A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment


Minimizing a company’s operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of uncertainty or risk involved. In this study, we focus on a processing production plant as a model for a chain of operations and supply chain actions. We consider the stochastically varying production and transportation costs from the site to the plant, as well as from the plant to the customer base. Through stochastic optimization, we demonstrate that the plant producer can benefit from improved financial outcomes by setting higher sale prices while simultaneously lowering optimized production costs. This can be accomplished by selectively choosing producers whose production cost probability density function follows a Pareto distribution. Notably, a lower Pareto exponent yields better supply chain cost optimization predictions. Alternatively, a Gaussian stochastic fluctuation may be proposed as a more suitable choice when trading off optimization and simplicity. Although this may result in slightly less optimal performance, it offers advantages in terms of ease of implementation and computational efficiency.

Publication DOI: https://doi.org/10.3390/e25091245
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied Mathematics & Data Science
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
College of Engineering & Physical Sciences > Engineering for Health
College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application
Additional Information: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: green supply chain management,noise,stochastic models,supply chain risk model,Information Systems,Physics and Astronomy(all),Electrical and Electronic Engineering,Mathematical Physics,Physics and Astronomy (miscellaneous)
Publication ISSN: 1099-4300
Last Modified: 24 May 2024 07:18
Date Deposited: 12 Sep 2023 09:05
Full Text Link:
Related URLs: https://www.mdp ... -4300/25/9/1245 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-08-22
Published Online Date: 2023-08-22
Accepted Date: 2023-08-18
Authors: Petridis, Konstantinos
Dey, Prasanta (ORCID Profile 0000-0002-9984-5374)
Chattopadhyay, Amit (ORCID Profile 0000-0001-5499-6008)
Boufounou, Paraskevi
Toudas, Kanellos
Malesios, Chrisovalantis



Version: Published Version

License: Creative Commons Attribution

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