scholarly journals Research on Route Deviation Transit Operation Scheduling—A Case Study in Suburb No. 5 Road of Harbin

2022 ◽  
Vol 14 (2) ◽  
pp. 633
Author(s):  
Xianglong Sun ◽  
Sai Liu
Keyword(s):  
Travel Demand ◽  
Search Algorithm ◽  
Operating Cost ◽  
Heuristic Search Algorithm ◽  
Scheduling Model ◽  
Special Groups ◽  
Passenger Travel ◽  
Response Modes ◽  
Transit Operation ◽  
Operation Scheduling

Route deviation transit is a flexible “door-to-door” service method that combines the efficiency of conventional public transport modes and the flexibility of demand response modes, meeting the travel needs of people with low travel density and special groups. In this paper, the minimum value of the sum of vehicle operating cost and passenger travel cost was the optimal goal, and the RDT multi-vehicle operation scheduling model was constructed. Taking the available relaxation time as the control parameter of the RDT system and considering the insertion process of the random travel demand of the passengers during the operation process, we used a heuristic search algorithm to solve the scheduling model. This paper took Suburb No. 5 Road of Harbin as an example, using MATLAB to simulate the RDT operation scheduling model to verify the stability and feasibility of the RDT system under different demands. The results showed that under different demand conditions, the system indicators such as passenger travel time, waiting time, and vehicle mileage in the RDT system fluctuated very little, and the system performance was relatively stable. Under the same demand conditions, the per capita cost of the RDT system was 5.9% to 10.8% less than that of the conventional bus system. When the demand ρ is 20~40 person/hour, the RDT system is more effective than the conventional bus for the 5 bus in the suburbs of Harbin.

Study on Combinational Scheduling Optimization of Bus Transit Rapid Based on Tabu Search & Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1827-1831
Author(s):  
Cheng Zhi Chang ◽  
Xu Mei Chen ◽  
Meng Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Search Algorithm ◽  
Operating Cost ◽  
Fundamental Principle ◽  
Total System ◽  
Scheduling Optimization ◽  
System Cost ◽  
Scheduling Model ◽  
Mixed Genetic Algorithm

The goal is to minimize the sum of operating cost and passengers’ travel cost, and establish an optimized combinational scheduling model of Bus Rapid Transit (BRT) combined with regular bus, express bus and shuttle bus. A mixed genetic algorithm based on tabu search algorithm (GA-TS) has been designed after analyzing the fundamental principle of genetic algorithm (GA) and tabu search (TS). A case study has been carried out on the combinational scheduling optimization of a selecting BRT line. By adopting the combinational scheduling model, 5.24% of the total system cost can be saved, which is quite prominent. The mixed genetic algorithm based on GA-TS can optimize the BRT scheduling system, shorten the turnaround time of operating BRT vehicles, effectively reduce the total system cost of BRT and improve decision-making efficiency and service quality.

scholarly journals Optimization Method of Customized Shuttle Bus Lines under Random Condition

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 52
Author(s):  
Zhichao Sun ◽  
Kang Zhou ◽  
Xinzheng Yang ◽  
Xiao Peng ◽  
Rui Song
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Operating Cost ◽  
Optimization Method ◽  
Routing Problem ◽  
Hybrid Intelligent Algorithm ◽  
Passenger Travel ◽  
Time Requirements ◽  
The Cost ◽  
Stochastic Constraints

