Article
Mathematics, Applied
E. Duman
Summary: The Turkish Cashier Problem (TCP) is a special case of the traveling salesman problem, focusing on finding the optimal route to minimize transportation cost for cashiers. To address this problem, a heuristic algorithm was developed, along with a tight lower bound, and it was demonstrated that the heuristic algorithm performs well for practical instances of the problem.
APPLIED AND COMPUTATIONAL MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Yongliang Lu, Jin-Kao Hao, Qinghua Wu
Summary: In this work, we investigate a transformation approach to solve the Clustered Traveling Salesman Problem (CTSP) by converting it to the well-studied Traveling Salesman Problem (TSP). We explore the performance of state-of-the-art TSP solvers on clustered instances converted from CTSP and compare it with methods specifically designed for CTSP. Intensive computational experiments on benchmark instances are presented to draw conclusions.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Mesut Gunduz, Murat Aslan
Summary: Jaya algorithm is a newly proposed stochastic population-based metaheuristic optimization algorithm that improves intensification and diversification of population by utilizing best and worst solutions. With discrete modifications, the algorithm, called DJAYA, has shown competitive performance in solving various discrete optimization problems like the symmetric traveling salesman problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Emine BAS, Erkan Ulker
Summary: Heuristic algorithms, like the Social Spider Algorithm, are effective for solving complex real-world problems efficiently. DSSA, a modification of SSA, shows promising performance especially for low and middle-scale Traveling Salesman Problems, making it a viable option for discrete optimization tasks.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Yong Wang, Zunpu Han
Summary: The study combines the hybrid symbiotic organisms search (SOS) and ant colony optimization (ACO) algorithms for solving the traveling salesman problem (TSP), demonstrating improved performance through adaptive parameter optimization and validating the results through experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Xin Wei, Liang Ma, Huizhen Zhang, Yong Liu
Summary: Multi-core computers have been widely used in commercial services and household usage in the past decade, although they have not shown general advantages for users. Due to hardware and programming language limitations, it is difficult to transform traditional algorithms into multi-core, multi-thread algorithms, requiring the design of new algorithms to fully utilize multi-core chips.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Interdisciplinary Applications
Pengfei He, Jin-Kao Hao
Summary: The paper presents a hybrid algorithm for solving the multiple traveling salesman problem, combining different optimization strategies to achieve good results.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
Pengfei He, Jin-Kao Hao
Summary: The Colored Traveling Salesmen Problem (CTSP) is a generalization of the popular Traveling Salesman Problem, involving multiple salesmen; The goal is to determine the shortest Hamiltonian circuit for each salesman, satisfying specific conditions; It is known to be computationally challenging, and a solution has been proposed.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Mathematics
Kezong Tang, Xiong-Fei Wei, Yuan-Hao Jiang, Zi-Wei Chen, Lihua Yang
Summary: The AACO-LST algorithm proposes solutions to the dimensional catastrophe problems by improving the state transfer rule and using a 2-opt operator for local optimization. It also introduces adaptive pheromone update rules to enhance search efficiency, resulting in improved solution quality and convergence speed.
Article
Mathematics
Jeewaka Perera, Shih-Hsi Liu, Marjan Mernik, Matej Crepinsek, Miha Ravber
Summary: This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for solving multi-objective TSPs. MODGRL improves an earlier deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. The results show that MODGRL outperforms the competitors on convergence and diversity measured by the hypervolume indicator.
Article
Engineering, Civil
Xianghu Meng, Jun Li, MengChu Zhou, Xianzhong Dai
Summary: This study introduces a colored traveling salesman problem with edge weights among cities changing over time, applicable to dynamic routing in logistic distribution systems. Through a specific algorithm, solution quality and adaptability to environmental changes are improved.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jing Zhong, Yuelei Feng, Shuyu Tang, Jiang Xiong, Xiangguang Dai, Nian Zhang
Summary: This paper proposes a collaborative neurodynamic optimization method to solve the traveling salesman problem. The method utilizes a neural network and particle swarm optimization to effectively solve the TSP.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Mathematics
Vincent F. Yu, Shih-Wei Lin, Panca Jodiawan, Yu-Chi Lai
Summary: This study investigates the Flying Sidekick Traveling Salesman Problem (FSTSP) and proposes a revised mixed-integer linear programming (MILP) model and an effective heuristic based on simulated annealing (SA) to solve the problem. The proposed SA heuristic outperforms existing algorithms and achieves satisfactory results in various benchmark instances.
