(442g) Closed-Loop Optimal Operational Planning of Supply Chains with Product Quality Dynamics

Supply chain management (SCM) and optimization are of paramount importance in today's competitive markets. The integrated management of the different levels of the supply chain, such as production and distribution, has enabled businesses to meet ever-changing consumer demand at minimum costs [1]. One of the critical aspects of SCM is ensuring product quality. Of particular interest is the case of the supply chain of perishable products (e.g., fresh food and produce, biologics such as vaccines and organs), whose quality evolves over a time scale that is comparable to their transition time through the supply chain. We will refer to these systems as supply chains with product quality dynamics.

Significant research efforts have been devoted to measuring and modelling product quality. These include, e.g., understanding the chemical processes responsible for quality degradation as a function of environmental conditions (e.g., temperature during transportation and storage) [2]. Furthermore, new technologies have allowed for real-time and non-destructive quality measurements [3], which in turn have enabled online quality and temperature control of products throughout the supply chain [4]. Thus far, however, SCM techniques have not fully incorporated these advances in a coherent strategy for the optimal management of supply chains with product quality dynamics, particularly for simultaneously optimizing supply chain operations and product quality.

Motivated by the above, in the present work we present a production and distribution planning framework for the supply chain that explicitly accounts for product dynamics. We propose a receding-horizon closed-loop solution approach, such that product quality information, as well as other disturbances (i.e., product demand), are updated periodically via a feedback mechanism during the operation of the supply chain. Furthermore, similarly to (economic) model predictive control, we propose penalizing deviations from a reference trajectory in order to improve long-term planning. To demonstrate the validity of the formulation, we present an illustrative case study where product degradation rate and demand fluctuate simultaneously in time.

References:

[1] Papageorgiou L. G.: Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33, 1931 – 1938, (2009).

[2] C. Man, A. Jones, Shelf Life Evaluation of Foods, Springer, (2000).

[3] J. A. Abbott, Quality measurement of fruits and vegetables, Postharvest Biology and Technology, 15, 207 – 225, (1999).

[4] M. M. Aung, Y. S. Chang, Temperature management for the quality assurance of a perishable food supply chain, Food Control, 40, 198 – 207, (2014).