The Mobile Tourist Guide: An OR Opportunity

Vansteenwegen, Pieter; Oudheusden, Dirk Van

doi:10.1057/ori.2007.17

Cited by 188 publications

(100 citation statements)

References 7 publications

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“…where α is a parameter that is set by the user and is generally smaller than the parameter α as used in constraint (16). The setting of α is critical and we will provide a detailed discussion about the same in our experimental results section.…”

Section: Solving Sop With Chance Constraintsmentioning

confidence: 99%

“…While OPs have been used to represent problems like vehicle routing [7] and production scheduling [2], in this work, we are motivated by the problem of tourist trip design problems similar to the one described in Vansteenwegen et al [16,1]. Specifically, we address the problem of providing risk sensitive route guidance to visitors at theme parks [10], where the presence of queues at nodes lead to stochastic travel time between nodes.…”

Section: Introductionmentioning

confidence: 99%

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Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems

Varakantham

Kumar

2013

Algorithmic Decision Theory

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Abstract. Orienteering problems (OPs) are typically used to model routing and trip planning problems. OP is a variant of the well known traveling salesman problem where the goal is to compute the highest reward path that includes a subset of nodes and has an overall travel time less than the specified deadline. Stochastic orienteering problems (SOPs) extend OPs to account for uncertain travel times and are significantly harder to solve than deterministic OPs. In this paper, we contribute a scalable mixed integer LP formulation for solving risk aware SOPs, which is a principled approximation of the underlying stochastic optimization problem. Empirically, our approach provides significantly better solution quality than the previous best approach over a range of synthetic benchmarks and on a real-world theme park trip planning problem.

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Section: Solving Sop With Chance Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems

Varakantham

Kumar

2013

Algorithmic Decision Theory

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“…After preprocessing, data is transmitted within short distance and small range and stored in database built in perception layer. Network layer function is data transmission and share, which is operated by Internet or local area network to realize safe and steady transmission [4,5,6]. Application layer function is data processing and dissemination, which uses cloud computing, fuzzy recognition, etc.…”

Section: Structure Of Internet Of Thingsmentioning

confidence: 99%

Foundation Of City Tourism Information Service Model Based On Internet Of Things

Zhou¹,

Pang²,

Li³

et al. 2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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On analyzing Internet of Things, this paper brings forward the city tourism information service model. It designs the multiclass structure of the model which contains threes layers and sub-layers, on which base, related databases and subdatabases are created to store tourism data information. And then it develops the city tourism information service system and analyzes the main functions of the system, of which, the system can output selected tourism information, tourism map and tourism route to provide service for tourists.

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“…The TTDP are studied by [5] and [6] where electronic tourist guide was developed using an optimization method. Vansteenwegen et al [6] employed an effective iterated local search that included an insert and shake steps to escape from local optima.…”

Section: Introductionmentioning

confidence: 99%

Economic tour package model using heuristic

Abdul-Rahman¹,

Benjamin²,

Bakar³

2014

AIP Conference Proceedings

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Abstract.A tour-package is a prearranged tour that includes products and services such as food, activities, accommodation, and transportation, which are sold at a single price. Since the competitiveness within tourism industry is very high, many of the tour agents try to provide attractive tour-packages in order to meet tourist satisfaction as much as possible. Some of the criteria that are considered by the tourist are the number of places to be visited and the cost of the tour-packages. Previous studies indicate that tourists tend to choose economical tour-packages and aiming to visit as many places as they can cover. Thus, this study proposed tour-package model using heuristic approach. The aim is to find economical tour-packages and at the same time to propose as many places as possible to be visited by tourist in a given geographical area particularly in Langkawi Island. The proposed model considers only one starting point where the tour starts and ends at an identified hotel. This study covers 31 most attractive places in Langkawi Island from various categories of tourist attractions. Besides, the allocation of period for lunch and dinner are included in the proposed itineraries where it covers 11 popular restaurants around Langkawi Island. In developing the itinerary, the proposed heuristic approach considers time window for each site (hotel/restaurant/place) so that it represents real world implementation. We present three itineraries with different time constraints (1-day, 2-day and 3-day tour-package). The aim of economic model is to minimize the tour-package cost as much as possible by considering entrance fee of each visited place. We compare the proposed model with our uneconomic model from our previous study. The uneconomic model has no limitation to the cost with the aim to maximize the number of places to be visited. Comparison between the uneconomic and economic itinerary has shown that the proposed model have successfully achieved the objective that minimize the tour cost and cover maximum number of places to be visited.

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The Mobile Tourist Guide: An OR Opportunity

Cited by 188 publications

References 7 publications

Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems

Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems

Foundation Of City Tourism Information Service Model Based On Internet Of Things

Economic tour package model using heuristic

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