Special Issue "Post-pandemic Operational Research Applications: Models and Algorithms"

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1921

Special Issue Editor

Facultad de Ciencias Fisico-Matematicas, Universidad Autonoma de Nuevo Leon, San Nicolás de Los Garza CP 66450, Nuevo León, Mexico
Interests: optimization algorithms; logistics; applied mathematics; mathematical programming; supply chain management; operations research; evolutionary algorithms

Special Issue Information

Dear Colleagues,

Operational research is a very important research area of applied mathematics, in which the optimization of modeled real-life problems is the aim. This area provides the best possible solution for decision-makers. Recently, operational research (OR) has innovated to study decision-making problems during the pandemic caused by COVID-19. However, critical situations that can be studied from an OR perspective are maintained after the peaks of the pandemic. For instance, the disruption of supply chains, closure of facilities, shortages in raw materials, nearshoring, and other implications are derived from the pandemic.

Therefore, there are diverse situations that deserve an OR approach to support the decision-making process. In particular, new theories, mathematical models, exact, heuristic, and metaheuristic solution methods, and case studies are of interest for this Special Issue.

The aim of this Special Issue is to bring together innovative and original research that discusses interesting developments in post-pandemic OR applications. We welcome submissions that fit into (but are not limited to) the following topics:

  • Healthcare problems
  • Public and private transportation systems
  • Humanitarian logistics
  • Telecommunication networks
  • Hierarchical decision systems
  • Facility location
  • Supply chains (production, transportation, routing, among others)
  • Scheduling
  • Inventory policies
  • Data mining
  • Multi-objective optimization systems

We invite authors to submit original research articles. This Special Issue provides a platform for researchers and practitioners to communicate their novel and unpublished research on related problems.

Prof. Dr. José Fernando Camacho-Vallejo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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A Bi-Level Vaccination Points Location Problem That Aims at Social Distancing and Equity for the Inhabitants
Axioms 2023, 12(3), 305; https://doi.org/10.3390/axioms12030305 - 17 Mar 2023
Viewed by 939
Designing efficient vaccination programs that consider the needs of the population is very relevant to prevent reoccurrence of the COVID-19 pandemic. The government needs to provide vaccination points to give out vaccine doses to the population. In this paper, the authors analyze the [...] Read more.
Designing efficient vaccination programs that consider the needs of the population is very relevant to prevent reoccurrence of the COVID-19 pandemic. The government needs to provide vaccination points to give out vaccine doses to the population. In this paper, the authors analyze the location of vaccination points whilst addressing the inhabitants’ preferences. Two objectives that prevent crowding of inhabitants are considered. The government aims for the minimum distance between located vaccination points is maximized, and for the number of inhabitants that attend the different vaccination points to be equitable. One of the key aspects of this problem is the assumption that inhabitants freely choose the located vaccination point to go. That decision affects the objectives of the government, since crowding at vaccination points may appear due to the inhabitants’ decisions. This problem is modeled as a bi-objective, bi-level program, in which the upper level is associated to the government and the lower level to the inhabitants. To approximate the Pareto front of this problem, a cross-entropy metaheuristic is proposed. The algorithm incorporates criteria to handle two objective functions in a simultaneous manner, and optimally solve the lower-level problem for each government decision. The proposed algorithm is tested over an adapted set of benchmark instances and pertinent analysis of the results is included. An important managerial insight is that locating far vaccination points does not lead us to a more equitable allocation of inhabitants. Full article
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