Vehicle Routing Optimization Problem: A Study on Capacitated Vehicle Routing Problem

Praveen, V.; Keerthika, Dr.P.; Sivapriya, G.; Sarankumar, A.; Bhasker, Boddu

doi:10.1016/j.matpr.2022.05.185

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…Fleet capacity: Capacitated VRP (CVRP) optimizes routes considering vehicle capacity limits such as weight, volume, and number of parcels [40,41]. Sometimes, however, these limits can be ignored for mail delivery, where the parcel volumes are so small compared with fleet capacity, and perishable item delivery, when vehicles cannot wait for the full load of items [42].…”

Section: Nature Of Lmd Problemsmentioning

confidence: 99%

A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas

Jazemi,

Alidadiani,

Ahn

et al. 2023

Applied Sciences

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Logistics has long been important in an industrial society. Compared with the traditional structure of distribution, which requires freight to be delivered mostly to warehouses or retail stores, customers now often prefer packages to be delivered to their residences, especially after the delivery challenges during the COVID-19 pandemic. The delivery of parcels to urban residential areas increases the challenge due to the amount of delivery volume, tight delivery schedules, and continuously changing delivery conditions. Last-mile delivery tries to address the challenges, taking advantage of the available automation, sensor and communication technologies, and people’s attitudes toward parcel delivery for the benefit of all stakeholders. Various approaches to last-mile delivery have been proposed and analyzed in the literature. This paper reviews the recent literature on vehicle routing for last-mile delivery. The review identified four major categories: crowdshipping, parcel lockers, delivery by sidekicks, and delivery to optional points. The nature of the problems is discussed in five aspects: fleet capacity, time window, fleet option, dynamism of input, and stochastic parameters. The review identifies the achievements and limitations of the research in the areas and proposes a future research agenda.

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Section: Nature Of Lmd Problemsmentioning

confidence: 99%

A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas

Jazemi,

Alidadiani,

Ahn

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…2 The total cost, distance traveled, and other parameters are considered on a demand basis. 3 Praveen et al, novel Particle Positioning Particle Swarm Optimization (P 3 SO) approach is designed to predict the robust route with the elimination of nonlinearity measures. The service time, movement of the vehicles, and status of the move towards a particular direction are comes under the linearity measure category.…”

Section: Introductionmentioning

confidence: 99%

“…When some requests from dynamic customers arise during the execution of the delivery schedule, the manager must determine whether arrived request is included in the service over the remaining time, vehicles, dynamic customers' location, and time windows 2 . The total cost, distance traveled, and other parameters are considered on a demand basis 3 . Praveen et al, novel Particle Positioning Particle Swarm Optimization (

{P}^3 SO

) approach is designed to predict the robust route with the elimination of nonlinearity measures.…”

Section: Introductionmentioning

confidence: 99%

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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“…It has been applied to various fields, and various VRP variants have been gradually developed, such as the capacitated vehicle routing problem (CVRP), vehicle routing problem with time window (VRPTW), green vehicle routing problem (GVRP), and multi distribution center vehicle routing problem (MDCVRP). Due to the restricted load capacity of vehicles, it is imperative to take into account capacity limitations when solving the VRP [9][10][11][12][13][14][15][16]. In the process of waste collection and transportation, in order to complete the work on time and efficiently, it is necessary to consider the constraints of the time window when optimizing the route [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

Li,

Deng,

et al. 2023

Sustainability

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In recent years, the waste produced as a result of the production and consumption activities of urban residents has led to significant environmental degradation and resource wastage. This paper focuses on the research object of municipal solid waste (MSW) collection and transportation based on the concept of “sustainable development and green economy”. Firstly, this study examines the current state of urban domestic garbage collection and transportation. It analyzes the following challenges and deficiencies of the existing collection and transportation system: (1) the operating efficiency of garbage collection vehicles is low, resulting in a significant accumulation of waste on the roadside and within the community; (2) the vehicle collection and transportation routes are fixed, and there are empty vehicles running; (3) the amount of garbage on a route exceeds the vehicle’s loading capacity, which requires the vehicle to perform a second round of collection and transportation. To enhance the efficiency of urban garbage collection and transportation and minimize the collection and transportation costs, we are investigating the problem of optimizing the path for green vehicles. To comprehensively optimize the fixed cost, variable cost, and carbon emission cost incurred during vehicle operation, a vehicle routing model with time windows is established, taking into account vehicle load constraints. Carbon emission coefficient and carbon tax parameters are introduced into the model and the “fuel-carbon emission” conversion method is used to measure the carbon cost of enterprises. An improved ant colony optimization (ACO) method is proposed: (1) the introduction of a vehicle load factor improves the ant state transfer method; (2) the updated pheromone method is improved, and additional pheromone is added to both the feasible path and the path with the minimum objective function; (3) the max–min ACO algorithm is introduced to address the issue of premature convergence of the algorithm; (4) the embedding of a 2-opt algorithm further prevents the ACO algorithm from falling into the local optimum. Finally, the calculation results based on the example data demonstrate that the algorithm has a significant advantage over the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The total transportation distance determined by this algorithm is shorter than that of the GA and PSO methods, and the total cost of the scheme is 1.66% and 1.89% lower than that determined by GA and PSO, respectively. Compared to the data from the actual case, the number of vehicles required in the operation of this algorithm and model is reduced by three. Additionally, the total cost, fixed cost, and carbon emission cost incurred by the vehicles during operation were reduced by 31.2%, 60%, and 25.3% respectively. The results of this study help the station to collect and distribute waste efficiently, while also achieving the goals of energy saving, consumption reduction, and emission reduction.

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Vehicle Routing Optimization Problem: A Study on Capacitated Vehicle Routing Problem

Cited by 10 publications

References 28 publications

A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas

A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas

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

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

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