A decision support framework for home health care transportation with simultaneous multi-vehicle routing and staff scheduling synchronization

Nasir, Jamal Abdul; Kuo, Yong-Hong

doi:10.1016/j.dss.2020.113361

Cited by 59 publications

(23 citation statements)

References 62 publications

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“…Several studies have argued that the aim of train rescheduling is to reduce passenger inconvenience when published schedules incur traffic disruptions [ 12 , 13 ]. This is because the loss of passenger time and satisfaction will be implicitly and irrevocably considered [ 14 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Kang

Xiao

Sun

et al. 2022

Decision Support Systems

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Travel restriction measures have been widely implemented to curb the continued spread of COVID-19 during the Chinese Lunar New Year celebrations. Many operation lines and train schedules of China's railway were either heavily adjusted or canceled. In this study, a mixed-integer linear programming model and a two-step solution algorithm were developed to handle such large-scale adjustments. The formulation considers a flexible time window for each operation line and locomotive traction operations, and minimizes the number of locomotives utilized with their total idle time for train rescheduling and locomotive assignment, respectively. The solution algorithm determines the minimum locomotive fleet size based on the optimal train rescheduling results; it then reduces the traction idle time of locomotives. In response to the uncertainty of COVID-19, two tailored approaches were also designed to recover and remove operation lines, which can insert and cut operation lines based on the results of locomotive assignment. Finally, we conducted a case study of the Beijing-Tianjin intercity railway from the start of the COVID-19 outbreak in December 2019 to the recovery of operations.

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Section: Literature Reviewmentioning

confidence: 99%

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Kang

Xiao

Sun

et al. 2022

Decision Support Systems

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“…They were designed using ALNS to reduce the total cost. Nasir ( 2020 ), under Synchronization requirements between HHC staff and Home Delivery Vehicles (HDVs) visits, a MILP model is developed to characterize the optimization problem for minimizing the total cost. A Hybrid Genetic Algorithm (HGA) presented to suggest HHC planning decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this realistic world, innovations in technologies like Global positioning systems (GPS), Radio Frequency Identification (RFID) tags, smart mobile updates etc., create a significant impact on logistics and transportation services. These advancements in technologies help to perform fleet management effectively and proficiently (Liao 2016 ; Nasir 2020 ). Hence, many researchers have been attracted to investigate more about the Dynamic Vehicle Routing Problem (DVRP) (Larsen 2002 ; Euchi 2015 : Fikar 2017 ; Demirbilek 2019 ; Sangeetha 2020a ).…”

Section: Introductionmentioning

confidence: 99%

Mutated cuckoo search algorithm for dynamic vehicle routing problem and synchronization occurs within the time slots in home healthcare

Sangeetha

Srinivasan

2021

Int J Syst Assur Eng Manag

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“…Decision support tools have been developed in the literature to improve the efficiency of healthcare delivery services such as improving scheduling of elective patients [ 31 ], nurse routing for home dialysis [ 32 ] and other home healthcare logistics [ 33 ], to name a few. Our work complements these efforts by addressing the unique aspects of online, near real-time, assignment of patients to medical professionals.…”

Section: Related Workmentioning

confidence: 99%

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Rosemarin

Rosenfeld

Lapp

et al. 2021

Sensors

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Central to any medical domain is the challenging patient to medical professional assignment task, aimed at getting the right patient to the right medical professional at the right time. This task is highly complex and involves partially conflicting objectives such as minimizing patient wait-time while providing maximal level of care. To tackle this challenge, medical institutions apply common scheduling heuristics to guide their decisions. These generic heuristics often do not align with the expectations of each specific medical institution. In this article, we propose a novel learning-based online optimization approach we term Learning-Based Assignment (LBA), which provides decision makers with a tailored, data-centered decision support algorithm that facilitates dynamic, institution-specific multi-variate decisions, without altering existing medical workflows. We adapt our generic approach to two medical settings: (1) the assignment of patients to caregivers in an emergency department; and (2) the assignment of medical scans to radiologists. In an extensive empirical evaluation, using real-world data and medical experts’ input from two distinctive medical domains, we show that our proposed approach provides a dynamic, robust and configurable data-driven solution which can significantly improve upon existing medical practices.

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A decision support framework for home health care transportation with simultaneous multi-vehicle routing and staff scheduling synchronization

Cited by 59 publications

References 62 publications

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Mutated cuckoo search algorithm for dynamic vehicle routing problem and synchronization occurs within the time slots in home healthcare

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

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