Selected Genetic Algorithms for Vehicle Routing Problem Solving

Ochelska-Mierzejewska, Joanna; Poniszewska-Marańda, Aneta; Maranda, Witold

doi:10.3390/electronics10243147

Cited by 26 publications

(15 citation statements)

References 38 publications

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“…To solve an NPhard problem avoiding complex and time-consuming computations, several metaheuristics can be used. In recent years, the scope of the application of fuzzy set theory [44] and genetic algorithms for solving routing problems [37,[45][46][47] have grown significantly.…”

Section: Optimisation Methods In Distribution Systemsmentioning

confidence: 99%

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

et al. 2023

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A city’s transport and distribution system requires the effective organization of urban freight deliveries that take into consideration the economic, ecological, and social impact. Implementation of the concept of green logistics necessitates the use of environmentally friendly and energy-efficient modes of transport, which consider the existing infrastructure and the possibility of its development. The aim of this work was to facilitate the transfer to rail transport, using the existing tramway infrastructure, those shipments which previously entered the city center by road. The paper proposes a simulation model for small consignments delivered by freight trams in the city of Poznań, Poland. Operational planning is carried out in conditions of uncertainty and risk. The uncertainty is due to the stochastic nature of the demand for products. The risk is characterized by the probability that, due to technological and technical limitations, a given customer will not be fully served from the distribution center. The authors provide a decision support tool for evaluating the possible locations of tram depots and for route planning. The assignment of the routes, with the criterion of minimizing the costs for the carrier, and with a limitation on tram operating time, is based on the use of genetic algorithms, which makes it possible to obtain a solution that is close to optimal, within a reasonable time period. As a result of a series of simulation experiments and statistical data processing, the distribution laws and expected average values of the technological parameters were determined for the functioning of the city transport and distribution system under conditions of uncertainty and risk.

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Section: Optimisation Methods In Distribution Systemsmentioning

confidence: 99%

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

et al. 2023

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“…Replace old population with the new one 9: until convergence or maximum number of iterations is reached Fujdiak et al [50] and Ikram et al [51] use GAs applied to the smart waste management problem. Ochelska-Mierzejewska et al [52] discuss the usage of GAs for solving the routing problem.…”

Section: Genetic Algorithms (Gas)mentioning

confidence: 99%

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Gatti,

Barbierato,

Pozzi

2024

Electronics

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This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.

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“…), we strive to achieve certain goals (e.g., shortest total vehicle miles driven, lowest total transportation costs, vehicles arriving at a certain time, minimum number of vehicles used, and so on.) [68][69][70][71].…”

Section: Modeling Capacitated Vehicle Routingmentioning

confidence: 99%

A Novel Multistrategy-Based Differential Evolution Algorithm and Its Application

Jin-yin¹,

Shang

Jing

et al. 2022

Electronics

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To address the poor searchability, population diversity, and slow convergence speed of the differential evolution (DE) algorithm in solving capacitated vehicle routing problems (CVRP), a new multistrategy-based differential evolution algorithm with the saving mileage algorithm, sequential encoding, and gravitational search algorithm, namely SEGDE, is proposed to solve CVRP in this paper. Firstly, an optimization model of CVRP with the shortest total vehicle routing is established. Then, the saving mileage algorithm is employed to initialize the population of the DE to improve the initial solution quality and the search efficiency. The sequential encoding approach is used to adjust the differential mutation strategy to legalize the current solution and ensure its effectiveness. Finally, the gravitational search algorithm is applied to calculate the gravitational relationship between points to effectively adjust the evolutionary search direction and further improve the search efficiency. Four CVRPs are selected to verify the effectiveness of the proposed SEGDE algorithm. The experimental results show that the proposed SEGDE algorithm can effectively solve the CVRPs and obtain the ideal vehicle routing. It adopts better search speed, global optimization ability, routing length, and stability.

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Selected Genetic Algorithms for Vehicle Routing Problem Solving

Cited by 26 publications

References 38 publications

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

A Novel Multistrategy-Based Differential Evolution Algorithm and Its Application

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