchoa et al (2017) Different techniques were proposed and evaluated for efficiently searching in space S B k : neighborhood reduction, dynamic move filters, concatenation techniques and efficient memory structures. Moreover, tunneling techniques were employed to reshape S B k into a search space more similar to S A as the search progresses.…”

Section: Comparison With Recent State-of-the-art Algorithmsmentioning

confidence: 99%

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Heuristics for vehicle routing problems: Sequence or set optimization?

Toffolo

Vidal

Wauters

2019

Computers & Operations Research

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We investigate a structural decomposition for the capacitated vehicle routing problem (CVRP) based on vehicle-to-customer "assignment" and visits "sequencing" decision variables.We show that an heuristic search focused on assignment decisions with a systematic optimal choice of sequences (using Concorde TSP solver) during each move evaluation is promising but requires a prohibitive computational effort. We therefore introduce an intermediate search space, based on the dynamic programming procedure of Balas & Simonetti, which finds a good compromise between intensification and computational efficiency. A variety of speed-up techniques are proposed for a fast exploration: neighborhood reductions, dynamic move filters, memory structures, and concatenation techniques. Finally, a tunneling strategy is designed to reshape the search space as the algorithm progresses.The combination of these techniques within a classical local search, as well as in the unified hybrid genetic search (UHGS) leads to significant improvements of solution accuracy. New best solutions are found for surprisingly small instances with as few as 256 customers. These solutions had not been attained up to now with classic neighborhoods. Overall, this research permits to better evaluate the respective impact of sequence and assignment optimization, proposes new ways of combining the optimization of these two decision sets, and opens promising research perspectices for the CVRP and its variants.

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Leveraging single‐objective heuristics to solve bi‐objective problems: Heuristic box splitting and its application to vehicle routing

2019

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After decades of intensive research on the vehicle routing problem (VRP), many highly efficient single-objective heuristics exist for a multitude of VRP variants. But when new side-objectives emerge-such as service quality, workload balance, pollution reduction, consistency-the prevailing approach has been to develop new, problem-specific, and increasingly complex multiobjective (MO) methods. Yet in principle, MO problems can be efficiently solved with existing single-objective solvers. This is the fundamental idea behind the well-known -constraint method (ECM). Despite its generality and conceptual simplicity, the ECM has been largely ignored in the domain of heuristics and remains associated mostly with exact algorithms. In this article, we dispel these preconceptions and demonstrate that -constraintbased frameworks can be a highly effective way to directly leverage the decades of research on single-objective VRP heuristics in emerging MO settings. KEYWORDS box algorithm, epsilon-constraint, metaheuristics, multiobjective, vehicle routingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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New benchmark instances for the Capacitated Vehicle Routing Problem

Cited by 329 publications

References 27 publications

Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Heuristics for vehicle routing problems: Sequence or set optimization?

Leveraging single‐objective heuristics to solve bi‐objective problems: Heuristic box splitting and its application to vehicle routing

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