(34f) Modeling and Optimization of Supply Chains of Perishable Products with Multiple Time-Varying Quality Attributes

Supply chain management and optimization are paramount in keeping an organization’s operations efficient, as well as in achieving maximum consumer service levels. A critical example of such supply chain systems is food production and distribution, for which optimal planning and operation can have significant societal, environmental and economic impact. Data indicates that roughly one fourth of the food produced for human consumption is wasted due to poor handling throughout the supply chain in the United States [1]. Additionally, it is estimated that the logistics associated with refrigerated storage and transportation of perishable food products amount to approximately 1% of the total carbon dioxide emissions worldwide [2].

The conventional metric used to assess a perishable product’s quality is the shelf life. This reflects, often inaccurately, the number of days remaining until the product is unfit for sale or consumption. Increasingly stricter safety regulations [3] require developing and tracking more rigorous product quality metrics, that are associated with quantifiable physicochemical attributes of the product (e.g., color, firmness, bacterial content, chemical composition). Accounting for detailed product “quality dynamics” based on such measurable variables (which indicate the rate at which a product degrades during its transition through the supply chain), enables a more rational approach to supply chain management. In addition to managing shipments and inventory, this includes optimizing environmental conditions (e.g., temperature, humidity and atmosphere composition) during production, storage and shipment. Integrating such considerations in supply chain optimization frameworks is essential to ensuring that products meet quality and safety standards and in reducing inventory waste.

Capturing the dynamic evolution of the quality variables of highly perishable inventory and embedding the resulting models in operational planning calculations, can be computationally demanding [4,5]. This problem is exacerbated when multiple quality metrics must be simultaneously tracked and/or controlled, and results in extremely large-scale planning problems as the topology of the supply network expands.

Motivated by the above, in this work, we introduce a computationally efficient modeling and optimization approach for supply chain operational planning dealing with products with multiple quality attributes. Specifically, we propose decomposing the supply chain model based on individual quality features, and formulating linking constraints that establish inventory conservation. We analyze several commonly encountered circumstances, including the presence of correlation between the quality variables, and demonstrate the particulars of implementing the proposed approach in such cases. We provide several computational examples in support of these developments.

[1] Zhengxia Dou, James D. Ferguson, David T. Galligan, Alan M. Kelly, Steven M. Finn, and Robert Giegengack. Assessing us food wastage and opportunities for reduction. Global Food Security, 8:19–26, 2016. [2] S. J. James and C. James. The food cold-chain and climate change. Food Research International, 43(7):1944–1956, 2010. [3] Mahelet G. Fikru. Developing environmental performance indicators for food and beverage processors in the USA. Ecological Indicators.79:106-113, 2017. Aiying Rong, Renzo Akkerman, and Martin Grunow. An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics. 131(1): 421-429, 2011. [5] Fernando Lejarza and Michael Baldea. Closed-loop optimal operational planning of supply chains with fast product quality dynamics. Computers & Chemical Engineering, 132:106594,2020.