A hybrid variable neighborhood search for the Orienteering Problem with mandatory visits and exclusionary constraints

Palomo-Martínez, Pamela J.; Salazar-Aguilar, M. Angélica; Laporte, Gilbert

doi:10.1016/j.cor.2015.11.007

Cited by 28 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of papers that are closely related to our work appeared after the publication of the above surveys. A hybrid heuristic composed of a greedy randomized adaptive search procedure (GRASP) and a variable neighborhood search (VNS) was proposed by [15] to solve a generalization of the OP. This variant contains constraints imposing mandatory visits and incompatibilities among nodes.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Vidigal Corrêa,

Dong,

Iori

et al. 2023

Networks

View full text Add to dashboard Cite

This paper addresses a real‐world multi‐period orienteering problem arising in a large Italian company that needs to patrol an area in order to provide security services to a set of customers. Each customer requires different services on a weekly basis. Some services are mandatory, while others are optional. It might be impossible to perform all optional services, and each of them is assigned a score when performed. The challenge is to determine a set of routes, one per day, that maximizes a weighted sum of the total collected score and total working time, while meeting several operational constraints, including hard time windows, maximum riding time, minimum number of services performed, and minimum time between two consecutive visits for the same service at the same customer. To solve the problem, we propose an iterated local search that invokes at each iteration an inner variable neighborhood descent procedure. Computational tests performed on a large number of real‐world instances prove that the developed algorithm is very efficient, and finds in a short time solutions that are consistently better than those produced by a mathematical model, and those in use at the company.

show abstract

Section: Brief Literature Reviewmentioning

confidence: 99%

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Vidigal Corrêa,

Dong,

Iori

et al. 2023

Networks

View full text Add to dashboard Cite

show abstract

“…In the same year, Palomo-Martínez et, al. [16] solved the OP problem by using VNS and GRASP techniques. Li and Hu [17] merged OPTW with compulsory POIs and modeled it as the OP with compulsory nodes and time windows (OPCNTW).…”

Section: B Op For Ttdpmentioning

confidence: 99%

“…The second one is the minimum total distance which is often selected when the tourists consist of elders or young children in tours. OP 1998 [14], 2017 [15], 2017 [16]  P 2009 [23], 2015 [24], 2015 [25], 2017 [26], 2018 [27], 2018 [28], 2020 [29] P1 P2 OPTW 2007 [4]  P 2011 [17]  P 2016 [30] P1 P2 The proposed model    P (D) TOPTW. 2011 [11]  P 2019 [13]    P 2015 [34], 2016 [35] * * 2018 [31] P1 P2 2018 [32], 2019 [33] P (D)…”

Section: Itinerary Selection From Pareto Frontmentioning

confidence: 99%

Multi-Objective Trip Planning With Solution Ranking Based on User Preference and Restaurant Selection

2022

View full text Add to dashboard Cite

The tourist trip design problem (TTDP) helps the trip planners, such as tourists, tour companies, and government agencies, automate their trip planning. TTDP solver chooses and sequences an optimal subset of point of interest (POIs), which adhere to the POIs attributes and tourist preferences, and then generates a travel itinerary that maximizes their pleasure. However, the traditional TTDP does not include the lunch period at a local restaurant, which causes the rest of the itinerary in the afternoon to shift, nor compulsory POIs that the trip planners must be included in the itinerary. Moreover, as tourism contributes to high greenhouse gas emissions, especially from its transportation, minimizing the itinerary's total distance is also considered. Unfortunately, this objective conflicts with the profit scores; no single itinerary can optimize both objectives simultaneously. Hence, the multi-objective technique and the results of non-dominated itineraries can be organized as a Pareto front. The trip planners can choose one suitable itinerary from the Pareto front based on their preferences. To address these real-world issues, we formulate a new variant of the well-known orienteering problem with time windows (OPTW) called the multi-objective orienteering problem with Time Windows, Restaurant Selection, and Compulsory POIs (MOPTW-RSCP). The proposed problem is provided with a mathematical formulation and two exact algorithms for solving them, i.e., greedy and branch-and-cut Pareto-based techniques. The algorithms' performance is tested against the Rattanakosin island (the old city of Bangkok) dataset. We conduct 24 test cases, and the computational results confirm the algorithms' efficiency.INDEX TERMS branch-and-cut algorithm, greedy algorithm, knee solution, low carbon tourism, multiobjective optimization, orienteering with time windows, point-of-interests, recommendation, solution ranking, tourist trip design problem

show abstract

“…Hence, a heuristic approach is essential to tackle such instances. Recent metaheuristic approaches are shown to be the most effective implementations to solve the OP in terms of solution quality and execution time (Ke et al [9], Santini [10], Palomo-Martínez et al [11], Verbeeck et al [12]).…”

Section: Only One Fixed Depot (Central Node)mentioning

confidence: 99%

A Multi-Start Granular Skewed Variable Neighborhood Tabu Search for the Roaming Salesman Problem

Shahmanzari

Aksen

2021

Applied Soft Computing

View full text Add to dashboard Cite

A hybrid variable neighborhood search for the Orienteering Problem with mandatory visits and exclusionary constraints

Cited by 28 publications

References 36 publications

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Multi-Objective Trip Planning With Solution Ranking Based on User Preference and Restaurant Selection

A Multi-Start Granular Skewed Variable Neighborhood Tabu Search for the Roaming Salesman Problem

Contact Info

Product

Resources

About