Time evolution of a Supply Chain Network: Kinetic modeling

Debnath, Biswajit; El-Hassani, Rihab; Chattopadhyay, Amit K.; Kumar, T. Krishna; Ghosh, Sadhan Kumar; Baidya, Rahul

doi:10.1016/j.physa.2022.128085

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…In “pull” systems, demand drives production while, in the second case, production is fixed based on demand estimation. Such deductions are likely to be modified once the time evolution of a supply chain is considered [ 79 ]. However, in most cases the optimal design of the supply chain network is constructed around parameter values that approximate the upper and lower bounds of the transported quantities.…”

Section: Discussionmentioning

confidence: 99%

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

Petridis,

Dey,

Chattopadhyay

et al. 2023

Entropy

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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.

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Section: Discussionmentioning

confidence: 99%

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

Petridis,

Dey,

Chattopadhyay

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

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Cleaner Production in Multivariate Supply Chain Networks: Sustainable Business Future Through a “Roll of Dice”

Debnath,

Chattopadhyay,

Kumar

2024

Circular Economy and Sustainability

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Mathematical programming model for cost-optimized and environmentally sustainable supply chain design

Egami

2024

AIP Advances

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Sustainability is becoming increasingly important as a topic in general supply chains and cold chains. This study addresses the critical need for an integrated and comprehensive approach to supply chain design that simultaneously maximizes cost efficiency and incorporates environmental sustainability, focusing specifically on cold chains. Prior research has identified a gap in existing studies, highlighting the absence of an overarching framework from the logistics providers’ perspective. Our novel mathematical programming model introduces a unique strategy that carefully balances environmental concerns and cost optimization, addressing the shortcomings found in previous works. By integrating variables related to distribution, transportation, and inventory management, the model aims to minimize overall expenses and environmental impact, specifically regarding energy consumption and carbon emissions. We formulate and solve the optimization problem through rigorous mathematical programming, offering a flexible solution applicable to various logistics scenarios. The results demonstrate a significant reduction in overall expenses and environmental effects, providing logistics providers with a powerful tool for informed decision-making and sustainable supply chain management. This research contributes to the evolving field of supply chain sustainability by presenting a practical and effective model for maximizing cost savings while promoting environmental responsibility. The study concludes with compelling statistics, revealing a noteworthy decrease in overall expenses by €25 128 146.73 (100.25%) and environmental impact by 17 359.39 (100.04%). The outcomes show a notable decrease in general expenses and environmental effects, giving logistics providers a strong instrument for wise choices and sustainable supply chain management.

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Time evolution of a Supply Chain Network: Kinetic modeling

Cited by 4 publications

References 39 publications

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

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

Cleaner Production in Multivariate Supply Chain Networks: Sustainable Business Future Through a “Roll of Dice”

Mathematical programming model for cost-optimized and environmentally sustainable supply chain design

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