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Mathematical Modelling and Optimization for Complex Production under Supply Chain...
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Mathematical Modelling and Optimization for Complex Production under Supply Chain Management

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3040

Special Issue Editors

Dr. Mitali Sarkar

E-Mail Website
Guest Editor
Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77, Cheongam-ro, Nam-gu, Pohang 37673, Republic of Korea
Interests: smart production; reliability; production planning and control; inventory management; supply chain management; bio-fuel
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Biswajit Sarkar

E-Mail Website
Guest Editor
Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, Republic of Korea
Interests: supply chain management; optimization; artificial intelligence; smart production; mathematical modelling; inventory management; smart logistics; biomathematics; circular economy; sustainability; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

An important challenge for most industrial management systems is to simultaneously consider the optimization of many decision variables under the coordination or non-coordination of supply chain management (SCM).

Any classical optimization technique can obtain the solution if the number of decision variables is less than ten. However, for the complex or smart products, the number of decision variables is much more higher and are extremely nonlinear. Thus, finding the solution to these SCM becomes a tedious job for the researcher. As a result, any nonlinear optimization, meta-heuristic, fuzzy optimization, or data science (artificial intelligence approaches) is utilized to obtain the solution, which, however, may not achieve the optimum solution. This is the major gap in the present research on supply chain management. This Special Issue aims to discuss major nonlinear approaches or other optimizations to solve supply chain problems with many decision variables, selecting industry and SCM problems for further development.

This Special Issue aims to simplify several applications of SCM, utilizing the best optimization technique to obtain the optimum solution rather than simply the best solution among others. The applicability of these two aspects in any large-scale SCM model is acceptable. Thus, new approaches, methodologies, modelling, and technology utilization are sufficient for solving complex supply chain management.

Potential topics to be covered:

  • Nonlinear approaches: constrained and unconstrained nonlinear problems;
  • Meta-heuristic methods;
  • Convex optimization;
  • Robust optimization;
  • Optimization by artificial intelligence;
  • Fuzzy optimization.

Dr. Mitali Sarkar
Prof. Dr. Biswajit Sarkar
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.

Published Papers (2 papers)

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Research

29 pages, 2295 KiB  
Article
Optimal Decisions on Greenness, Carbon Emission Reductions, and Flexibility for Imperfect Production with Partial Outsourcing
by Bikash Koli Dey, Hyesung Seok and Kwanghun Chung
Mathematics 2024, 12(5), 654; https://doi.org/10.3390/math12050654 - 23 Feb 2024
Viewed by 603
Abstract
Global emphasis on sustainable development is widespread, with industries playing a pivotal role in advancing global sustainability within the business and retail sectors. Consumer awareness of environmental concerns, such as pollution, prompts a focus on product biodegradability and eco-friendliness. Consequently, customers are drawn [...] Read more.
Global emphasis on sustainable development is widespread, with industries playing a pivotal role in advancing global sustainability within the business and retail sectors. Consumer awareness of environmental concerns, such as pollution, prompts a focus on product biodegradability and eco-friendliness. Consequently, customers are drawn to products with higher green credentials. This study delves into the effectiveness of green attributes in retail industries, exploring the optimization of profit through a variable production rate and variable unit production cost, considering the selling price and the demand dependent on the product’s green level. In the long run, production systems may shift to an “out-of-control” state, resulting in the random production of imperfect items that must be remanufactured to maintain the industry’s positive brand image. To mitigate the impact of defective items, the industry opts to partially outsource a percentage of items, preventing shortages. However, this complex retailing system generates a significant amount of carbon emissions. This study introduces investments aimed at reducing carbon emissions to address this issue. In contrast with the existing literature, a green-level-dependent unit raw material cost is considered here for variable unit production cost. Ultimately, this study seeks to maximize the overall system’s profit by optimizing the selling price, order quantity, production rate, green level, and carbon emission reduction investments. The classical optimization technique is utilized to obtain analytic optimum results for the decision variables and total profit. Special cases and sensitivity analyses illustrate the real-world applicability and impact of green levels. Numerical findings indicate that considering the product’s green-level-dependent demand and unit production rate is 22.44% more beneficial than nongreen products, partial outsourcing provides a 1.28% advantage, and flexibility in the production rate yields a 69.60% benefit over traditional systems without green elements. Additionally, technological investments to reduce carbon emissions result in a notable reduction of up to 4.53%. Full article
(This article belongs to the Special Issue Mathematical Modelling and Optimization for Complex Production under Supply Chain Management)
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25 pages, 2384 KiB  
Article
How Effective Is Reverse Cross-Docking and Carbon Policies in Controlling Carbon Emission from the Fashion Industry?
by Taniya Mukherjee, Isha Sangal, Biswajit Sarkar, Qais Almaamari and Tamer M. Alkadash
Mathematics 2023, 11(13), 2880; https://doi.org/10.3390/math11132880 - 27 Jun 2023
Cited by 2 | Viewed by 2031
Abstract
The present consumer behavior is manipulated by “fast fashion”, where purchasing new, trendy, affordable clothes is preferred over recycling old ones. This changing mannerism has escalated the GHG emissions from the fashion industry. Energy-intensive raw material production, preparation, and processing contribute to considerable [...] Read more.
The present consumer behavior is manipulated by “fast fashion”, where purchasing new, trendy, affordable clothes is preferred over recycling old ones. This changing mannerism has escalated the GHG emissions from the fashion industry. Energy-intensive raw material production, preparation, and processing contribute to considerable emissions. The management of the returned goods from the primary market and further processing through the secondary outlets indulge in reverse logistics. In this paper, efforts are made to minimize the total cost and the carbon emission amount during the process of managing the return articles from the primary market to the reverse distribution center, further processing of the articles at the secondary outlet, and the return of the unsold or excess articles from the secondary outlet. Reverse cross-docking has been implemented in managing the return articles, while environmental concerns over GHG emissions have been addressed by investing in green technology under a strict carbon cap policy. In this research, return articles from the primary and secondary markets, rework of the returned articles, and disposal of the impaired returned articles have been considered. The carbon emission cost at all stages of transportation, rework, or disposal has also been incorporated into this model. A constrained mixed integer linear programming model is proposed and solved considering green investment. A numerical example has been formulated to investigate the effect of green technology on the total cost. The results portray that, though the total cost increases by nearly 2% due to investment in green technology, it ensures a considerable drop of 23% in the carbon emission amount. Also, the result is successful in establishing that reverse cross-docking is a better option than traditional warehousing in terms of minimizing the cost. Full article
(This article belongs to the Special Issue Mathematical Modelling and Optimization for Complex Production under Supply Chain Management)
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