Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Routing winter gritting vehicles
CO89 Selected papers of the conference on Combinatorial Optimization
Sensor based motion planning: the hierarchical generalized Voronoi graph
Sensor based motion planning: the hierarchical generalized Voronoi graph
Approximation Algorithms for Some Postman Problems
Journal of the ACM (JACM)
Approximation algorithms for lawn mowing and milling
Computational Geometry: Theory and Applications
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
A Short History of Cleaning Robots
Autonomous Robots
A cutting plane algorithm for the capacitated arc routing problem
Computers and Operations Research
Improvement Procedures for the Undirected Rural Postman Problem
INFORMS Journal on Computing
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
A deterministic tabu search algorithm for the capacitated arc routing problem
Computers and Operations Research
Robotics and Autonomous Systems
Sensor-based coverage with extended range detectors
IEEE Transactions on Robotics
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Capacitated arc routing problem (CARP) is the determination of vehicle tours that serve all positive-demand edges (required edge) exactly once without exceeding vehicle capacity while minimizing sum of all tour costs. In CARP, total demand of a tour is calculated by means of all required edges on the tour. In this study, a new CARP variation is introduced, which considers not only required edges but also traversed edges while calculating total demand of the tour. The traversing demand occurs when the traversed edge is either servicing or non-servicing (deadheading). Since the new CARP formulation incurs deadheading edge demands it is called CARP with deadheading demands. An integer linear model is given for the problem which is used to solve small-sized instances, optimally. A constructive heuristic is presented to solve the problem which is a modified version of a well-known CARP heuristic. Furthermore, two post-optimization procedures are presented to improve the solution of the heuristic algorithm. The effectiveness of the proposed methods is shown on test problems, which are obtained by modifying CARP test instances.