Time-constrained maximal covering routing problem

Abstract

We introduce the time-constrained maximal covering routing problem (TCMCRP), as a generalization of the covering salesman problem. In this problem, we are given a central depot, a set of facilities and several customers which are located within a pre-determined coverage distance of available facilities. Each facility can supply the demand of some customers which are within its coverage radius. Starting from the depot, the goal is to maximize the total number of covered customers, by constructing a set of p length constraint Hamiltonian cycles. We have proposed a mixed integer linear programming model and three heuristic algorithms, namely iterated local search (ILS), tabu search (TS) and variable neighborhood search (VNS), to solve the problem. Extensive computational tests on this problem and some of its variants clearly indicate the effectiveness of the developed solution methods.

A multi‐vehicle covering tour problem with speed optimization

“…Vehicle routes are generated by choosing actions (i.e., idle at a waypoint on the route or add an arc to traverse to the route) based on their scores until the operational deadline T is violated or all targets have been covered sufficiently. Similar to the methods implemented by [2,39], these scores represent the value-to-cost ratio of each action. The action with the highest score is selected, so long as it neither extends the vehicle's schedule beyond the operational deadline nor creates any invalid arcs (i.e., arcs that are not in the network).…”

“…Note that it is not possible for v ∈ M(L 1 ) but v ∉ M(L 2 ) since condition (ii) must be satisfied in order for L 1 to dominate L 2 . We define c extra 0 (L 2 ) ∶= 0.…”

Last mile delivery routing problem using autonomous electric vehicles

“…• Time-constrained Maximal Covering Routing Problem (TCMCRP) (Amiri & Salari, 2019;Sinnl, 2021), which Generalizes TCMCSP to find the K cycles (routes). In both TCMCSP and TCMCRP, customers are either visited, or covered, or left isolated.…”

Mining subgraph coverage patterns from graph transactions

“…More recently, the work in [ 41 ] proposed TOPKLS, a local search algorithm for finding diversified top- k cliques from a given graph. The notion of coverage has been well explored to solve the maximum coverage problem in facility location [ 6 ]. However, these works explore the notion of coverage for a single graph as opposed to a GTD, which is our focus.…”