Efficient Algorithms and Heuristics for Capacitated Covering Salesman Problems

מושגי ליבה

The author introduces the Capacitated Covering Salesman Problem (CCSP) and proposes optimization methodologies based on ILP and BRKGA. The approach aims to minimize total distance traversed by vehicles while servicing customers efficiently.

תקציר

This paper introduces the CCSP, a unique problem in vehicle routing that allows for remote servicing of customers within a coverage area. Optimization methodologies like ILP and BRKGA are proposed and evaluated on benchmark instances, showing promising results.
The CCSP generalizes the well-known CVRP by incorporating the concept of service by covering, providing new insights into efficient vehicle routing problems. Various related problems like CSP, CTP, m-CTP, and MDCTVRP are discussed with their respective methodologies.
The proposed matheuristic for CCSP combines exact formulations with heuristic approaches to tackle large instances effectively. Computational experiments demonstrate the effectiveness of these methodologies in solving complex routing problems.
Overall, this work contributes to advancing solutions for challenging vehicle routing problems through innovative algorithms and heuristics.

סטטיסטיקה

The overall optimality gaps were 0.10%, 8.99%, and 30.28% for MDCTVRPm, Fflow, and Fnode methodologies respectively.
For small instances, MDCTVRPm obtained optimal solutions for 117 out of 120 instances.
The matheuristic methodology improved BRKGA solutions for 187 out of 495 instances.

ציטוטים

תובנות מפתח מזוקקות מ:

Exact algorithms and heuristics for capacitated covering salesman problems

by Luca... ב- arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.06995.pdf

שאלות מעמיקות

How can the concepts introduced in this paper be applied to real-world logistics scenarios

The concepts introduced in this paper, such as the Capacitated Covering Salesman Problem (CCSP) and the Multi-Depot Covering Tour Vehicle Routing Problem (MDCTVRP), can be applied to real-world logistics scenarios in various ways. For example:

Last-Mile Delivery Optimization: Companies can use these optimization models to plan efficient routes for delivery vehicles, ensuring that all customer demands are met while minimizing travel distances.
Emergency Response Planning: During natural disasters or emergencies, these models can help emergency services optimize their response by covering affected areas efficiently with limited resources.
Supply Chain Management: By incorporating coverage constraints into vehicle routing problems, companies can ensure that distribution centers are adequately covered and serviced by vehicles.

These applications demonstrate how the concepts presented in this research can be utilized to improve operational efficiency and resource utilization in real-world logistics scenarios.

What potential limitations or drawbacks might arise from relying heavily on heuristic approaches in solving complex optimization problems

While heuristic approaches offer practical solutions for complex optimization problems like vehicle routing, they also come with potential limitations:

Sub-Optimality: Heuristics may not always guarantee finding the optimal solution due to their reliance on approximations and simplifications of the problem.
Sensitivity to Parameters: The performance of heuristics is often sensitive to parameter settings, requiring careful tuning for each specific problem instance.
Limited Generalizability: Heuristic algorithms may perform well on certain types of instances but struggle with others, limiting their generalizability across a wide range of scenarios.

Relying heavily on heuristic approaches without considering their limitations could lead to suboptimal solutions or inefficiencies in solving complex optimization problems.

How could advancements in technology impact the efficiency and scalability of algorithms proposed in this research

Advancements in technology have the potential to significantly impact the efficiency and scalability of algorithms proposed in this research:

Computational Power: With advancements in hardware capabilities such as faster processors and increased memory capacity, algorithms like BRKGA could run more efficiently on larger datasets.
Parallel Processing: Utilizing parallel processing techniques enabled by modern computing architectures could speed up algorithm execution times for solving large-scale instances.
Cloud Computing: Leveraging cloud computing resources allows for scalable deployment of optimization algorithms, enabling them to handle larger problem sizes effectively.
Machine Learning Integration: Integrating machine learning techniques into heuristic algorithms could enhance their performance through adaptive learning from data patterns.

By leveraging technological advancements effectively, researchers can enhance the efficiency and scalability of these algorithms for tackling real-world logistics challenges more effectively than before.