A two-stage approach to the orienteering problem with stochastic weights

Evers, Lanah; Glorie, Kristiaan; Ster, Suzanne van der; Barros, Ana Isabel; Monsuur, Herman

doi:10.1016/j.cor.2013.09.011

Cited by 68 publications

(74 citation statements)

References 29 publications

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“…The difference between the problem presented by Evers et al [39,42] and the TD-OPSWTW is that no penalty is received for scheduled vertices that are not visited. Furthermore, for the TD-OPSWTW all vertices are known in advance and an a-priori route needs to be found.…”

Section: -17mentioning

confidence: 99%

“…Solution methods for the OPSW or relevant problems similar to the OPSW were discussed in [21,41,42,106,107,134,138]. Note that in most of these works the term weight is used for the travel time and capacity for the time budget or t max .…”

Section: Orienteering Problems With Stochastic Travel Timesmentioning

confidence: 99%

“…The military application of the OP considers unmanned aerial vehicle (UAV) mission planning to collect intelligence information about different locations in the area of operations [39,40,42,101,115,155]. The aim of these missions is to acquire as much information as possible during the flight, while the length of the flight is limited by the available fuel capacity of the UAV.…”

Section: Applicationsmentioning

confidence: 99%

“…Evers et al [42] study an orienteering problem with a stochastic travel time on each arc and a hard constraint on the total time of a route. They introduce a two-stage recourse model where a recourse action of aborting the route (returning to the depot) is taken in the second stage.…”

Section: -9mentioning

confidence: 99%

“…In general only the work of Lau et al [86] considers time-dependent stochastic travel times, all other papers use timeindependent stochastic travel times. Recall that in [21,42,86,134,138] no timewindows are incorporated.…”

Section: Position In the Literaturementioning

confidence: 99%

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Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Verbeeck¹

2016

4OR-Q J Oper Res

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Section: -17mentioning

confidence: 99%

Section: Orienteering Problems With Stochastic Travel Timesmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: -9mentioning

confidence: 99%

Section: Position In the Literaturementioning

confidence: 99%

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Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Verbeeck¹

2016

4OR-Q J Oper Res

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Maximizing pleasure in day tours: Utility theory approach

Jana

Mitra

2020

Journal of Tourism Research

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A personalized day tour planning problem is studied to maximize the total pleasure gained from the tour. The concept of utility theory is employed for this purpose. Considering the uniform and diminishing marginal pleasure intensities, two zero-one mixed integer nonlinear programming models are proposed. Both the models can accommodate preferred time slots, visits to specific spots, and spots diversity. A branch-and-bound algorithm is used to solve the proposed models. Finally, they are applied to solve the day tour problem for Delhi, India. The tour plan obtained using the diminishing marginal pleasure intensity offers an improved value of total pleasure.

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Team orienteering with uncertain rewards and service times with an application to phlebotomist intrahospital routing

Jin¹,

Thomas

2019

Networks

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This study focuses on the intrahospital routing of phlebotomists at the University of Iowa Hospitals and Clinics (UIHC). Phlebotomists are responsible for drawing specimens from patients based on doctors' orders. The results of the analysis of these specimens play an important role in determining patient treatment. However, the demand for phlebotomists is likely to outpace supply over the next few years. Therefore, it is important to improve the efficiency of phlebotomists. In this paper, we formulate the phlebotomist intrahospital routing problem as a team orienteering problem with stochastic rewards and service times. The rewards and service times are particularly interesting as they are the result of a queueing process. We present an a priori solution approach and derive a method for efficiently sampling the value of a solution, a value that cannot be determined analytically. Finally, we demonstrate that our proposed approach outperforms the current practice at UIHC. KEYWORDShealthcare operations, heuristic, Markov decision process, phlebotomy, queueing, service routing INTRODUCTIONResearch shows that laboratory performance affects approximately 60%-70% of the most critical medical decisions related to admission, discharge, and the medication of inpatients [12]. Phlebotomists are a key part of these laboratory services. Phlebotomists primarily draw blood, urine, and other samples from patients and are in fact often the patients' only contact with medical laboratories. With an increasing demand for healthcare services, the demand for phlebotomists is expected to grow 25% between 2014 and 2024 [5]. With the demand for healthcare workers at an all-time high [21] and healthcare dominating the American Staffing Associations Skills Gap Index, a measure of the hardest to fill jobs [1], it seems unlikely that the demand for phlebotomists can be met. Thus, increasing the efficiency of phlebotomists will be crucial to avoid delays in the admissions, discharge, and medication of patients.With this need in mind, this study focuses on the intrahospital routing of phlebotomists. This study is specifically motivated by the Department of Pathology at the University of Iowa Hospitals and Clinics (UIHC). This problem falls into the emerging category of routing problems called service routing problems. In service routing problems, service providers are routed among various customer locations at which a service must be performed. In contrast to traditional routing problems, however, attributes of the particular provider performing the service and/or the attributes of the service to be provided lead to heterogeneous and often stochastic service times. In addition to the intrahospital routing problem studied in this paper, examples of service routing problems include home healthcare routing [32] and service technician routing [11]. In coming years, the set of such problems will only increase. This increase will in part be due to the ubiquity of data that will allow us to solve hidden routing problems such Networks. 2019;73:453-465....

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A two-stage approach to the orienteering problem with stochastic weights

Cited by 68 publications

References 29 publications

Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Maximizing pleasure in day tours: Utility theory approach

Team orienteering with uncertain rewards and service times with an application to phlebotomist intrahospital routing

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