Applications of the OP

Vansteenwegen, Pieter; Gunawan, Aldy

doi:10.1007/978-3-030-29746-6_7

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…Indeed, as reported by Vansteenwegen et al. (2019a) and Ruiz‐Meza and Montoya‐Torres (2022), almost all papers in the TTDP literature use the OP (and other extended variants) to formulate the TTDP. Moreover, various extensions of the TTDP can also be formulated as the variants of the TOP, OPTW, or TOPTW.…”

Section: Problem Descriptionmentioning

confidence: 95%

“…The reader is referred to Vansteenwegen et al. (2019a) and Ruiz‐Meza and Montoya‐Torres (2022) for a comprehensive list of the different variants.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Vansteenwegen et al. (2019a) listed several TTDP variants and Ruiz‐Meza and Montoya‐Torres (2022) gave a classification of different variants and solution techniques. Variants include heterogeneous preferences of tourists, multiple TW, different classes of users, multiobjective function, to name a few.…”

Section: Problem Descriptionmentioning

confidence: 99%

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The bus sightseeing problem

Zhang

Tarantilis

et al. 2022

Int Trans Operational Res

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The basic characteristic of vehicle routing problems with profits (VRPP) is that locations to be visited are not predetermined. On the contrary, they are selected in pursuit of maximizing the profit collected from them. Significant research focus has been directed toward profitable routing variants due to the practical importance of their applications and their interesting structure, which jointly optimizes node selection and routing decisions. Profitable routing applications arise in the tourism industry aiming to maximize the profit score of attractions visited within a limited time period. In this paper, a new VRPP is introduced, referred to as the bus sightseeing problem (BSP). The BSP calls for determining bus tours for transporting different groups of tourists with different preferences on touristic attractions. Two interconnected decision levels have to be jointly tackled: assignment of tourists to buses and routing of buses to the various attractions. A mixed-integer programming formulation for the BSP is provided and solved by a Benders decomposition algorithm. For large-scale instances, an iterated local search based metaheuristic algorithm is developed with some tailored neighborhood operators. The proposed methods are tested on a large family of test instances, and the obtained computational results demonstrate the effectiveness of the proposed solution approaches.

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Section: Problem Descriptionmentioning

confidence: 95%

“…The reader is referred to Vansteenwegen et al. (2019a) and Ruiz‐Meza and Montoya‐Torres (2022) for a comprehensive list of the different variants.…”

Section: Problem Descriptionmentioning

confidence: 99%

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The bus sightseeing problem

Zhang

Tarantilis

et al. 2022

Int Trans Operational Res

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“…OPs and TOPs naturally arise in areas such as transportation and logistics, where, for instance, sites usually correspond to customers and agents correspond to vehicles used for the delivery of goods under different constraints [6][7][8][9]. Orienteering models have however been used in a much wider range of application scenarios, including recommender systems for the design of tourist trips, allocation of resources in civilian and military operations, emergency response, search and rescue, and task outsourcing using a crowdsourcing mechanism (see [10] for a recent and extensive review of applications of OP models).…”

Section: Introductionmentioning

confidence: 99%

A clustering metaheuristic for large orienteering problems

Elzein

2022

PLoS ONE

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The Orienteering Problem is a routing problem that commonly appears in mapping, transportation, distribution, and scheduling scenarios. The Orienteering Problem is a challenging NP-hard problem, such that obtaining optimal or near optimal solutions usually requires a significant amount of computation for large and even moderately large instances. As a result, existing algorithms cannot effectively be utilized for solving large Orienteering Problems in an online setting, which is often required in real-world applications where a plan has to be iteratively computed. For instance, a planner might need to adapt to changes in the scenario or in the environment (e.g., this is common in goods delivery, as well as in mobile robotic scenarios for coverage, monitoring, and surveillance). Motivated by these limitations, we propose a multi-stage clustering-based metaheuristic for tackling large Orienteering Problems in an effective, strategically controlled amount of computation time. The metaheuristic starts by decomposing the problem into smaller, independent sub-problems that are efficiently solved using an algorithm of choice, sequentially or in parallel. The obtained solutions are then merged to form a solution for the original problem, and then further optimized and processed to ensure feasibility. The metaheuristic aims to dramatically improve the computation time of a given Orienteering Problem algorithm without a significant decrease in the solution quality of that algorithm, especially for large Orienteering Problems. We have validated the effectiveness of the proposed metaheuristic design through a set of computational experiments. In particular, using a state-of-the-art heuristic and an exact algorithm, we have shown that it is significantly beneficial to use the Orienteering Problem algorithm plugged into our metaheuristic, as opposed to using it as a standalone algorithm. This was found to be especially true for large problems. As a result, large instances of Orienteering Problems can be effectively tackled within reasonable time bounds even for online application scenarios.

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