Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows.

Saeed Tasouji Hassanpour, Ginger Y Ke, Jiahong Zhao, David M Tulett

Open access · greenAbstract read

In one paragraph

Article in Computers & industrial engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

12.2field-weighted citation impact, top 1% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed, 42 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Saeed Tasouji HassanpourFaculty of Business Administration, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada A1B 3X5.

Ginger Y KeFaculty of Business Administration, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada A1B 3X5.

Jiahong ZhaoSchool of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China.

David M TulettFaculty of Business Administration, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada A1B 3X5.

Memorial University of Newfoundland · CAGuangdong University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has presented tremendous challenges to the world, one of which is the management of infectious waste generated by healthcare activities. Finding cost-efficient services with minimum threats to public health has become a top priority. The pandemic has induced extreme uncertainties, not only in the amount of generated waste, but also in the associated service times. With this in mind, the present study develops a mixed-integer linear programming (MILP) model for the location-routing problem with time windows (LRPTW). To handle the uncertainty in the amount of generated waste, three scenarios are defined respectively reflecting different severity levels of a pandemic. Furthermore, chance constraints are applied to deal with the variation of the service times at small generation nodes, and time windows at the transfer facilities. The complexity of the resulting mathematical model motivated the application of a branch-and-price (B&P) algorithm along with an

Indexed as

Branch-and-price algorithmChance constrained programmingInfectious wasteLocation-routing problemStochastic programmingTime windows

Identifiers

PMID36741205

DOI10.1016/j.cie.2023.109066

PMCPMC9890827

OpenAlexW4318773910

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.