An NSGA-II Algorithm for the Green Vehicle Routing Problem

Jemai, Jaber; Zekri, Manel; Mellouli, Khaled

doi:10.1007/978-3-642-29124-1_4

Cited by 64 publications

(25 citation statements)

References 14 publications

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“…To map the whole Pareto optimal frontier, the optimization procedure often should be repeated many times, which is a time-consuming process [18]. Evolutionary algorithms have been recognized to be well suited to multiobjective optimization, for example, one of the most efficient and commonly used versions of multi-objective GA (NSGA-II) can handle large and complex constraints by natural-inspired operators, and the NSGA-II algorithm has low computational complexity and good convergence by applying effective elite strategy than the previous evolutionary algorithms [15]; it has been successfully applied in solving many complex engineering optimization problems and achieved remarkable results [19][20][21][22][23][24][25][26]. First of all, NSGA-II is selected to solve the multi-objective optimization model for oil-gas production process, and then, in order to further improve the diversity and convergence of Pareto optimal solutions obtained by NSGA-II algorithm when solving the complicated and constrained optimization problems, an improved NSGA-II algorithm (I-NSGA-II) is proposed in this paper.…”

Section: Multi-objective Optimization For Oil-gas Production Process mentioning

confidence: 99%

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Tan

Gao

Wang

2015

Journal of the Taiwan Institute of Chemical Engineers

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Section: Multi-objective Optimization For Oil-gas Production Process mentioning

confidence: 99%

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Tan

Gao

Wang

2015

Journal of the Taiwan Institute of Chemical Engineers

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“…This trend of reducing carbon emissions was mainly encouraged by the preferences of governments, shippers, and other stakeholders [13]. Maritime transport contributes greatly to carbon emissions [14,15].…”

Section: Reducion Of Ghg Emissionsmentioning

confidence: 99%

Bi-Objective Optimization of Vessel Speed and Route for Sustainable Coastal Shipping under the Regulations of Emission Control Areas

et al. 2019

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To comply with the regulations of emission control areas (ECAs), most operators have to switch to low-sulfur fuels inside the ECAs. Besides, a low-carbon objective is essential for long-term environmental protection; thus, is regarded as important as making profit. Therefore, the operators start making speed and route decisions under the two objectives of minimizing carbon emissions and maximizing profit. Drawing on existing methods, this paper formulates the profit and carbon emissions in sustainable coastal shipping, investigates the speed and route principles, and determines the best tradeoff between profit and carbon emissions. It is found that vessel speed should be set between emissions-optimum speed and profit-optimum speed, and the route must be selected in light of the speed decision. Next, the optimal choices of speed and route were examined under different scenarios and vessel types. The results show that the operation measures and objectives depend greatly on fuel price, vessel load, and vessel parameters. The operator should speed up the vessel if he/she wants to make more profit or if the scenario is favorable for profit making; e.g., low fuel price and high vessel load (LFHL). Large vessels should pursue more profit under LFHL conditions, without having to sail further outside the ECA. But this rule does not apply to small vessels. In addition, the operator should slow down the vessel inside the ECA and sail further, outside the ECA, with the growth in the price spread between marine gas oil (MGO) and heavy fuel oil (HFO), especially at a low HFO price. The research findings help operators to design operational measures that best suit the limit on sulfur content in fuel and the situation of the shipping market.

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“…A few variants of the NSGA II algorithm have been used for solving multi objective vehicle routing problems, e.g. for solving vehicle routing problem with route balancing [22], for solving multi objective vehicle routing problems with time windows [23] and for solving a green vehicle routing problems [24]. Multi objective genetic algorithms for the solution of multi objective vehicle routing problems have been used in [25,26].…”

Section: Research Concerning Materials Distribution Optimizationmentioning

confidence: 99%

Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019

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Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

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An NSGA-II Algorithm for the Green Vehicle Routing Problem

Cited by 64 publications

References 14 publications

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Multi-objective optimization method using an improved NSGA-II algorithm for oil–gas production process

Bi-Objective Optimization of Vessel Speed and Route for Sustainable Coastal Shipping under the Regulations of Emission Control Areas

Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

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