Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions

Abdoli, Behroz; MirHassani, S. A.; Hooshmand, F.

doi:10.1007/s11356-017-9740-8

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…Other than single-objective, researchers that try to develop GVRP concepts also use multi-objective. The example of this multi-objective are minimizing fuel consumption and optimizing customer satisfaction [63], minimizing fuel consumption and carbon emission [64,65,66], minimizing total cost and carbon emission [67,68,69,70,71], minimizing total travel distance and carbon emission [72,74,78,80] or fuel consumption [73,79,81], minimizing total travel distance and energy consumed [75,76,82], minimizing cost and air pollution [87], and improving energy efficiency and customer satisfaction [77].…”

Section: Gvrp With Multi-objectivementioning

confidence: 99%

Heterogeneous Fleet Green Vehicle Routing Problem: A Literature Review

Normasari

Lathifah

2021

Angkasa

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Transportation, as a part of the supply chain process, contributes to carbon emission which leads to climate change and global warming. This environmental issue gives an impact to decisions regarding the supply chain of a company. One way to deal with this issue is by analyzing their vehicle routing problem. In this study, the issue about routing problems in green supply chain by considering the heterogeneous fleet is being discussed. One variant of Green Vehicle Routing Problem (GVRP) reviewed in this paper is about Heterogeneous Alternative Fuel Vehicles for Green Vehicle Routing Problem (HAFVGVRP). The purpose of this study is to review the development of GVRP with heterogeneous alternative fuel vehicles and the gap or state-of-the-art on existing researches. The review was classified according to the objectives, type of fleet, and solution used. Moreover, this study also presents the trend and direction of further research.

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Section: Gvrp With Multi-objectivementioning

confidence: 99%

Heterogeneous Fleet Green Vehicle Routing Problem: A Literature Review

Normasari

Lathifah

2021

Angkasa

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“…The road transport industry needs effective methods and policies to reduce GHG emissions as the environment gets worse. The GVRP helps the logistics companies to develop their distributions with achieving the goal of reducing GHG emissions (Abdoli et al, 2017). Lin et al (2014) conducted a literature review on the GVRP and pointed out that the vehicle scheduling problem and the VRP needs to be addressed.…”

Section: Literature Reviewmentioning

confidence: 99%

A green vehicle routing model based on modified particle swarm optimization for cold chain logistics

Yan

Lim

Tseng

2019

IMDS

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Purpose This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions. Design/methodology/approach This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case. Findings The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning. Research limitations/implications There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions. Originality/value Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

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“…A simulation-based restricted dynamic programming, which incorporated a weighted random sampling, and a restricted dynamic programming heuristic and simulation, was developed to solve large sized problems. Abdoli et al [5] studied the green VRP to reduce greenhouse gas emissions while considering the necessity of alternative fuel vehicles to refuel at alternative fueling stations when travelling long distances. A mixed integer programming model was presented, to show that the utilization of alternative fuel vehicles could reduce greenhouse gas emissions significantly.…”

Section: Literature Reviewmentioning

confidence: 99%

“…VRP was first introduced by Dantzig and Ramser [4], and the main objectives of the problem are to minimize the total travelling cost, time, or distance with a fleet of vehicles, starting and ending their routes at the depot while satisfying the various demands of customers [4]. Since the introduction, different types of VRP have been tackled to incorporate real-world issues [5]. A comprehensive overview of the problem, variants, practical issues, formulations, and solution methods can be found in Toth and Vigo [6], Laporte [7], Pillac et al [8], and Toth and Vigo [9].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Kang

Lee

2018

Symmetry

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The vehicle routing problem (VRP) is a challenging combinatorial optimization problem. This research focuses on the problem under which a manufacturer needs to outsource materials from other suppliers and to ship the materials back to the company. Heterogeneous vehicles are available to ship the materials, and each vehicle has a limited loading capacity and a limited travelling distance. The purpose of this research is to study a multiple vehicle routing problem (MVRP) with soft time window and heterogeneous vehicles. Two models, using mixed integer programming (MIP) and genetic algorithm (GA), are developed to solve the problem. The MIP model is first constructed to minimize the total transportation cost, which includes the assignment cost, travelling cost, and the tardiness cost, for the manufacturer. The optimal solution can present multiple vehicle routing and the loading size of each vehicle in each period. The GA is next applied to solve the problem so that a near-optimal solution can be obtained when the problem is too difficult to be solved using the MIP. A case of a food manufacturing company is used to examine the practicality of the proposed MIP model and the GA model. The results show that the MIP model can obtain the optimal solution under a short computational time when the scale of the problem is small. When the problem becomes non-deterministic polynomial hard (NP-hard), the MIP model cannot find the optimal solution. On the other hand, the GA model can obtain a near-optimal solution within a reasonable amount of computational time. This paper is related to several important topics of the Symmetry journal in the areas of mathematics and computer science theory and methods. In the area of mathematics, the theories of linear and non-linear algebraic structures and information technology are adopted. In the area of computer science, theory and methods, and metaheuristics are applied.Unit of measure: min.Symmetry 2018, 10, 650 9 of 20 Table 6 lists the problem configurations for the three cases, including the number of periods, number of suppliers, number of vehicles, size of the transportation network, number of variables for the MIP, and number of constraints for the MIP. In the first case, there are five periods, the number of suppliers is five, the number of vehicles is three, the transportation network is 6 × 6, and the numbers of variables and constraints for the MIP model are 931 and 1171, respectively. In the second case, there are seven periods, the number of suppliers is nine, the number of vehicles is four, the transportation network is 10 × 10, and the numbers of variables and constraints for the MIP model are 3903 and 3977, respectively. In the third case, there are nine periods, the number of suppliers is 12, the number of vehicles is five, the transportation network is 13 × 13, and the numbers of variables and constraints for the MIP model are 9758 and 9919, respectively.

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Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions

Cited by 17 publications

References 20 publications

Heterogeneous Fleet Green Vehicle Routing Problem: A Literature Review

Heterogeneous Fleet Green Vehicle Routing Problem: A Literature Review

A green vehicle routing model based on modified particle swarm optimization for cold chain logistics

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

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