A Bumble Bees Mating Optimization algorithm for the Open Vehicle Routing Problem

Marinakis, Yannis; Marinaki, Magdalene

doi:10.1016/j.swevo.2013.12.003

Cited by 52 publications

(19 citation statements)

References 63 publications

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“…The open vehicle routing problem is studied in which the vehicle does not need to return to the depot after serving the last customer point. The results show that the hybrid ant colony optimization algorithm is an effective method for solving the open vehicle routing problem (Marinakis and Marinaki, 2014) [15]. It is due to the current environmental requirements with multipopulation metaheuristics for solving vehicle routing problems (Osaba and Díaz, 2016) [16].…”

Section: Vehicle Routing Optimizationmentioning

confidence: 99%

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Liu

Shan

Zhang

et al. 2018

Mathematical Problems in Engineering

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Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.

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Section: Vehicle Routing Optimizationmentioning

confidence: 99%

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Liu

Shan

Zhang

et al. 2018

Mathematical Problems in Engineering

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“…This, together with the fact that the Hamiltonian cycle problem (HCP) is NP-hard and can be reduced to the Hamiltonian path problem (HPP) [11], allows us to conclude that the shortest HPP is NP-hard. Consequently, the OVRP is also an NP-hard problem, justifying the development of heuristics and meta-heuristics (see [12], where a new swarm intelligence approach is proposed). The vehicle routing with backup provisioning, under discussion here, can be seen as a variant of the OVRP applied to the transportation of persons, considering multiple depots and having the possibility of backup provision to certain critical stops.…”

Section: State Of the Artmentioning

confidence: 99%

Planning of Vehicle Routing with Backup Provisioning Using Wireless Sensor Technologies

2017

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Wireless sensor technologies can be used by intelligent transportation systems to provide innovative services that lead to improvements in road safety and congestion, increasing end-user satisfaction. In this article, we address vehicle routing with backup provisioning, where the possibility of reacting to overloading/overcrowding of vehicles at certain stops is considered. This is based on the availability of vehicle load information, which can be captured using wireless sensor technologies. After discussing the infrastructure and monitoring tool, the problem is mathematically formalized, and a heuristic algorithm using local search procedures is proposed. Results show that planning routes with backup provisioning can allow fast response to overcrowding while reducing costs. Therefore, sustainable urban mobility, with efficient use of resources, can be provided while increasing the quality of service perceived by users.

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“…Therefore, many heuristic approaches have been developed to solve the problem. Among the most important heuristic algorithms used to solve the OVRP are those in [32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem

et al. 2020

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This paper presents a hybrid grasshopper optimization algorithm using a novel decoder and local search to solve instances of the open vehicle routing problem with capacity and distance constraints. The algorithm's decoder first defines the number of vehicles to be used and then it partitions the clients, assigning them to the available routes. The algorithm performs a local search in three neighborhoods after decoding. When a new best solution is found, every route is locally optimized by solving a traveling salesman problem, considering the depot and clients in the route. Three sets containing a total of 30 benchmark problems from the literature were used to test the algorithm. The experiments considered two cases of the problem. In the first, the primary objective is to minimize the total number of vehicles and then the total distance to be traveled. In the second case, the total distance traveled by the vehicles is minimized. The obtained results showed the algorithm's proficient performance. For the first case, the algorithm was able to improve or match the best-known solutions for 21 of the 30 benchmark problems. For the second case, the best-known solutions for 18 of the 30 benchmark problems were found or improved by the algorithm. Finally, a case study from a real-life problem is included.

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A Bumble Bees Mating Optimization algorithm for the Open Vehicle Routing Problem

Cited by 52 publications

References 63 publications

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Planning of Vehicle Routing with Backup Provisioning Using Wireless Sensor Technologies

A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem

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