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Mathematics
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Mathematical Optimization in Supply Chain Management
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Mathematical Optimization in Supply Chain Management

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 987

Special Issue Editors

Dr. Michael Hamilton

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Guest Editor
Department of Industrial and Systems Engineering, College of Engineering, North Carolina Agriculture & Technical State University, Greensboro, NC 27411, USA
Interests: systems engineering; data science; modeling and simulation
Dr. Niamat Ullah Ibne Hossain

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Guest Editor
Engineering Management Department, Arkansas State University, Jonesboro, AR 72401, USA
Interests: machine learning; data science; hyperspectral imaging; artificial intelligence; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Raed M. Jaradat

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Bagley College of Engineering, Mississippi State University, Starkville, MS 39759, USA
Interests: systems thinking and complex systems; systems engineering; engineering management; risk management and system of systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic provided insight in the world supply chain and the weakness due to due to expected events.  Specifically, system disruptions were significantly impacted by the closures of businesses, quarantine measures, and the disparities in the global health systems due to access inequalities and the differences in countries healthcare based on their world class ranking. As a result of the global health crisis, it is critical to identify the weaknesses in the global system and provide solutions that optimize the coordination of critical supplies across the world. This special issue is interested in research that uses probabilistic modeling or artificial intelligence to optimize the global supply chain throughput or increased resilience of the current supply chain paradigm. The probabilistic modeling approach of interests but not limited to includes both deterministic and stochastic modeling paradigms as well as static and dynamic modeling approaches. Also, journal articles that use deep learning or shallow machine learning approaches to minimize supply chain disruption, maximize supply chain resilience, or optimize the interstitial link between the system of system network.

Dr. Michael Hamilton
Dr. Niamat Ullah Ibne Hossain
Dr. Raed M. Jaradat
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

  • COVID-19
  • pandemic ethnic disparities
  • supply chain disruption
  • supply chain resilience
  • healthcare inequalities
  • supply chain sustainability

Published Papers (1 paper)

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Research

58 pages, 5191 KiB  
Article
A Supply Chain Model with Carbon Emissions and Preservation Technology for Deteriorating Items under Trade Credit Policy and Learning in Fuzzy
by Osama Abdulaziz Alamri
Mathematics 2023, 11(13), 2946; https://doi.org/10.3390/math11132946 - 30 Jun 2023
Viewed by 657
Abstract
In this study, a supply chain model is proposed with preservation technology under learning fuzzy theory for deteriorating items where the demand rate depends on the selling price and also treats as a triangular fuzzy number. The deterioration rate of any item cannot [...] Read more.
In this study, a supply chain model is proposed with preservation technology under learning fuzzy theory for deteriorating items where the demand rate depends on the selling price and also treats as a triangular fuzzy number. The deterioration rate of any item cannot be eliminated due to its natural process, but it can be controlled with the help of preservation technology. Some harmful gases are emitted during the preservation process due to deteriorating items that harm the environment. In general, it can be easily seen that most of the sellers offer a trade credit policy to their regular buyers. In this paper, the retailer’s inventory stock reduces due to demand and deterioration. It is also assumed that some units are defective due to machine defects or delivery inefficiency. The retailer accepted the policy of trade credit offered by the seller. The aim of this paper is to enhance the profit of the supply chain partners. We proposed a theorem to get the optimal values of the selling price and cycle length. The retailer’s total profit is a function of selling price and cycle length, and the retailer’s total profit is optimized with respect to selling price and cycle length under trade-credit. Numerical examples are also presented for the validation of the present study, and sensitivity analysis is also discussed to know the robustness of the supply chain model. Managerial insight and observation have been given in the sensitivity section. Limitations and future work of this paper have been presented in the conclusion section. Full article
(This article belongs to the Special Issue Mathematical Optimization in Supply Chain Management)
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