Two metaheuristic approaches for solving the multi-compartment vehicle routing problem

Yahyaoui, Hiba; Kaabachi, Islem; Krichen, Saoussen; Dekdouk, Abdulkader

doi:10.1007/s12351-018-0403-4

Cited by 22 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Klodawski et al [15] generated a simulation model of VRP with dynamic information in the FlexSim. Yahyaoui et al [16] proposed a GA based on partially matched crossover to solve the multi-compartment vehicle routing problem in oil delivery and found that solutions generated by the proposed algorithm on all involved standard cases were optimal. On the basis of the above research results, several types of VRP optimization models have been built.…”

Section: State Of the Artmentioning

confidence: 99%

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Wang¹,

Huang²

2022

Int. j. simul. model.

View full text Add to dashboard Cite

To minimize the total time for the distribution of relief commodities participated by both private companies and the government, a vehicle routing problem (VRP) model in emergencies was proposed. Considering the differences in the starting points of vehicles, the VRP of general logistics, and departments of vehicles, constraints, such as vehicle capacity limitation and time windows, were introduced into the model, which was close to meeting the practical demands of emergency relief. A hybrid code genetic algorithm (HCGA) was proposed, and it used dynamic mutations to avoid early traps in local optimization and to accelerate convergence. This algorithm was programmed by MATLAB. Furthermore, the vehicle routing optimization plans in an emergency was calculated by a simple genetic algorithm (SGA) and the HCGA, respectively. Results demonstrate that the total time for relief distribution in the HCGA is 11.62 % lower and the calculation time is 14.24 % shorter than that of the SGA. The HCGA is not only convenient in processing the constraints of the model and the natural description of problem solutions, but it is also effective in improving the complexity.

show abstract

Section: State Of the Artmentioning

confidence: 99%

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Wang¹,

Huang²

2022

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…Differential Evolution (DE) algorithm with a triangular mutation operator is proposed to solve the optimization problem [22] and applied to the stochastic programming problems [23]. Many researchers presented the applications of metaheuristic algorithms in different types of problems such as unconstrained function optimization [24], vehicle routing problems [25][26][27], machine scheduling [28,29], mine production schedules [30], project selection [31], soil science [32], feature selection problem [33,34], risk identification in supply chain [35] etc. For constrained optimization problems, Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) was presented and compared to other metaheuristic algorithms [36].…”

Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Agrawal¹,

Alnowibet²,

Mohamed³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are solved using GSK algorithm and the results are compared with eight other state-of-the-art metaheuristic algorithms. The obtained results are also compared with the optimal global solution and the results quoted in the literature. To investigate the performance of the GSK algorithm on a real-world problem, a solid stochastic fixed charge transportation problem is examined, in which the parameters of the problem are considered as random variables. The obtained results show that the GSK algorithm outperforms other algorithms in terms of convergence, robustness, computational time, and quality of obtained solutions.

show abstract

“…The algorithm stops when it gets an individual that solves the problem, has the main fitness utility or when the population converges, it means not find a better individual for many generations [3]. Promising results for GA are found in several problems, such as the metaheuristic approach to the multi-compartment vehicle routing problem (MCVRP) using voracious and genetic algorithm [4] and in the optimization of routes of trained vehicles [5].…”

Section: Motivationmentioning

confidence: 99%

Genetic algorithm for the strategic location of a network of fire hydrants

Saldaña

Honorio

Gutiérrez

2021

Ingeniare. Rev. chil. ing.

View full text Add to dashboard Cite

In Trujillo, a norther city from Peru, the number of fire hydrants is currently 497; only 10% are working out in the center of this city. Faced with this situation, the firefighters do not attend in optimum time the various emergencies happen, making possible the increase of material damages and victims due to the lack of water supply caused by the inoperativeness hydrants and also a non-optimum distribution. In this research, a network of hydrants was strategically located through the design and output of a genetic algorithm, GA. There are many solutions, and only one individual must be selected, the most efficient. It was evaluated by the fitness function about the length from a common point to others where the hydrants are, and the best solution was determined by applying genetic operators like crossover and mutation, which means the location points of the hydrants on the map of the city. The result shows a very good solution for a hydrant network; in addition, the number of hydrants that make up the network with the average distance of the network that reduce the time to attend an emergency.The result shows a very good solution for a hydrant network; the number of hydrants that make up the network with the average distance of the network reduces the time to attend an emergency. It will be useful to redistribute the hydrants for better locations.

show abstract

Two metaheuristic approaches for solving the multi-compartment vehicle routing problem

Cited by 22 publications

References 30 publications

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

A Hybrid Code Genetic Algorithm for VRP in Public-Private Emergency Collaborations

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Genetic algorithm for the strategic location of a network of fire hydrants

Contact Info

Product

Resources

About