Hybrid Genetic and Simulated Annealing Algorithm for Capacitated Vehicle Routing Problem

Sajid, Mohammad; Jafar, Asmi; Sharma, S.K.

doi:10.1109/pdgc50313.2020.9315798

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…It is also mandatory that the capacity limit of each vehicle must be observed, and the demand of every customer is fulfilled by one vehicle without splitting the demand. The vehicle always begins and ends the journey at the central depot [3,20].…”

Section: Bi-objective Capacitated Vehicle Routing Problemmentioning

confidence: 99%

“…As shown in Figure 2, the proposed routing algorithm initially sets the maximum iteration count, and the population P t of η solutions (chromosomes) is generated randomly. After initialization, the fitness values of all chromosomes are computed, i.e., the values of the overall distance traveled and longest route are computed using a cluster-first routesecond approach [3,20]. Next, the population P t is sorted and decomposed into Pareto fronts using a non-dominated sorting approach.…”

Section: Nsga-ii-based Routing Algorithmmentioning

confidence: 99%

“…To preserve the genetic diversity and to avoid the precipitate convergence or local optima, the mutation operator is used, which may be based on different operators depending on the problem. The proposed NSGA-II-based routing algorithm employs swap, exchange, and two-opt* operators stochastically to perform the mutation operation [3,20]. One of the three local operators is selected to perform the mutation according to the mutation probability.…”

Section: Mutation Operatormentioning

confidence: 99%

“…The NSGA-II_NNX, NSGA-II_GTBCX, and NSGA-II_EAX utilize nearest-neighbour crossover (NNX), giant tour best cost route crossover (GTBCX), and edge assembly crossover (EAX) operators, respectively. The EAX operator was proposed by Y. Nagata [21], while the NNX operator is a greedy crossover operator that works based on nearest neighbour heuristics [3,20]. The three versions of NSGA-II were developed using Python 3.9.6 on DESKTOP-L9O54PJ, Intel(R) Core™ i7-8700, Windows-10.…”

Section: Simulation Studymentioning

confidence: 99%

“…The performance of GTBCX is evaluated against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) crossover operators [3,20,21].…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Novel Algorithm for Capacitated Vehicle Routing Problem for Smart Cities

Sajid

Singh

Haidri³

et al. 2021

Symmetry

Self Cite

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Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.

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Section: Bi-objective Capacitated Vehicle Routing Problemmentioning

confidence: 99%

Section: Nsga-ii-based Routing Algorithmmentioning

confidence: 99%

Section: Mutation Operatormentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

“…The performance of GTBCX is evaluated against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) crossover operators [3,20,21].…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Novel Algorithm for Capacitated Vehicle Routing Problem for Smart Cities

Sajid

Singh

Haidri³

et al. 2021

Symmetry

Self Cite

View full text Add to dashboard Cite

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Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

Praveen¹,

Periasamy

Gurusamy³

et al. 2023

Concurrency and Computation

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SummaryElectric vehicles (EVs) are drastically growing, and their emergence is more popular. Moreover, with the constraint driving range, the batteries of these vehicles are rechargeable throughout the routes in various situations. Here, a new problem is formulated with the inclusion of both battery swapping and partial recharging decisions. The energy level and the routing directions are more important, and the investigators intend to handle these issues effectually. However, the major constraint is routing and time window. Here, a heuristic model (LTS‐RSS) is designed to find the best matching solution. A novel Random Sub‐Space (RSS) and local Tabu search (LTS) are modeled to handle these issues. The probabilistic model of RSS is anticipated by integrating the consequences of time windows and distances. The experimentation is done with an online database and used for performance validation. The outcomes show that the newly modeled (LTS‐RSS) approach enhances the significance of the model. The outcomes of all the instances with diverse strategies enhance the model's robustness and stability for resolving these issues. The empirical analysis is done with MATLAB 2020b simulator, and metrics like optimization of routing solution, the best vehicle, best distance, and numbers of vehicles are evaluated, and the outcomes are compared with various other approaches.

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