scholarly journals Optimal Operation Solution for Public Bicycles Based on Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 1141-1149
Author(s):  
Pinhong Zeng
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Optimal Operation ◽  
Optimal Route ◽  
Vehicle Route ◽  
Scheduling Model ◽  
Original Number ◽  
Bicycle Transportation

Aiming at the various problems with the scheduling of urban public bicycles, this paper conducted a research on the shortest path between rental points and employed the Floyd algorithm to find the optimal route. Based on the conditions of limited number of bicycle transportation vehicles and in different time slots the bicycle rental points were required to restore to the original number of bicycles, a constraint scheduling model was established according to the bicycle supply-demand relationships of the rental points, and the Genetic Algorithm (GA) was used to solve the model to find the shortest path. In terms of balancing the bicycles at each rental point, this paper re-distributed the initial bicycles according to the different demands of each rental point in different time slots, and solved the problem using the solution of the first problem to obtain the optimal vehicle route. This research is a useful reference for solving difficulties in public bicycle scheduling.

Commercial Vehicle Route Planning Design Based on Partition Coding Genetic Algorithm

2020 ◽  
Author(s):  
Kailang Chen ◽  
Hanyu Zhang ◽  
Xin Huang ◽  
Long Chen ◽  
Qi Shi
Keyword(s):  
Genetic Algorithm ◽  
Commercial Vehicle ◽  
Route Planning ◽  
Vehicle Route

Application of Genetic Algorithm in modeling of Shortest Path problem

2020 ◽  
Author(s):  
Zhihui Yang ◽  
Huiwen Xia ◽  
Fuwen Su ◽  
Jiayu Zhao ◽  
Fan Feng
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Shortest Path Problem

Holes Machining Process Optimization with Genetic Algorithm

2011 ◽  
Vol 460-461 ◽  
pp. 117-122 ◽  
Author(s):  
Guang Yu Zhu ◽  
Lian Fang Chen
Keyword(s):  
Genetic Algorithm ◽  
Cutting Tool ◽  
Machining Process ◽  
Processing Route ◽  
Optimal Route ◽  
Optimal Processing ◽  
Global Optimal ◽  
Level Method ◽  
Multi Level ◽  
Changing Times

In this paper, a multi-level method has been adopted to optimize the holes machining process with genetic algorithm (GA). Based on the analyzing of the features of the part with multi-holes, the local optimal processing route for the holes with the same processing feature is obtained with GA, then try to obtain the global optimal route with GA by considering the obtained local optimal route and the holes with different features. That is what the multi-level method means. The optimal route means the minimum moving length of the cutting tool and the minimum changing times of the cutting tool. The experiment is carried out to verify the algorithm and the proposed method, and result indicates that with GA and using the multi-level method the optimal holes machining route can be achieved efficiently.

scholarly journals A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS

2016 ◽  
Vol XLI-B2 ◽  
pp. 299-304 ◽  
Author(s):  
A. A. Heidari ◽  
M. R. Delavar
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Shortest Paths ◽  
The Body ◽  
Shortest Path Problems ◽  
Conventional Procedure ◽  
Path Lengths ◽  
The Cost ◽  
Uncertain Networks ◽  
Exploration Exploitation

In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

scholarly journals SCHEDULLING MODEL OF REPLENISHMENT AT SEA FOR STRICKING FORCE UNIT IN SEA OPERATION USING GENETIC ALGORITHM

JOURNAL ASRO ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Aris Tri Ika R ◽  
Benny Sukandari ◽  
Okol Sri Suharyo ◽  
Ayip Rivai Prabowo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Time Windows ◽  
Lubricating Oil ◽  
Optimal Time ◽  
Routing Problem ◽  
Scheduling Model ◽  
Force Unit ◽  
Replenishment At Sea ◽  
Crossover And Mutation

Navy as a marine core in the defense force is responsible for providing security for realizing stability and security of the country.  At any time there was an invasion of other countries past through sea,  TNI AL must be able to break the enemy resistance line through a sea operation to obtain the sea superiority. But this time the endurance of Striking force Unit at only 7-10 days and required replenishment at sea to maximize the presence in the theater of operations to meet a demand of the logistics: HSD, Freshwater, Lubricating Oil, foodstuffs and amonisi. For the optimal replenishment at sea required scheduling model supporting unit to get the minimum time striking force unit was on node rendezvous. Replenishment at sea scheduling model for striking force unit refers to the problems Vehicle routing problem with time windows using Genetic Algorithms. These wheelbase used is roulette for reproduction, crossover, and mutation of genes. Genetic algorithms have obtained optimum results in the shortest route provisioning scenario uses one supporting unit with a total time of 6.89 days. In scenario two supporting unit with minimal time is 4.97 days. In the scenario, the changing of the node replenishment Genetic Algorithm also get optimal time is 4.97 days with two supporting units. Research continued by changing the parameters of the population, the probability of crossover and mutation that can affect the performance of the genetic algorithm to obtain the solution. Keywords: Genetic Algorithm, Model Scheduling, Striking Force unit

Micro-Grid Energy Optimization Include Battery-Swapping-Station

2014 ◽  
Vol 672-674 ◽  
pp. 1358-1363
Author(s):  
Liu Shu ◽  
Fang Liu ◽  
Xiu Yang
Keyword(s):  
Genetic Algorithm ◽  
Renewable Energy ◽  
Electric Vehicle ◽  
Negative Impact ◽  
Operating Cost ◽  
Energy Optimization ◽  
Optimal Operation ◽  
Operation Efficiency ◽  
Micro Grid ◽  
Minimum Operating

Accessing electric vehicle (EV) into micro-grid (MG) by battery-swapping station (BSS) will not only reduce the negative impact brought by EVs which are directly accessed into MG, but also improve the capacity of MG to absorb more renewable energy. That BSS is regarded as schedulable load is guided to avoid peak and fill valley according to TOU. As a result, the gap between peak and valley of MG is decreased and the operation efficiency of MG is elevated. A specific MG is taken as the studying object and the minimum operating cost is regarded as the optimizing goal, then the genetic algorithm is used to optimize the outputting of each micro-source and the charging power of BSS so that the optimal operation is realized.

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