Advances in Meter Reading: Heuristic Solution of the Close Enough Traveling Salesman Problem over a Street Network

Shuttleworth, Robert; Golden, Bruce L.; Smith, Susan; Wasil, Edward

doi:10.1007/978-0-387-77778-8_22

Cited by 35 publications

(28 citation statements)

References 8 publications

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“…In this version, each neighborhood is a compact set in the plane in a way that by intersecting this set, all the demands located at its neighborhood will be covered. Several approximation algorithms have also been proposed for this problem (see, e.g., Arkin & Hassin, 1994;Gulczynski, Heath, &Price, 2006 andShuttleworth, Golden, Smith, &Wasil, 2008).…”

Section: Introductionmentioning

confidence: 99%

Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem

Salari

Reihaneh

Sabbagh

2015

Computers & Industrial Engineering

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Section: Introductionmentioning

confidence: 99%

Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem

Salari

Reihaneh

Sabbagh

2015

Computers & Industrial Engineering

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“…Several generalizations and special cases of the CSP have been studied in the literature, see e.g. Gulczynski et al [2006], Shuttleworth et al [2008], Golden et al [2012], Behdani and Smith [2014]. Similar to the CSP, in the TSP-D the truck does not have to visit all nodes.…”

Section: Related Literaturementioning

confidence: 99%

Optimization Approaches for the Traveling Salesman Problem with Drone

Agatz

Bouman²,

Schmidt³

2015

SSRN Journal

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The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last-mile to their customers. One technology-enabled opportunity that recently has received much attention is the use of a drone to support deliveries. An innovative last-mile delivery concept in which a truck collaborates with a drone to make deliveries gives rise to a new variant of the traveling salesman problem (TSP) that we call the TSP with drone. In this paper, we model this problem as an IP and develop several fast route first-cluster second heuristics based on local search and dynamic programming. We prove worst-case approximation ratios for the heuristics and test their performance by comparing the solutions to the optimal solutions for small instances. In addition, we apply our heuristics to several artificial instances with different characteristics and sizes. Our experiments show that substantial savings are possible with this concept in comparison to truck-only delivery.

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“…Moreover, in some developing countries, mobile health care delivery teams operate at a selected number of sites, to which the supported people must travel. The arc routing pendant of the problem occurs in automatic meter reading (see Shuttleworth et al [125] and Chapter 13 in [35]). …”

Section: Delivery and Collectionmentioning

confidence: 99%

Chapter 1: The Family of Vehicle Routing Problems

Irnich¹,

Toth²,

Vigo³

2014

Vehicle Routing

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A generic verbal definition of the family of vehicle routing problems can be the following:Given: A set of transportation requests and a fleet of vehicles.The problem is then to find a plan for the following:Task: Determine a set of vehicle routes to perform all (or some) transportation requests with the given vehicle fleet at minimum cost; in particular, decide which vehicle handles which requests in which sequence so that all vehicle routes can be feasibly executed.In this type of problem, subsumed under the term Vehicle Routing Problem (VRP), the transportation requests to be served are generally concentrated in specific points of a road network as opposed to the Arc Routing Problems (ARP; see the companion book by Corberán and Laporte [35]), where the requests are dispersed along the arcs, i.e., street segments of the underlying road network. The following sections will shed light on the basic VRP components, which are the transportation requests and how they can be performed, the fleet of vehicles, the related costs and profits (if relevant), and the feasibility of routes.Before going into the details, however, we discuss the economic relevance of computersupported vehicle routing. Indeed, the large number of real-world applications have widely shown that the use of computerized solution methods for the solution of VRP, both at the planning and the operational levels, yields substantial savings in the global transportation costs. The success of the utilization of optimization techniques is due not only to the power of the current computer systems and to the full integration of the information systems into the operations and commercial processes, but it can also be attributed to the development of rigorous mathematical models, which are able to take into account al-1 Downloaded 12/15/14 to 132.236.27.111. Redistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php 2 Chapter 1. The Family of Vehicle Routing Problems most all the characteristics of the VRP arising in real-world applications. Furthermore, the corresponding algorithms and their computer implementations (software tools) play an essential role in finding high-quality feasible solutions for real-world instances within acceptable computing times. Compared to procedures not based on optimization techniques, significant cost savings and a better utilization of the vehicle fleet can be achieved. In addition, by means of such planning software it is possible to improve the automation, standardization, and integration into the organizations' overall planning processes, leading to less time-consuming and more cost-efficient planning processes with respect to manual planning. Moreover, computerized planning allows planners to compare several different planning scenarios, and herewith to choose a best one through a careful and fast evaluation of cost and service-related performance indicators.In the last years, software tools integrating telematics services (electronic data transmission between vehicles and planners) have b...

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Advances in Meter Reading: Heuristic Solution of the Close Enough Traveling Salesman Problem over a Street Network

Cited by 35 publications

References 8 publications

Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem

Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem

Optimization Approaches for the Traveling Salesman Problem with Drone

Chapter 1: The Family of Vehicle Routing Problems

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