Transit network optimization can effectively improve transit efficiency, improve traffic conditions, and reduce the pollution of the environment. In order to better meet the travel demands of passengers, the factors influencing passengers’ satisfaction with a customized bus are fully analyzed. Taking the minimum operating cost of the enterprise as the objective and considering the random travel time constraints of passengers, the customized bus routes are optimized. The K-means clustering analysis is used to classify the passengers’ needs based on the analysis of the passenger travel demand of the customized shuttle bus, and the time stochastic uncertainty under the operating environment of the customized shuttle bus line is fully considered. On the basis of meeting the passenger travel time requirements and minimizing the cost of service operation, an optimization model that maximizes the overall satisfaction of passengers and public transit enterprises is structured. The smaller the value of the objective function is, the lower the operating cost. When the value is negative, it means there is profit. The model is processed by the deterministic processing method of random constraints, and then the hybrid intelligent algorithm is used to solve the model. A stochastic simulation technique is used to train stochastic constraints to approximate uncertain functions. Then, the improved immune clonal algorithm is used to solve the vehicle routing problem. Finally, it is proved by a case that the method can reasonably and efficiently realize the optimization of the customized shuttle bus lines in the region.

scholarly journals SIMULATED ANNEALING GENETIC ALGORITHM-BASED HARVESTER OPERATION SCHEDULING MODEL

2021 ◽  
pp. 249-260
Author(s):  
Qingkai Zhang ◽  
Guangqiao Cao ◽  
Junjie Zhang ◽  
Yuxiang Huang ◽  
Cong Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Scheduling Algorithm ◽  
Supply And Demand ◽  
Operating Cost ◽  
Optimal Solution ◽  
Agricultural Machinery ◽  
Simulated Annealing Genetic Algorithm ◽  
Scheduling Model ◽  
Operation Scheduling

To address problems involving the poor matching ability of supply and demand information and outdated scheduling methods in agricultural machinery operation service, in this study, we proposed a harvester operation scheduling model and algorithm for an order-oriented multi-machine collaborative operation within a region. First, we analysed the order-oriented multi-machine collaborative operation within the region and the characteristics of agricultural machinery operation scheduling, examined the revenue of a mechanized harvesting operation and the components of each cost, and constructed a harvester operation scheduling model with the operation income as the optimization goal. Second, we proposed a simulated annealing genetic algorithm-based harvester operation scheduling algorithm and analysed the validity and stability of the algorithm through experimental simulations. The results showed that the proposed harvester operation scheduling model effectively integrated the operating cost, transfer cost, waiting time cost, and operation delay cost of the harvester, and the accuracy of the harvester operation scheduling model was improved; the harvester operation scheduling algorithm based on simulated annealing genetic algorithm (SAGA) was able to obtain a global near-optimal solution of high quality and stability with high computational efficiency.

scholarly journals An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4398
Author(s):  
Yiqi Li ◽  
Jing Zhang ◽  
Zhoujun Ma ◽  
Yang Peng ◽  
Shuwen Zhao
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Value At Risk ◽  
Operating Cost ◽  
Optimization Method ◽  
Conditional Value At Risk ◽  
Demand Side ◽  
Scheduling Model ◽  
Management Optimization ◽  
Demand Side Response

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

scholarly journals Exact and Heuristic Methods for Observing Task-Oriented Satellite Cluster Agent Team Formation

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Hao Chen ◽  
Shu Yang ◽  
Jun Li ◽  
Ning Jing
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Low Cost ◽  
Team Formation ◽  
Satellite Systems ◽  
Heuristic Search Algorithm ◽  
Earth Observation Satellite ◽  
Novel Algorithms ◽  
Swarm Intelligence Optimization ◽  
Task Oriented

With the development of aerospace science and technology, Earth Observation Satellite cluster which consists of heterogeneous satellites with many kinds of payloads appears gradually. Compared with the traditional satellite systems, satellite cluster has some particular characteristics, such as large-scale, heterogeneous satellite platforms, various payloads, and the capacity of performing all the observation tasks. How to select a subset from satellite cluster to perform all observation tasks effectively with low cost is a new challenge arousing in the field of aerospace resource scheduling. This is the agent team formation problem for observation task-oriented satellite cluster. A mathematical scheduling model is built. Three novel algorithms, i.e., complete search algorithm, heuristic search algorithm, and swarm intelligence optimization algorithm, are proposed to solve the problem in different scales. Finally, some experiments are conducted to validate the effectiveness and practicability of our algorithms.

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