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Mathematics
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Optimization and Uncertainty
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Optimization and Uncertainty

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4607

Special Issue Editors

Prof. Dr. Balbina Virginia Casas Méndez

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Guest Editor
Department of Statistics, Mathematical Analysis and Optimization, Faculty of Mathematics, University of Santiago de Compostela, Campus Vida s/n, 15782 Santiago de Compostela, Spain
Interests: game theory in machine learning; mathematical programming and optimization; metaheuristics algorithms; models of cooperation in operational research; resource allocation and routing problems
Prof. Dr. Rosa María Crujeiras

E-Mail Website
Co-Guest Editor
Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Campus Vida s/n 15782, Santiago de Compostela, Spain
Interests: spatial statistics; nonparametric methods; directional data analysis; mathematics; computer science
Prof. Dr. Joaquín Sánchez-Soriano

E-Mail Website
Co-Guest Editor
U.I. Center of Operations Research (CIO), Universidad Miguel Hernández de Elche, 03202 Elche, Spain
Interests: games; game development; game theory; game theory and decision theory; telecommunications; engineering; statistics; optimization methods; optimization; decision analysis; mathematical programming

Special Issue Information

Dear Colleagues,

Optimization problems have remained under the mathematical spotlight since the seminal  works of Fermat in the seventeenth century. In fact, optimization techniques are demanded from the most varied areas of knowledge, such as economics, management, or computer sciences.

Today, the development of statistical learning, the need to adapt hospital management to emerging pandemics, and the fight against climate change require the development of a powerful and precise optimization methodology that in most cases faces phenomena dominated by uncertainty. This is the case when evaluating improvement proposals in the industrial sector, when dealing with massive data analysis or when considering reorganization to improve the quality of a health service. Thus, this Special Issue focuses on the presentation of current advances in theoretical and applied research in the field of optimization in a context of uncertainty, understood from the probabilistic or other perspectives. This Special Issue aims to provide a platform for researchers from academia and industry to present their new and unpublished work in the field of optimization under uncertainty. This will help to foster future research in the emerging field of statistical learning and in the new challenges posed by logistics, health, or environmental sciences, and to contribute to the development and application of fuzzy optimization in engineering or management problems, among others.

Prof. Dr. Balbina Virginia Casas Méndez
Prof. Dr. Rosa María Crujeiras
Prof. Dr. Joaquín Sánchez-Soriano
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • stochastic programming
  • stochastic models
  • optimization
  • fuzzy optimization
  • environmental sciences
  • industry
  • health

Published Papers (2 papers)

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Research

39 pages, 675 KiB  
Article
On Multistage Multiscale Stochastic Capacitated Multiple Allocation Hub Network Expansion Planning
by Laureano F. Escudero and Juan F. Monge
Mathematics 2021, 9(24), 3177; https://0-doi-org.brum.beds.ac.uk/10.3390/math9243177 - 09 Dec 2021
Cited by 3 | Viewed by 1609
Abstract
The hub location problem (HLP) basically consists of selecting nodes from a network to act as hubs to be used for flow traffic directioning, i.e., flow collection from some origin nodes, probably transfer it to other hubs, and distributing it to destination nodes. [...] Read more.
The hub location problem (HLP) basically consists of selecting nodes from a network to act as hubs to be used for flow traffic directioning, i.e., flow collection from some origin nodes, probably transfer it to other hubs, and distributing it to destination nodes. A potential expansion on the hub building and capacitated modules increasing along a time horizon is also considered. So, uncertainty is inherent to the problem. Two types of time scaling are dealt with; specifically, a long one (viz., semesters, years), where the strategic decisions are made, and another whose timing is much shorter for the operational decisions. Thus, two types of uncertain parameters are also considered; namely, strategic and operational ones. This work focuses on the development of a stochastic mixed integer linear optimization modeling framework and a matheuristic approach for solving the multistage multiscale allocation hub location network expansion planning problem under uncertainty. Given the intrinsic difficulty of the problem and the huge dimensions of the instances (due to the network size of realistic instances as well as the cardinality of the strategic scenario tree and operational ones), it is unrealistic to seek an optimal solution. A matheuristic algorithm, so-called SFR3, is introduced, which stands for scenario variables fixing and iteratively randomizing the relaxation reduction of the constraints and variables’ integrality. It obtains a (hopefully, good) feasible solution in reasonable time and a lower bound of the optimal solution value to assess the solution quality. The performance of the overall approach is computationally assessed by using stochastic-based perturbed well-known CAB data. Full article
(This article belongs to the Special Issue Optimization and Uncertainty)
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12 pages, 5413 KiB  
Article
Optimal Pricing and Ordering Strategies with a Flexible Return Strategy under Uncertainty
by Pan Guo, Yanlin Jia, Junwei Gan and Xiaofeng Li
Mathematics 2021, 9(17), 2097; https://0-doi-org.brum.beds.ac.uk/10.3390/math9172097 - 30 Aug 2021
Cited by 7 | Viewed by 1660
Abstract
To coordinate the supply chain risk caused by demand uncertainty, this paper proposed a flexible return strategy under demand uncertainty, in which the retailer can choose return quantity independently by put option after the selling season, while the return quantity is usually determined [...] Read more.
To coordinate the supply chain risk caused by demand uncertainty, this paper proposed a flexible return strategy under demand uncertainty, in which the retailer can choose return quantity independently by put option after the selling season, while the return quantity is usually determined by the supplier in the classical return strategy. In our novel return strategy, the exercise price is not fixed, and we developed the base model of this strategy, named the selective buyback contracts model. We have solved the optimal pricing and ordering strategies of supply chain members. Numerical studies demonstrated that the contracts can coordinate a supply chain with one retailer and one supplier, and the supplier can adjust the profit distribution of the supply chain by adjusting the option exercise price. Compared with other return strategies, the selective buyback contracts give the retailer more power of choice, and the supplier receives risk compensation from the put options. Full article
(This article belongs to the Special Issue Optimization and Uncertainty)
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