Efficient computational strategies for dynamic inventory liquidation

We develop efficient computational strategies for the inventory liquidation problem, which is characterized by a retailer disposing of a fixed amount of inventory over a period of time. Liquidating end-of-cycle products optimally represents a challenging problem owing to its inherent stochasticity. The growing scale of liquidation problems further increases the need for solutions that are revenue- and time-efficient. We propose to address the inventory liquidation problem by deriving deterministic representations of stochastic demand, which provides significant theoretical and practical benefits as well as an intuitive understanding of the problem and the proposed solution. First, this paper develops a dynamic programming approach and a greedy heuristic approach to find the optimal liquidation strategy under deterministic demand representation. Importantly, we show that our heuristic approach is optimal under realistic conditions and is computationally less complex than dynamic programming. Second, we explore the relationships between liquidation revenue and several key elements of the liquidation problem via both computational experiments and theoretical analyses. We derive multiple managerial implications and demonstrate how the proposed heuristic approach can serve as an efficient decision support tool for inventory managers. Third, under stochastic demand, we conduct a comprehensive set of simulation experiments to benchmark the performance of our proposed heuristic approach with alternatives, including other simple approaches (e.g., the fixed-price strategy) as well as advanced stochastic approaches (e.g., stochastic dynamic programming). In particular, we consider a strategy that uses the proposed greedy heuristic to determine prices iteratively throughout the liquidation period. Computational experiments demonstrate that such iterative strategy stably produces higher total revenue than other alternatives and produces near-optimal total revenue in expectation while maintaining significant computational efficiency, compared with advanced techniques that solve the liquidation problem directly under stochastic demand. Our work advances the computational design for inventory liquidation and provides practical insights.