A Simulated Annealing Algorithm Based Solution Method for a Green Vehicle Routing Problem with Fuel Consumption

Karagül, Kenan; Şahin, Yusuf; Aydemir, Erdal; Oral, Aykut

doi:10.1007/978-3-319-97511-5_6

Cited by 42 publications

(27 citation statements)

References 55 publications

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“…It can be noted that several papers are dealing with fuel consumption and pollution emissions and not AFVs, but the term green VRP is used to indicate a more ecologically aware routing problem [62,139].…”

Section: Green and Pollution Routing Problemmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

175

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In order to ensure high-quality and on-time delivery in logistic distribution processes, it is necessary to efficiently manage the delivery fleet. Nowadays, due to the new policies and regulations related to greenhouse gas emission in the transport sector, logistic companies are paying higher penalties for each emission gram of CO2/km. With electric vehicle market penetration, many companies are evaluating the integration of electric vehicles in their fleet, as they do not have local greenhouse gas emissions, produce minimal noise, and are independent of the fluctuating oil price. The well-researched vehicle routing problem (VRP) is extended to the electric vehicle routing problem (E-VRP), which takes into account specific characteristics of electric vehicles. In this paper, a literature review on recent developments regarding the E-VRP is presented. The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models. Several variants of the E-VRP and related problems are observed. To cope with the new routing challenges in E-VRP, efficient VRP heuristics and metaheuristics had to be adapted. An overview of the state-of-the-art procedures for solving the E-VRP and related problems is presented.

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Section: Green and Pollution Routing Problemmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

175

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“…Similarly, atoms is getting balance which is steady state condition in each iteration temperature. Finally, internal energy of the material is down minimum point because of the system heat's catch the environmental temperature (Lin and Fei, 2012;Şahin and Eroğlu, 2014;Karagul et al, 2019). Osman (1993) developed a hybrid simulated annealing algorithm with the tabu search algorithm for solving a CVRP.…”

Section: Simulated Annealing (Sa) Algorithmmentioning

confidence: 99%

“…Furthermore, there are some variants such as VRP with heterogenous fleet size (Taillard, 1993;Salhi et al, 2014;Coelho et al, 2016;Penna et al, 2019), open VRP (Tarantilis et al, 2004;Letchford et al, 2007;Tyasnurita et al, 2017), rich VRP (Hartl et al, 2006;Koç et al, 2018;Sim et al, 2019), fuzzy VRP (Brito et al, 2009;Cao and Lai, 2010;Kuo et al, 2016), and swap-body VRP (Huber and Geiger, 2017;Absi et al, 2017). Nowadays, VRP with green transportation strategy has been attractively studied with minimizing the carbon emissions on routing (Erdogan and Miller-Hooks, 2012;Bektas et al, 2016;Ene et al, 2016;Koç and Karaoglan, 2016;Montoya et al, 2016;Karagul et al, 2019;Koç et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Solving a Periodic Capacitated Vehicle Routing Problem using Simulated Annealing Algorithm for a Manufacturing Company

Aydemir

Karagül

2020

BJO&PM

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Goal: This paper aims to implement a periodic capacitated vehicle routing problem with simulated annealing algorithm using a real-life industrial distribution problem and to recommend it to industry practitioners. The authors aimed to achieve high-performance solutions by coding a manually solved industrial problem and thus solving a real-life vehicle routing problem using Julia language and simulated annealing algorithm. Design / Methodology / Approach: The vehicle routing problem (VRP) that is a widely studied combinatorial optimization and integer programming problem, aims to design optimal tours for a fleet of vehicles serving a given set of customers at different locations. The simulated annealing algorithm is used for periodic capacitated vehicle routing problem. Julia is a state-of-art scientific computation language. Therefore, a Julia programming language toolbox developed for logistic optimization is used. Results: The results are compared to savings algorithms from Matlab in terms of solution quality and time. It is seen that the simulated annealing algorithm with Julia gives better solution quality in reasonable simulation time compared to the constructive savings algorithm. Limitations of the investigation: The data of the company is obtained from 12 periods with a history of four years. About the capacitated vehicle routing problem, the homogenous fleet with 3000 meters/vehicle is used. Then, the simulated annealing design parameters are chosen rule-of-thumb. Therefore, better performance can be obtained by optimizing the simulated annealing parameters. Practical implications: In this study, a furniture roving parts manufacturing company that have 30 customers in Denizli, an industrial city in the west part of Turkey, is investigated. Before the scheduling implementation with Julia, the company has no effective and efficient planning as they have been using spreadsheet programs for vehicle scheduling solutions. In this study, the solutions with Julia are used in practice for the distribution with higher utilization rate and minimum number of vehicles. The simulated annealing and savings algorithms are compared in terms of solution time and performance. The savings algorithm has produced better solution time, the simulated annealing approach has minimum total distance objective value, minimum number of required vehicles, and maximum vehicle utilization rate for the whole model. Thus, this paper can contribute to small scale business management in the sense of presenting a digitalization solution for the vehicle scheduling solution. Also, Julia application of simulated annealing for vehicle scheduling is demonstrated that can help both academicians and practitioners in organizations, mainly in logistics and distribution problems. Originality / Value: The main contribution of this study is a new solution method to capacitated vehicle routing problems for a real-life industrial problem using the advantages of the high-level computing language Julia and a meta-heuristic algorithm, the simulated annealing...

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“…Consequently, experts develop heuristic and metaheuristic algorithms to solve this problem. Several metaheuristics algorithms have been studied in GVRP to minimize fuel consumption, such as Simulated Annealing (SA) (Eydi & Alavi, 2018;Felipe et al, 2014;Karagul et al, 2019;Koç & Karaoglan, 2016;Kuo, 2010;Normasari et al, 2019;Yasin & Vincent, 2013), Ant Colony Optimization (ACO) (Jabir et al, 2017;Zhang et al, 2019), and Revised Hybrid Intelligent Algorithm (Wang et al, 2018). Particle Swarm Optimization (PSO) (Norouzi et al, 2017;Poonthalir & Nadarajan, 2018), Tabu Search (TS) (Kuo & Wang, 2011), and Genetic Algorithms (GA) (Franco et al, 2017) were also proposed to solve it.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Whale Optimization Algorithm for Green Vehicle Routing Problem

Dewi

Utama

2020

Systems Science & Control Engineering

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A Simulated Annealing Algorithm Based Solution Method for a Green Vehicle Routing Problem with Fuel Consumption

Cited by 42 publications

References 55 publications

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Solving a Periodic Capacitated Vehicle Routing Problem using Simulated Annealing Algorithm for a Manufacturing Company

A New Hybrid Whale Optimization Algorithm for Green Vehicle Routing Problem

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