scholarly journals Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 223
Author(s):  
Iosif V. Katsaragakis ◽  
Ioannis X. Tassopoulos ◽  
Grigorios N. Beligiannis
Keyword(s):  
Public Transportation ◽  
Specific Problem ◽  
Evaluation Criteria ◽  
Main Process ◽  
Urban Transit ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Specific Data ◽  
Cat Swarm Optimization ◽  
First Time

Presented in this research paper is an attempt to apply a cat swarm optimization (CSO)-based algorithm to the urban transit routing problem (UTRP). Using the proposed algorithm, we can attain feasible and efficient (near) optimal route sets for public transportation networks. It is, to our knowledge, the first time that cat swarm optimization (CSO)-based algorithm is applied to cope with this specific problem. The algorithm’s efficiency and excellent performance are demonstrated by conducting experiments with both real-world as well as artificial data. These specific data have also been used as test instances by other researchers in their publications. Computational results reveal that the proposed cat swarm optimization (CSO)-based algorithm exhibits better performance, using the same evaluation criteria, compared to most of the other existing approaches applied to the same test instances. The differences of the proposed algorithm in comparison with other published approaches lie in its main process, which is a modification of the classic cat swarm optimization (CSO) algorithm applied to solve the urban transit routing problem. This modification in addition to a variation of the initialization process, as well as the enrichment of the algorithm with a process of improving the final solution, constitute the innovations of this contribution. The UTRP is studied from both passenger and provider sides of interest, and the algorithm is applied in both cases according to necessary modifications.

scholarly journals Penyelesaian Urban Transit Routing Problem Menggunakan Algoritma Hyper-heuristics berbasis Modified Particle Swarm Optimization based on Gravitational field Interactions

2021 ◽  
Vol 8 (4) ◽  
pp. 1984-1997
Author(s):  
Shof Rijal Ahlan Robbani
Keyword(s):  
Particle Swarm Optimization ◽  
Gravitational Field ◽  
Public Transport ◽  
Particle Swarm ◽  
Hill Climbing ◽  
Urban Transit ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Modified Particle Swarm Optimization

Kemacetan lalu lintas dapat diatasi dengan adanya public transport. Penerapan public transport yang optimal perlu dilakukan penentuan rute yang baik. Untuk mendapatkan rute public transport yang optimal, maka perlu dilakukan beberapa percobaan kombinasi antara jarak titik awal dan tujuan. Sehingga masalah dapat dikatakan sebagai masalah kombinatorik. VRP merupakan permasalahan kombinatorik. Oleh karena itu permasalahan dapat diselesaikan menggunakan metode metaheuristik. Penelitian ini akan menggunakan algoritma Modified Particle Swarm Optimization (MPSO-GI) dengan pendekatan Hyper-heuristics untuk menyelesaikan masalah penentuan rute public transport. Data yang digunakan merupakan dataset Mumford dan Mandl yang digunakan pada beberapa penelitian sebelumnya. Penelitian dilakukan dengan membandingkan hasil solusi yang dihasilkan oleh metode yang ditawarkan dengan hasil pada penelitian sebelumnya. Sehingga dapat diketahui kelebihan dan kekurangan dari metode yang ditawarkan. Berdasarkan hasil uji coba dapat ketahui bahwa algoritma MPSO-GI dengan pendekatan Hyper-Heuristics dapat diimpelmentasikan dan menyelesaikan masalah UTRP. MPSO-GI dengan pendekatan Hyper-Heuristics berhasil memperbaiki solusi hill-climbing di hamper semua dataset dengan nilai yang stabil. Hasil metode MPSO-GI dengan pendekatan Hyper-Heuristics unggul dalam menghasilkan solusi biaya penumpang pada dataset Mandl4, Mandl6, Mandl7, Mandl8 dan biaya operator pada dataset Mandl4 dan Mandl6 jika dibandingkan dengan metode pada penelitian sebelumnya.

scholarly journals Adaptive Cat Swarm Optimization Algorithm and Its Applications in Vehicle Routing Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiao-Fang Ji ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Pei Hu ◽  
Qing-Wei Chai ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Cat Swarm Optimization ◽  
Fixed Dimension ◽  
Speed Up ◽  
Effectiveness And Efficiency ◽  
Experimental Findings

This paper proposes a novel hybrid algorithm named Adaptive Cat Swarm Optimization (ACSO). It combines the benefits of two swarm intelligence algorithms, CSO and APSO, and presents better search results. Firstly, some strategies are implemented to improve the performance of the proposed hybrid algorithm. The tracing radius of the cat group is limited, and the random number parameter r is adaptive adjusted. In addition, a scaling factor update method, called a memory factor y, is introduced into the proposed algorithm. They can be learnt very well so as to jump out of local optimums and speed up the global convergence. Secondly, by comparing the proposed algorithm with PSO, APSO, and CSO, 23 benchmark functions are verified by simulation experiments, which consists of unimodal, multimodal, and fixed-dimension multimodal. The results show the effectiveness and efficiency of the innovative hybrid algorithm. Lastly, the proposed ACSO is utilized to solve the Vehicle Routing Problem (VRP). Experimental findings also reveal the practicability of the ACSO through a comparison with certain existing methods.

scholarly journals Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

scholarly journals Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

2021 ◽  
Author(s):  
Duygu Sergi ◽  
Irem Ucal Sari
Keyword(s):  
Health Care ◽  
Public Transportation ◽  
Health Care Services ◽  
Public Services ◽  
Fast Response ◽  
Care Services ◽  
Citizen Complaints ◽  
Criteria Weights ◽  
Available Information ◽  
First Time

AbstractIn this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased quality. After obtaining criteria weights using fuzzy Z-AHP, health care services, waste disposal department, public transportation, information services, social care services, and citizen complaints resolution centers are compared using fuzzy Z-WASPAS that is proposed for the first time in this paper. Results show that health care services have dominant importance for the digitalization among public services.

scholarly journals Evaluating Performance of Public Transport Networks by Using Public Transport Criteria Matrix Analytic Hierarchy Process Models—Case Study of Stonnington, Bayswater, and Cockburn Public Transport Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6949
Author(s):  
Gang Lin ◽  
Shaoli Wang ◽  
Conghua Lin ◽  
Linshan Bu ◽  
Honglei Xu
Keyword(s):  
Sustainable Development ◽  
Public Transport ◽  
Public Transportation ◽  
Evaluation Criteria ◽  
Performance Standards ◽  
Transport Network ◽  
Process Models ◽  
Transport Infrastructure ◽  
Transport Networks ◽  
Ahp Model

To mitigate car traffic problems, the United Nations Human Settlements Programme (UN-Habitat) issued a document that provides guidelines for sustainable development and the promotion of public transport. The efficiency of the policies and strategies needs to be evaluated to improve the performance of public transportation networks. To assess the performance of a public transport network, it is first necessary to select evaluation criteria. Based on existing indicators, this research proposes a public transport criteria matrix that includes the basic public transport infrastructure level, public transport service level, economic benefit level, and sustainable development level. A public transport criteria matrix AHP model is established to assess the performance of public transport networks. The established model selects appropriate evaluation criteria based on existing performance standards. It is applied to study the Stonnington, Bayswater, and Cockburn public transport network, representing a series of land use and transport policy backgrounds. The local public transport authorities can apply the established transport criteria matrix AHP model to monitor the performance of a public transport network and provide guidance for its improvement.

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