262,191 research outputs found
Ride Sharing with a Vehicle of Unlimited Capacity
A ride sharing problem is considered where we are given a graph, whose edges are equipped with a travel cost, plus a set of objects, each associated with a transportation request given by a pair of origin and destination nodes. A vehicle travels through the graph, carrying each object from its origin to its destination without any bound on the number of objects that can be simultaneously transported. The vehicle starts and terminates its ride at given nodes, and the goal is to compute a minimum-cost ride satisfying all requests. This ride sharing problem is shown to be tractable on paths by designing a O(h*log(h)+n) algorithm, with h being the number of distinct requests and with n being the number of nodes in the path. The algorithm is then used as a subroutine to efficiently solve instances defined over cycles, hence covering all graphs with maximum degree 2. This traces the frontier of tractability, since NP-hard instances are exhibited over trees whose maximum degree is 3
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire stationâs receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbulâs road network, and solve two location modelsâset-covering and maximal-coveringâas what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the cityâs fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire stationâs receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbulâs road network, and solve two location modelsâset-covering and maximal-coveringâas what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the cityâs fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
A taxonomy for emergency service station location problem
The emergency service station (ESS) location problem has been widely
studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically
consider the type of the emergency, the objective function, constraints, model
assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions
On The Continuous Coverage Problem for a Swarm of UAVs
Unmanned aerial vehicles (UAVs) can be used to provide wireless network and
remote surveillance coverage for disaster-affected areas. During such a
situation, the UAVs need to return periodically to a charging station for
recharging, due to their limited battery capacity. We study the problem of
minimizing the number of UAVs required for a continuous coverage of a given
area, given the recharging requirement. We prove that this problem is
NP-complete. Due to its intractability, we study partitioning the coverage
graph into cycles that start at the charging station. We first characterize the
minimum number of UAVs to cover such a cycle based on the charging time, the
traveling time, and the number of subareas to be covered by the cycle. Based on
this analysis, we then develop an efficient algorithm, the cycles with limited
energy algorithm. The straightforward method to continuously cover a given area
is to split it into N subareas and cover it by N cycles using N additional
UAVs. Our simulation results examine the importance of critical system
parameters: the energy capacity of the UAVs, the number of subareas in the
covered area, and the UAV charging and traveling times.We demonstrate that the
cycles with limited energy algorithm requires 69%-94% fewer additional UAVs
relative to the straightforward method, as the energy capacity of the UAVs is
increased, and 67%-71% fewer additional UAVs, as the number of subareas is
increased.Comment: 6 pages, 6 figure
Efficient Multi-Robot Coverage of a Known Environment
This paper addresses the complete area coverage problem of a known
environment by multiple-robots. Complete area coverage is the problem of moving
an end-effector over all available space while avoiding existing obstacles. In
such tasks, using multiple robots can increase the efficiency of the area
coverage in terms of minimizing the operational time and increase the
robustness in the face of robot attrition. Unfortunately, the problem of
finding an optimal solution for such an area coverage problem with multiple
robots is known to be NP-complete. In this paper we present two approximation
heuristics for solving the multi-robot coverage problem. The first solution
presented is a direct extension of an efficient single robot area coverage
algorithm, based on an exact cellular decomposition. The second algorithm is a
greedy approach that divides the area into equal regions and applies an
efficient single-robot coverage algorithm to each region. We present
experimental results for two algorithms. Results indicate that our approaches
provide good coverage distribution between robots and minimize the workload per
robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), 201
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