scholarly journals Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

2018 ◽  
Vol 10 (10) ◽  
pp. 3519 ◽  
Author(s):  
Cunrui Ma ◽  
Baohua Mao ◽  
Qi Xu ◽  
Guodong Hua ◽  
Sijia Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Vehicle Routing ◽  
Hazardous Materials ◽  
Transportation Energy ◽  
Pareto Solution ◽  
Hazardous Materials Transportation ◽  
Multi Objective ◽  
Transportation Risk ◽  
Multi Objective Genetic Algorithm

Focusing on the multi-depot vehicle routing problem (MDVRP) for hazardous materials transportation, this paper presents a multi-objective optimization model to minimize total transportation energy consumption and transportation risk. A two-stage method (TSM) and hybrid multi-objective genetic algorithm (HMOGA) are then developed to solve the model. The TSM is used to find the set of customer points served by each depot through the global search clustering method considering transportation energy consumption, transportation risk, and depot capacity in the first stage, and to determine the service order of customer points to each depot by using a multi-objective genetic algorithm with the banker method to seek dominant individuals and gather distance to keep evolving the population distribution in the second stage, while with the HMOGA, customer points serviced by the depot and the serviced orders are optimized simultaneously. Finally, by experimenting on two cases with three depots and 20 customer points, the results show that both methods can obtain a Pareto solution set, and the hybrid multi-objective genetic algorithm is able to find better vehicle routes in the whole transportation network. Compared with distance as the optimization objective, when energy consumption is the optimization objective, although distance is slightly increased, the number of vehicles and energy consumption are effectively reduced.

Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm

2012 ◽  
Vol 253-255 ◽  
pp. 1356-1359
Author(s):  
Ru Zhong ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Efficient Method ◽  
Vehicle Routing Problem ◽  
Multiple Objectives ◽  
Routing Problem ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

When there are multiple objectives co-existent in Vehicle routing problem(VRP), it is difficult to achieve optical status simultaneously. To solve this issue, it introduces a method of improved multi-objective Genetic Algorithm (MOGA). It adopts an approach close to heuristic algorithm to cultivate partial viable chromosomes, route decoding to ensure that all individuals meet constraints and uses relatively efficient method of arena contest to construct non-dominated set. Finally programme to fulfill the multi-objective algorithm and then apply it in the standard example of VRP to verity its effectiveness by comparison with the existing optimal results.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

scholarly journals A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

2019 ◽  
Vol 31 (5) ◽  
pp. 513-525
Author(s):  
Manman Li ◽  
Jian Lu ◽  
Wenxin Ma
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Multi Objective ◽  
Objective Time ◽  
Multi Objective Genetic Algorithm ◽  
Positive Effect

Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.

scholarly journals Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3118
Author(s):  
Qiushi Bi ◽  
Guoqiang Wang ◽  
Yongpeng Wang ◽  
Zongwei Yao ◽  
Robert Hall
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Trajectory Planning ◽  
Open Pit ◽  
Open Pit Mining ◽  
Second Phase ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Phase Optimization ◽  
Cable Shovel

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20   s to 10   s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

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