Petridis Konstantinos, Dey Prasanta Kumar, Chattopadhyay Amit K, Boufounou Paraskevi, Toudas Kanellos, Malesios Chrisovalantis
Department of Business Administration, University of Macedonia, 54006 Thessaloniki, Greece.
Aston Business School, Aston University, Birmingham B4 7ET, UK.
Entropy (Basel). 2023 Aug 22;25(9):1245. doi: 10.3390/e25091245.
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.
通过优化制造和分销供应链的绩效来最小化公司的运营风险是一项复杂的任务，涉及多个要素，每个要素都有其自身的供应链限制。传统的优化方法通常假定确定性为基本原则。然而，本文采用熵方法，通过将随机波动纳入供应链结构，强调了主观和客观不确定性在实现优化决策中的重要性。随机性代表着随机性，量化了所涉及的不确定性或风险水平。在本研究中，我们将一家加工生产厂作为一系列运营和供应链行动的模型。我们考虑从场地到工厂以及从工厂到客户群的随机变化的生产和运输成本。通过随机优化，我们证明工厂生产商可以通过提高销售价格同时降低优化生产成本来从改善的财务结果中受益。这可以通过有选择地选择生产成本概率密度函数遵循帕累托分布的生产商来实现。值得注意的是，较低的帕累托指数会产生更好的供应链成本优化预测。或者，在权衡优化和简单性时，可以提出高斯随机波动作为更合适的选择。虽然这可能会导致性能略逊一筹，但在易于实施和计算效率方面具有优势。
