A bi-objective latency based vehicle routing problem using hybrid GRASP-NSGAII algorithm

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

doi:10.1080/17509653.2022.2076168

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The approach was applied on vehicle problems for minimizing the travel time as a objective function. Barma et al 41 tested a newly developed hybrid optimization techniques namely, greedy randomized adaptive search procedure (GRASP) and NSGA‐II on vehicle for bi‐objective solution based on dormancy to obtain optimal solution. Hao et al 42 utilized a hybrid technique named estimation of distribution algorithm (EDA) combined with TLBO for solving multi‐objective problem in order to improve the searching ability and to easily cope up with the uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Chaotic‐quasi‐opposition based whale optimization technique applied to multi‐objective complementary scheduling of grid connected hydro‐thermal–wind–solar‐electric vehicle system

Paul,

Roy,

Mukherjee

2024

Optim Control Appl Methods

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The sources of fossil fuel are impoverishing in upcoming future. In the current research scenario, sincere effort has been taken worldwide to explore the use of renewable energy sources in electrical power system for the economic benefits and environmental consciousness. The main contribution of the proposed work is first, to find optimal hydro‐thermal scheduling (HTS) with wind, solar, and electric vehicles (EVs) for variable load. The target is to find out maximum utilization of renewable energy sources for economic power generation with less emission. Thus, a new approach of EV to grid has been adopted with wind–solar based HTS system for improving grid reliability and resilience. Second, there is a requirement to overcome the local optima problems with less convergence speed. This is obtained by employing a relatively new methodology, known as chaotic‐quasi‐opposition‐based whale optimization algorithm (WOA) (CQOWOA). The proposed algorithm is tested on HTS and wind–solar‐electric vehicle‐based HTS (HTWSVS) for three different cases. Different nonlinearities like valve point effect of thermal units, transmission losses, spillage rate of hydro reservoir units and uncertainties of wind, solar as well as EV are considered to judge the effectiveness of the proposed CQOWOA technique on realistic problems. The presence of wind, solar, and EV energy sources with HTS is evident from the test results of CQOWOA, for multi‐objective problem where cost and emission both are reduced significantly. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.025%. The comparative analysis of CQOWOA with the other optimization techniques validates its superiority on both the test systems by minimizing the generation cost and emission.

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Section: Introductionmentioning

confidence: 99%

Chaotic‐quasi‐opposition based whale optimization technique applied to multi‐objective complementary scheduling of grid connected hydro‐thermal–wind–solar‐electric vehicle system

Paul,

Roy,

Mukherjee

2024

Optim Control Appl Methods

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“…For example, Barma et al proposed a bi-objective CVRP based on priority for two types of customers and developed a hybrid metaheuristic algorithm based on the greedy randomized adaptive search procedure and nondominated sorting genetic algorithm to solve the model; they then analyzed the results according to three scenarios of average delay calculation based on customer type. The results showed that the hybrid GRASP-NSGAII algorithm outperformed the NSGAII algorithm, and the proposed model could be extended to more than two types of customers with different priorities [8]; A hybrid multi-objective evolutionary algorithm through a cluster primary route/secondary approach was proposed by Dutta et al to solve the multiobjective open green vehicle routing problem in sustainable environments, which uses the extended strength Pareto evolutionary algorithm (SPEA2) and nondominated sorting-based genetic algorithm (NSGA-II) to search for the best sub-route, and then uses the VIKOR method to identify the decision maker's choice-based solution for each cluster [9]. The effectiveness and feasibility of the proposed model were demonstrated through numerical examples and statistical analysis, with SPEA2 showing better performance than NSGA-II; this study provided a novel and effective solution for the open green vehicle routing problem.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

Tang

2023

Sustainability

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The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP.

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GRASP Algorithm for the Green Capacitated Vehicle Routing Problem (GCVRP)

Oumachtaq,

Ouzizi,

Douimi

2024

Lecture Notes in Networks and Systems

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A bi-objective latency based vehicle routing problem using hybrid GRASP-NSGAII algorithm

Cited by 4 publications

References 29 publications

Chaotic‐quasi‐opposition based whale optimization technique applied to multi‐objective complementary scheduling of grid connected hydro‐thermal–wind–solar‐electric vehicle system

Chaotic‐quasi‐opposition based whale optimization technique applied to multi‐objective complementary scheduling of grid connected hydro‐thermal–wind–solar‐electric vehicle system

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

GRASP Algorithm for the Green Capacitated Vehicle Routing Problem (GCVRP)

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