Article
Mathematics
Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Angela Amphawan, Ali Wagdy Mohamed
Summary: Optimization problems are common and important in various fields. The Dragonfly Algorithm, inspired by the swarming behaviors of dragonflies, has been proven to be more effective than other algorithms. In this paper, an optimized discrete adapted DA is proposed and applied to the traveling salesman problem, achieving higher effectiveness and efficiency.
Article
Computer Science, Information Systems
Joanna Ochelska-Mierzejewska, Aneta Poniszewska-Maranda, Witold Maranda
Summary: The TSP involves finding the shortest path connecting all cities, while the VRP focuses on defining routes for vehicles in logistics transportation. Optimizing the VRP can reduce total costs, with economic benefits in more open markets.
Article
Oncology
Katia I. Camacho-Caceres, Juan C. Acevedo-Diaz, Lynn M. Perez-Marty, Michael Ortiz, Juan Irizarry, Mauricio Cabrera-Rios, Clara E. Isaza
Article
Oncology
Clara Isaza, Juan F. Rosas, Enery Lorenzo, Arlette Marrero, Cristina Ortiz, Michael R. Ortiz, Lynn Perez, Mauricio Cabrera-Rios
Article
Construction & Building Technology
Nitza M. Garcia, Hildelix L. Soto-Toro, Mauricio Cabrera-Rios, Oscar Marcelo Suarez
ADVANCES IN CIVIL ENGINEERING
(2018)
Article
Materials Science, Ceramics
N. M. Garcia, L. E. Zapata, O. M. Suarez, M. Cabrera-Rios
ADVANCES IN APPLIED CERAMICS
(2015)
Article
Automation & Control Systems
Saul Villarreal, Jesus A. Jimenez, Tongdan Jin, Mauricio Cabrera-Rios
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2013)
Article
Multidisciplinary Sciences
Isis Narvaez-Bandera, Deiver Suarez-Gomez, Clara E. Isaza, Mauricio Cabrera-Rios
Summary: This article describes the development of an open-source tool that uses multiple criteria optimization algorithm for gene selection in microarray datasets. It provides an affordable, repeatable, and objective detection of differentially expressed genes. The tool was applied to the analysis of Parkinson's Disease-related microarray datasets, resulting in the identification of potential genetic biomarkers.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Yaileen M. Mendez-Vazquez, David A. Nembhard, Mauricio Cabrera-Rios
2020 WINTER SIMULATION CONFERENCE (WSC)
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Yaileen M. Mendez-Vazquez, David A. Nembhard
2017 WINTER SIMULATION CONFERENCE (WSC)
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Esmeralda Nino-Perez, Cesar A. Rivera-Collazo, Mauricio Cabrera-Rios, Yaileen M. Mendez-Vazquez
2017 WINTER SIMULATION CONFERENCE (WSC)
(2017)
Article
Mathematics, Applied
Diana Sanchez-Partida, Jose Luis Martinez-Flores, Mauricio Cabrera-Rios, Elias Olivares-Benitez
INTERNATIONAL JOURNAL OF COMBINATORIAL OPTIMIZATION PROBLEMS AND INFORMATICS
(2017)
Meeting Abstract
Oncology
Jaileene Perez-Morales, Mauricio Cabrera-Rios, Jonathan Gonzalez-Flores, Pedro Santiago-Cardona
Article
Chemistry, Applied
Y. Lara-Rodriguez, M. Sanchez-Pena, Y. Cedeno Mattei, O. Perales, H. E. Calderon, O. Marcelo Suarez, O. Chacon, M. Cabrera-Rios
REVISTA MEXICANA DE INGENIERIA QUIMICA
(2015)
Article
Engineering, Industrial
Alicia Berenice Rodriguez-Yanez, Yaileen Marie Mendez-Vazquez, Mauricio Cabrera-Rios
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL
(2014)
Article
Engineering, Industrial
Yaileen M. Mendez-Vazquez, Kasandra L. Ramirez-Rojas, Hecny Perez-Candelario, Mauricio Cabrera-Rios
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL
(2014)