A multi-objective ring star vehicle routing problem for perishable items

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

doi:10.1007/s12652-021-03059-2

Cited by 16 publications

(13 citation statements)

References 30 publications

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“…A multi-objective model of the capacitated VRP for perishable goods is proposed in [5]. The objectives of this model are to minimize the quality degradation of goods and to minimize the delivery costs.…”

Section: Related Workmentioning

confidence: 99%

“…Constraint (4) forms the sub-tour elimination constraint with v(S) being an appropriate lower bound on the number of required vehicles for this problem. Finally, Constraint (5) forms the integrity constraint defining x ij .…”

Section: Problem Statement and Complexitymentioning

confidence: 99%

“…alike. In literature, one can identify several works that provide a multi-objective approach (e.g., [5,16,30]). However, none of them also integrates such real-world constraints as mentioned before, which highly complicates the finding of solutions.…”

Section: Practical Implicationsmentioning

confidence: 99%

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Tackling the rich vehicle routing problem with nature-inspired algorithms

Lesch

König²,

Kounev

et al. 2022

Appl Intell

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In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Statement and Complexitymentioning

confidence: 99%

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Tackling the rich vehicle routing problem with nature-inspired algorithms

Lesch

König²,

Kounev

et al. 2022

Appl Intell

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show abstract

“… 2021 ) and uncertainty (Ning and Su 2017 ), and modelling techniques such as MP (Guo et al 2022 ) and multiobjective programming (Barma et al. 2022 ). Additionally, more algorithms can be applied to solving OVRP, such as bio-inspired routing algorithm (Ramamoorthy and Thangavelu 2022 ), ant colony optimization (Ramamoorthy and Thangavelu 2022a ) and genetic algorithm (Katoch et al.…”

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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“…Thus, they improved the approximate Pareto fronts produced by NSGA-II. Barma et al (2021) studied a multiobjective ring star VRP for perishable items. Although their study is not directly considered as a GVRP, the motivation behind their objective functions aims to reduce the negative impacts of the vehicles on the environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Bi‐objective green vehicle routing problem

Erdoğdu

Karabulut

2021

Int Trans Operational Res

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The green vehicle routing problem (GVRP) is a variant of the vehicle routing problem (VRP), which increasingly attracts many researchers in recent years due to the growing global environmental issues. As the transportation of the products grows, the number of vehicles in fleets and the pollutants caused by these vehicles also grow, which in turn negatively affects human health. In this paper, a biobjective GVRP was studied. The two objectives are minimizing the total distance and minimizing the total fuel consumption of all vehicle routes. As a solution method, an adaptive large neighborhood search was hybridized with two new local search heuristics. The proposed method was applied to two well‐known benchmark problem sets for VRPs and new approximate Pareto fronts were obtained for these benchmark sets.

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A multi-objective ring star vehicle routing problem for perishable items

Cited by 16 publications

References 30 publications

Tackling the rich vehicle routing problem with nature-inspired algorithms

Tackling the rich vehicle routing problem with nature-inspired algorithms

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

Bi‐objective green vehicle routing problem

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