A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times

Shi, Yong; Boudouh, Toufik; Grunder, Olivier

doi:10.1016/j.tre.2019.05.015

Cited by 123 publications

(78 citation statements)

References 52 publications

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“…Fathollahi-Fard et al [12] developed three fast heuristics and a lower bound as well as a hybrid of SA and variable neighborhood search (VNS) to address a new variant of routing and scheduling problem for the home healthcare application. Lastly, Shi et al [31] proposed a new robust optimization for the mentioned problem considering uncertain travel and service times. They applied two simplified metaheuristics based on SA and VNS to solve the proposed problem.…”

Section: Background and Literaturementioning

confidence: 99%

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A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment

Fathollahi-Fard

Ahmadi

Goodarzian

et al. 2020

Applied Soft Computing

131

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Home care services are an alternative answer to hospitalization, and play an important role in reducing the healthcare costs for governments and healthcare practitioners. To find a valid plan for these services, an optimization problem called the home healthcare routing and scheduling problem is motivated to perform the logistics of the home care services. Although most studies mainly focus on minimizing the total cost of logistics activities, no study, as far as we know, has treated the patients' satisfaction as an objective function under uncertainty. To make this problem more practical, this study proposes a bi-objective optimization methodology to model a multi-period and multi-depot home healthcare routing and scheduling problem in a fuzzy environment. With regards to a group of uncertain parameters such as the time of travel and services as well as patients' satisfaction, a fuzzy approach named as the Jimenez's method, is also utilized. To address the proposed home healthcare problem, new and well-established metaheuristics are obtained. Although the social engineering optimizer (SEO) has been applied to several optimization problems, it has not yet been applied in the healthcare routing and scheduling area. Another innovation is to develop a new modified multi-objective version of SEO by using an adaptive memory strategy, so-called AMSEO. Finally, a comprehensive discussion is provided by comparing the algorithms based on multi-objective metrics and sensitivity analyses. The practicality and efficiency of the AMSEO in this context lends weight to the development and application of the approach more broadly.

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Section: Background and Literaturementioning

confidence: 99%

“…Description of the proposed problem is inspired by the recent published papers in this area [3,9,12,26,31,32]. This development is a new multi-period, multi-depot and multi-objective routing and scheduling problem for the application of the home healthcare.…”

Section: Problem Descriptionmentioning

confidence: 99%

A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment

Fathollahi-Fard

Ahmadi

Goodarzian

et al. 2020

Applied Soft Computing

131

View full text Add to dashboard Cite

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“…The model improves the environmental and social performance of the urban network during all evaluation periods. Shi et al [ 26 ] formulate a model for a Home Health Care Routing and Scheduling Problem from the perspective of Robust Optimisation (RO) to take into account uncertain travel and service times. The non-deterministic variables are defined based on the theory of budget uncertainty and arrival time of each caregiver is rewritten as a complicated recursive function.…”

Section: Related Workmentioning

confidence: 99%

Multi-resource scheduling and routing for emergency recovery operations

Bodaghi

Shahparvari

Fadaki

et al. 2020

International Journal of Disaster Risk Reduction

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Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.

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“…Robust model can provide an effective uncertainty measurement method, so the research of robust optimization has high applicability. Applying the theory of robust optimization [48], the TS-MILO model is transformed into TS-MIRO model. The uncertain parameters change in uncertain sets, so it can be studied without depending on the probability distribution.…”

Section: A Uncertainty Scenariosmentioning

confidence: 99%

Three-Stage Mixed Integer Robust Optimization Model Applied to Humanitarian Emergency Logistics by Considering Secondary Disasters

et al. 2020

IEEE Access

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A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times

Cited by 123 publications

References 52 publications

A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment

A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment

Multi-resource scheduling and routing for emergency recovery operations

Three-Stage Mixed Integer Robust Optimization Model Applied to Humanitarian Emergency Logistics by Considering Secondary Disasters

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