Abstract
Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges, mathematical programming models are developed to evaluate supply chain flexibility. However, under uncertainty, supply chain models become complex and the scope of flexibility analysis is generally reduced. This paper presents a unified approach that can evaluate the flexibility of a four-echelon supply chain via a robust stochastic programming model. The model simultaneously considers the plans of multiple business divisions such as marketing, logistics, manufacturing, and procurement, whose goals are often conflicting. A numerical example with deterministic parameters is presented to introduce the analysis, and then, the model stochastic parameters are considered to evaluate flexibility. The results of the analysis on supply, manufacturing, and distribution flexibility are presented. Tradeoff analysis of demand variability and service levels is also carried out. The proposed approach facilitates the adoption of different management styles, thus improving supply chain resilience. The model can be extended to contexts pertaining to supply chain disruptions; for example, the model can be used to explore operation strategies when subtle events disrupt supply, manufacturing, or distribution.
Highlights
Chains (SCs) are required to handle environmental uncertainties
Our proposed robust stochastic programming model helps decision makers to evaluate supply, manufacturing, and distribution strategies, decide as to which service level should be selected from the perspective of demand variability, and understand the impact of a decision on the overall expected profit
This study developed a robust stochastic programming model to evaluate the flexibility of a four-echelon SC at a tactical level
Summary
Chains (SCs) are required to handle environmental uncertainties. it is important to develop strategies to improve the flexibility and resilience of SCs without compromising their operation efficiency and effectiveness [1]. Suppliers transport raw materials in multiple lots to factories Manufacturing processes convert these materials into finished products. In many situations, the parameters of deterministic models are not known completely In such cases, sensitivity analysis combined with parametric optimization is commonly adopted. Sensitivity analysis combined with parametric optimization is commonly adopted This strategy, known as parametric linear programming, is hardly relevant to optimization under uncertainty. Flexibility analysis often considers only one or two echelons of SCs. The present study is aimed at investigating a four-echelon SC tactical planning model by robust stochastic programming. The proposed model combines robust optimization and stochastic programming features to evaluate SC flexibility and resilience, considering scenarios with stochastic parameters and adjustable levels of demand variability. The conclusions and directions for future research are presented
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