What is it about?
Effective inventory system management allows companies to respond quickly to the market change and develop a flexible production system to improve their competitiveness. In this paper, we study an inventory problem of a perishable product with random deterioration rate under uncertainty in both demand and supply. Such problem appears in biomass supply chains because feedstock supply is always uncertain due to weather conditions, insect population. By formulating the problem as a multiperiod stochastic inventory model, we demonstrate that its optimal inventory policy is an order‐up‐to level policy. We develop an effective algorithm combining scenario‐based optimization and Lagrangian relaxation for quickly and approximately calculating the order‐up‐to levels. A computational study shows that the algorithm can find a near‐optimal solution with a relative gap of cost less than 1% on average in comparison with the optimal inventory policy.
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Why is it important?
(i) We introduce a periodic review perishable inventory model with uncertainty in both demand and supply where a part of inventory is degraded/deteriorated at a random rate from one period to the next. (ii) We develop a novel solution method that combines scenario-based optimization and Lagrangian relaxation to find a near-optimal solution (order-up-to levels) for the original problem. The proposed method is efficient for problems with a large number of scenarios. (iii) We conduct a numerical study that shows that the proposed solution approach can find near-optimal inventory policies with the deviation of the expected total cost less than 1% from the optimal expected total cost on average.
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This page is a summary of: An effective approach for optimization of a perishable inventory system with uncertainty in both demand and supply, International Transactions in Operational Research, July 2020, Wiley, DOI: 10.1111/itor.12846.
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