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Related Experiment Video

Updated: May 20, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Colored Traveling Salesman Problems: Models, Solutions, and Applications.

Jun Li, MengChu Zhou

    IEEE Transactions on Cybernetics
    |May 18, 2026
    PubMed
    Summary
    This summary is machine-generated.

    The colored traveling salesman problem (CTSP) generalizes existing routing problems. This survey provides a unified framework, solution methods, and application insights for CTSPs and related sequencing challenges.

    Related Experiment Videos

    Last Updated: May 20, 2026

    Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
    11:41

    Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

    Published on: February 1, 2020

    Area of Science:

    • Operations Research
    • Combinatorial Optimization
    • Computer Science

    Background:

    • The colored traveling salesman problem (CTSP) models salesman homogeneity and city assignment relationships using colors.
    • CTSP is a generalization of the traveling salesman problem (TSP) and multiple traveling salesman problems (MTSP).
    • Despite its potential, CTSP solutions and applications in logistics, manufacturing, and transportation require further exploration.

    Purpose of the Study:

    • To conduct the first comprehensive survey on CTSPs, fostering understanding, research, and applications.
    • To organize the CTSP development timeline and re-formalize existing CTSPs and variants.
    • To provide a unified perspective on CTSPs, their solutions, and applications.

    Main Methods:

    • Systematic review and synthesis of existing CTSP literature.
    • Comparative analysis of CTSPs with TSP, MTSP, and vehicle routing problems (VRPs).
    • Categorization and explanation of exact, heuristic, large-scale, learning-based, and parallel solution approaches for CTSPs.

    Main Results:

    • A unified formalization of CTSPs and their variants with diverse objectives and constraints.
    • A clear distinction and connection between CTSPs, TSPs, MTSPs, and VRPs.
    • An overview of solution methodologies and a review of CTSP applications in multimachine mission systems scheduling.

    Conclusions:

    • This survey serves as a foundational reference for CTSP research and related sequencing problems.
    • It highlights the need for further exploration of CTSP solutions and applications.
    • It identifies future research directions and potential applications for CTSPs.