The team orienteering problem

Chao, I-Ming; Golden, Bruce L.; Wasil, Edward

doi:10.1016/0377-2217(94)00289-4

Cited by 445 publications

(335 citation statements)

References 4 publications

Supporting

Mentioning

327

Contrasting

Unclassified

Order By: Relevance

“…The objective is to identify the customers which maximize the total collected profit while satisfying the given time limits for each vehicle. The TOP appeared first under the name Multiple Tour Maximum Collection Problem in [25], but the definition of TOP was introduced by Chao et al in [26] that proposed a heuristic algorithm. Tabu search approaches have been recently presented by [27,28].…”

Section: Related Workmentioning

confidence: 99%

Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs

Ceschia

Gaspero

Schaerf

2010

J Sched

View full text Add to dashboard Cite

In this work we formalize a new complex variant of the classical vehicle routing problem arising from a real-world application. Our formulation includes a heterogeneous fleet, a multi-day planning horizon, a complex carrier-dependent cost function for the vehicles, and the possibility of leaving orders unscheduled.We propose a metaheuristic approach based on tabu search and on a complex combination of neighborhood relations. Finally, we perform an experimental analysis to tune and compare different combinations, highlighting the most important features of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs

Ceschia

Gaspero

Schaerf

2010

J Sched

View full text Add to dashboard Cite

show abstract

“…The problem is to compute a route that (1) starts at the given starting location, (2) have a length that does not exceed the given distance limit and (3) goes via objects whose total score is maximal. The orienteering problem has been studied extensively [2,5] and several heuristics [1,4,8,9,16] and approximation algorithms [12] were proposed for it.…”

Section: Related Workmentioning

confidence: 99%

Route Search over Probabilistic Geospatial Data

Kanza

Safra

Sagiv

2009

Advances in Spatial and Temporal Databases

View full text Add to dashboard Cite

Abstract. In a route search over geospatial data, a user provides terms for specifying types of geographical entities that she wishes to visit. The goal is to find a route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geospatial entities that are relevant to the provided search terms. Earlier work studied the problem of finding a route that is effective in the sense that its length does not exceed a given limit, the relevancy of the objects is as high as possible, and the route visits a single object from each specified type. This paper investigates route search over probabilistic geospatial data. It is shown that the notion of an effective route requires a new definition and, specifically, two alternative semantics are proposed. Computing an effective route is more complicated, compared to the non-probabilistic case, and hence necessitates new algorithms. Heuristic methods for computing an effective route, under either one of the two semantics, are developed. (Note that the problem is NP-hard.) These methods are compared analytically and experimentally. In particular, experiments on both synthetic and realworld data illustrate the efficiency and effectiveness of these methods in computing a route under the two semantics.

show abstract

“…The GOP is a generalization of the orienteering problem (OP) and the main difference between the two is that each city in GOP has multiple scores while each city in OP has only one score [14][15][16]. Let V be the set of N points and E the set of edges between points in V. G = {V, E} is a complete graph.…”

Section: Generalized Orienteering Problemmentioning

confidence: 99%

Harmony Search for Generalized Orienteering Problem: Best Touring in China

Geem

Tseng

Park

2005

Lecture Notes in Computer Science

165

View full text Add to dashboard Cite

Abstract. In order to overcome the drawbacks of mathematical optimization techniques, soft computing algorithms have been vigorously introduced during the past decade. However, there are still some possibilities of devising new algorithms based on analogies with natural phenomena. A nature-inspired algorithm, mimicking the improvisation process of music players, has been recently developed and named Harmony Search (HS). The algorithm has been successfully applied to various engineering optimization problems. In this paper, the HS was applied to a TSP-like NP-hard Generalized Orienteering Problem (GOP) which is to find the utmost route under the total distance limit while satisfying multiple goals. Example area of the GOP is eastern part of China. The results of HS showed that the algorithm could find good solutions when compared to those of artificial neural network.

show abstract

The team orienteering problem

Cited by 445 publications

References 4 publications

Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs

Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs

Route Search over Probabilistic Geospatial Data

Harmony Search for Generalized Orienteering Problem: Best Touring in China

Contact Info

Product

Resources

About