A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem

Yahyaoui, Hiba; Krichen, Saoussen; Dekdouk, Abdelkader

doi:10.4018/ijamc.2018040104

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…In this paper, the authors suggest a method that combines GRASP and Tabu search. When GRASP is completely constructing a new solution, the authors apply the tabu search procedure to optimize this solution to improve more the best solution found by GRASP, a simple tabu search algorithm is supposed to avoid the restrictions of local optimality ( Yahyaoui et., 2018 ;Yang et al, 2013).…”

Section: General Grasp-tb Methodsmentioning

confidence: 99%

GRASP-Tabu Search Algorithms for the Route Planning Problem in Spatial Crowdsourcing

Bouatouche

Belkadi

2022

International Journal of Applied Metaheuristic Computing

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With the speedy progress of mobile devices, a lot of commercial enterprises have exploited crowdsourcing as a useful approach to gather information to develop their services. Thus, spatial crowdsourcing has appeared as a new platform in e-commerce and which implies procedures of requesters and workers. A requester submits spatial tasks request to the workers who choose and achieve them during a limited time. Thereafter, the requester pays only the worker for the well accomplished the task. In spatial crowdsourcing, each worker is required to physically move to the place to accomplish the spatial task and each task is linked with location and time. The objective of this article is to find an optimal route to the worker through maximizing her rewards with respecting some constraint, using an approach based on GRASP with Tabu. The proposed algorithm is used in the literature for benchmark instances. Computational results indicate that the proposed and the developed algorithm is competitive with other solution approaches.

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Section: General Grasp-tb Methodsmentioning

confidence: 99%

GRASP-Tabu Search Algorithms for the Route Planning Problem in Spatial Crowdsourcing

Bouatouche

Belkadi

2022

International Journal of Applied Metaheuristic Computing

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“…Thus the authors, integrated metaheuristic algorithm with machine learning algorithm for optimal prediction of weather. Some of the metaheuristic algorithms used for various applications are penguins search optimization algorithm for community detection (Guendouz, M., Amine, A., & Hamou, R. M. 2018), Genetic algorithm for vehicle routing problem (Yahyaoui, H., Krichen, S., & Dekdouk, A. 2018), Particle swarm optimization for task scheduling (Yin, P. Y., Yu, S. S., Wang, P. P., & Wang, Y. T. 2006), diabetes prediction using metaheuristic algorithm (Parameswaran, P., & Moorthy, R. S. 2019), provisioning of analytics as a service (Moorthy, R. S., & Pabitha, P. 2019) etc.. Also various metaheuristic algorithms studied in (Moorthy, R. S., & Pabitha, P. 2018, August) are analyzed and Whale Optimization algorithm is chosen to integrate with K-Nearest Neighbor (K-NN) for optimal prediction of weather.…”

Section: Introductionmentioning

confidence: 99%

An Optimal K-Nearest Neighbor for Weather Prediction Using Whale Optimization Algorithm

Moorthy

Pabitha

2021

International Journal of Applied Metaheuristic Computing

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The weather has a serious impact on the environment as it affects to change day to day life. In recent days, many algorithms were proposed to predict the weather. Although various machine learning algorithms predict the weather, the optimal prediction of weather is not addressed. Optimal Prediction of weather is required as it has a serious impact on human life. Thus this domain invites an optimal system that can forecast weather thereby saving human life. To optimally predict the changes in weather, a metaheuristic algorithm called Whale Optimization Algorithm (WOA) is integrated with machine learning algorithm K- Nearest Neighbor (K-NN). Whale optimization is an algorithm inspired by the social behavior of whales. The proposed WOAK-NN is compared with K-NN. The integration of WOA with K-NN aims to maximize accuracy, F-measure and minimize mean absolute error. Also, the time complexity of WOAK-NN is compared with K-NN and observed that when the dataset is large, WOAK-NN requires minimum time for an optimal prediction.

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“…With the increase of model complexity, the solving algorithms of the VRP become increasingly diverse. The algorithms roughly fall into three categories: precise algorithms, traditional heuristic algorithms, and modern heuristic algorithms [16][17][18]. For instance, Hanafi et al [19] presented a sweep algorithm to optimize the distribution routes in the capacitated VRP (CVRP).…”

Section: Introductionmentioning

confidence: 99%

Collaborative Distribution in the Soft Time Window of Agricultural-Means Supply Chain Based on Simulated Annealing-Genetic Algorithm

Zhu¹,

Wang²,

Li³

2020

JESA

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This paper mainly explores the collaborative distribution to multiple customers at the terminal of agricultural-means supply chain (AMSC). Firstly, a cost optimization model for collaborative distribution constrained by time window was constructed based on fuzzy appointment time function. Next, the proposed model was solved by simulated annealing-genetic algorithm (SA-GA). Through a case study, the cost optimization model constrained by customer satisfaction was compared with that not constrained by customer satisfaction. The results show that the cost optimization model constrained by customer satisfaction made the customers more satisfied without greatly elevating the distribution cost. The research results shed new light on the collaborative distribution of time-sensitive agricultural-means (AM) products, and the management of the AMSC.

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A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem

Cited by 4 publications

References 10 publications

GRASP-Tabu Search Algorithms for the Route Planning Problem in Spatial Crowdsourcing

GRASP-Tabu Search Algorithms for the Route Planning Problem in Spatial Crowdsourcing

An Optimal K-Nearest Neighbor for Weather Prediction Using Whale Optimization Algorithm

Collaborative Distribution in the Soft Time Window of Agricultural-Means Supply Chain Based on Simulated Annealing-Genetic Algorithm

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