A hybrid multi-objective evolutionary algorithm for open vehicle routing problem through cluster primary-route secondary approach

Kar, Samarjit; Dutta, Joydeep; Barma, Partha Sarathi; Mukherjee, Anupam; De, Tanmay

doi:10.1080/17509653.2021.2000901

Cited by 22 publications

(15 citation statements)

References 38 publications

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“…Moreover, no prior studies have examined these real life costs in the literature. For instance; Dasdemir et al [29] considered the minimization of the total travelled distance, Dutta et al [30] studied minimizing the operating costs and minimizing the carbon emission due to fuel consumption by the service vehicles, Hashemi et al [28] considered the minimization of the total transportation costs, Niu et al [36] studied on OVRP to minimize the total cost of fuel emissions cost and the driver wages, etc. Therefore, this study presents a new and precise model for companies that meet real life costs such as standard routing cost, stop by cost, and deviation cost.…”

Section: Discussionmentioning

confidence: 99%

“…Dasdemir et al [29] studied a real life OVRP with overbooking, multiple objectives and unknown initial pickup points. Dutta et al [30] proposed a model for OVRP which considers two conflicting realistic objectives: minimizing the operating costs and minimizing the carbon emission due to fuel consumption by the service vehicles. To solve the multi-objective problem Strength Pareto Evolutionary Algorithm (SPEA2) and Non-dominated Sorting-based Genetic Algorithm (NSGA-II) was studied.…”

Section: Introductionmentioning

confidence: 99%

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Modeling open vehicle routing problem with real life costs and solving via hybrid civilized genetic algorithm

TONBUL

2024

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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Many companies prefer to use third party logistics firms to deliver their goods and as such planning the return of the vehicles to the depot is not required. This is called open vehicle routing problem (OVRP). In literature, the OVRP is handled with minimum distance as objective function like vehicle routing problem. But in the real world, the objective function achieves minimum many costs like standard routing cost, stopping by cost and the deviation cost. The standard routes are previously defined under free market conditions by third party logistic firms. The deviation from the standard route is required to arrive cities which are not on the standard route. The stop by cost occurs on the delivery points. In this paper mentioned three costs are considered in the objective function while many papers consider only distance related costs in the literature. This paper proposes a new mathematical model for the OVRP. In the constraints, the last points of the routes are researched in detail. The standard route costs are determined by considering the last point of the route. Because of the NP-hard structure of the OVRP, the proposed mathematical model is solved with a hybrid metaheuristic called Civilized Genetic Algorithm (CGA). CGA is developed by hybridizing a modified genetic algorithm and a local search algorithm. The application of this study is implemented for the delivery routing of a combi boiler producer in Turkey. The third party logistic firms may use this proposed model and the solution approach for the real life applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling open vehicle routing problem with real life costs and solving via hybrid civilized genetic algorithm

TONBUL

2024

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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“…Therefore, the main idea is that although there is no guarantee of finding the optimal solution, genetic algorithms can obtain excellent feasible solutions (close to the optimal) with less computational effort than exact methods. Many works in the literature show the efficiency of the genetic algorithms applied to different extensions of the VRP (see, for instance, Krajcar et al, 1995;Jozefowiez et al, 2007aJozefowiez et al, , 2007bPham et al, 2017;Jiang et al, 2022;Dutta et al, 2022).…”

Section: Genetic Algorithmmentioning

confidence: 99%

A flow‐based model for the multivehicle covering tour problem with route balancing

Ota,

Fiorotto,

Ghidini

et al. 2023

Int Trans Operational Res

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This paper introduces a new flow‐based mathematical formulation for the multivehicle covering tour problem. This problem determines a set of vehicle routes for the available fleet; therefore, the total distance traveled is minimal. In the proposed mathematical formulation, each vertex represents a location of interest, and these vertices belong to three sets: vertices that can be visited; vertices that must be visited; and vertices that must be covered, in the sense that each vertex must be close to a visited vertex in a route. The proposed formulation aims to find solutions with balanced routes (all the routes with an equal or similar number of visited nodes) and extends the previous formulations proposed in the literature by considering a two‐index vehicle flow formulation with a decision variable to find the balanced routes. It constitutes an NP‐hard integer programming problem based on a two‐commodity flow model, which is solved by applying the branch‐and‐cut and biased random‐key genetic algorithms. This paper reports computational results for some adapted instances from TSPLIB to evaluate the efficiency of the proposed model. The computational results validate the branch‐and‐cut and genetic algorithms, whereby the latter performs better in large‐size instances with reduced computational runtime. Finally, the used approaches obtained competitive solutions compared to the literature.

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“…By now, many literatures on VRP have emerged, such as open VRP (Dutta et al. 2022a ) and green VRP (Dutta et al. 2022b ).…”

Section: Related Workmentioning

confidence: 99%

Uncertain multilevel programming with application to omni-channel vehicle routing problem

Gao

Ralescu

2022

J Ambient Intell Human Comput

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Multilevel programming is widely applied to solve decentralized decision-making problems. In practice, indeterminacies are presented in these problems due to volatile factors or emergencies. As a type of indeterminacy, uncertainty is introduced in multilevel programming. For resolving multilevel programming problems with uncertain parameters, this paper constructs the uncertain expected value multilevel programming model and chance-constrained multilevel programming. Then, these models are converted to their equivalent forms. Moreover, the Stackelberg-Nash equilibrium solutions are obtained by using a genetic algorithm. Finally, these models are applied to the omni-channel vehicle routing problem, and a numerical experiment is given. The numerical experiment shows that the established models can optimize the distribution efficiency by coordinating the interests of decision-makers.

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A hybrid multi-objective evolutionary algorithm for open vehicle routing problem through cluster primary-route secondary approach

Cited by 22 publications

References 38 publications

Modeling open vehicle routing problem with real life costs and solving via hybrid civilized genetic algorithm

Modeling open vehicle routing problem with real life costs and solving via hybrid civilized genetic algorithm

A flow‐based model for the multivehicle covering tour problem with route balancing

Uncertain multilevel programming with application to omni-channel vehicle routing problem

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