A Programming Method of Vehicle Dispatch for Service Centre

2011 ◽  
Vol 121-126 ◽  
pp. 662-666 ◽  
Author(s):  
Hang Sheng Jia ◽  
Fei Cheng
Keyword(s):  
Assignment Problem ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Algorithm Performance ◽  
Service Centre ◽  
Vehicle Assignment ◽  
Annealing Algorithm ◽  
Natural Description

This paper combines Genetic Algorithm with Simulated Annealing Algorithm, namely GA-SA，to discuss vehicle paths and take into account the condition of time with respect to multi-spot service combination problem in service centre. The prevalent genetic algorithms easily lose the optimal solution, which affects the entire algorithm performance for reality vehicle assignment problem in the service centre. Based on modelling the vehicle assignment problem with natural description, fitness function, crossover operation and mutation operation are made the improvement in the approach. The process of computation has also considered own characteristics of the service centre to enable the algorithm optimized performance, in order to obtain the large scale enhancement.

scholarly journals A simulated annealing algorithm for vehicle scheduling problem

2017 ◽  
Vol 1 (Q4) ◽  
pp. 70-77
Author(s):  
Ha Thi Mai Phan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Window ◽  
Fresh Concrete ◽  
Waiting Times ◽  
Optimal Solution ◽  
Production Scale ◽  
Construction Companies ◽  
Annealing Algorithm ◽  
Concrete Tank

As the construction activity has been growing, the companies that supply fresh concrete expand their production scale to meet their customers’ needs. The more customers, the longer queue tank trucks have to wait to pick up the fresh concrete. The customers are construction companies that have different construction works at the same time while the transportation time is only at night. They have to schedule efficiently the fleet of fresh concrete tank trucks during the night (turning the tank trucks a few turns) with constraints on the time window for the transfer of fresh concrete from the concrete company to the construction site as well as constraints on the waiting time for loading fresh concrete in the company. The scheduling for the fleet of construction company’s tank trucks will be modeled to minimize total transportation costs (fixed, variable) with estimated waiting times and tank truck’s turns several times during the night. The model of logistics problem is NP hard; Therefore, two algorithms are proposed to find the nearly optimal solution: heuristics and simulated annealing algorithm. The results will be compared and analyzed.

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

scholarly journals Hierarchical framework for coordinate optimization of train timetable and electric motor train unit circulation plan

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770579
Author(s):  
Chao Lu ◽  
Leishan Zhou ◽  
Jinjin Tang ◽  
Ran Chen
Keyword(s):  
Electric Motor ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Tree Construction ◽  
Total Travel Time ◽  
Annealing Algorithm ◽  
Increasing Demand ◽  
Main Model

To meet the increasing demand for improving railway service quality while using the precious mobile resources reasonably and economically, this research proposes a hierarchical approach that integrates the model for constructing an efficient electric motor train unit circulation plan into the model for optimized timetable design without predefining timetable details. A simulated annealing algorithm for solving the timetabling (main) model is designed, in which the neighborhood system is pretreated. A special tree construction–based branch-and-bound algorithm is improved for solving the electric motor train unit circulation planning (sub)model. The results of the numerical experiment verify the effectiveness of the proposed method. Specifically, compared to the randomly generated initial solution, the optimal solution obtained by the proposed methods reduces the total travel time by 686 min and reduces the number of electric motor train units by 5. The number of electric motor train units needed by the proposed method is on average at least two less than the method that handles the problem in a sequential way. Railway operators can implement this approach for balancing the efficiency of timetable and the quality of electric motor train unit circulation plan within a reasonable computation time when scheduling trains in railways.

scholarly journals The Determination of Preventive Maintenance using Simulated Annealing Algorithm Based on Weighted Fitness Function

2019 ◽  
Vol 20 (1) ◽  
pp. 53
Author(s):  
Yeny Krista Franty ◽  
Budhi Handoko
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Maintenance Period ◽  
Maintenance Schedule ◽  
Machine Maintenance ◽  
System Part ◽  
Annealing Algorithm ◽  
Engine Maintenance

This study aims to determine the machine maintenance schedule. We use the Simulated Annealing Algorithm. Fitness and reliability functions are functions that are used in the optimization process. Several weighting scenarios are done to see the unity of the function. The results of the scenario produce several alternative schedules. This algorithm is implemented on machines that have more than one sub-machine. This sub-machine is a smaller engine system part. This sub-machine also has one particular function. The results of the study show that the optimal engine maintenance period to use is six periods. There are five scheduling scenarios used in this problem. The resulting schedule can increase the value of reliability and can minimize costs.

scholarly journals Optimization and Design of Hammerheads and Fenders on Scrap Metal Shredders Based on Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Dynamic Balance ◽  
Optimal Solution ◽  
Scrap Metal ◽  
Improved Genetic Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Local Optimal Solution ◽  
Annealing Algorithm

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.

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