Algorithmic Approaches to Inventory Management Optimization

Abstract

An inventory management problem is addressed for a make-to-order supply chain that has inventory holding and/or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to an uncertain stationary consumer demand at each time period. Two sales scenarios are considered for any unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies are tested in a stochastic simulation environment that was built upon the open-source OR-Gym Python package. The results indicate that, of the three approaches, stochastic modeling yields the largest increase in profit, whereas reinforcement learning creates more balanced inventory policies that would potentially respond well to network disruptions. Furthermore, deterministic models perform well in determining dynamic reorder policies that are comparable to reinforcement learning in terms of their profitability.

Sensitivity Analysis of the Optimal Inventory-Pooling Strategies According to Multivariate Demand Dependence

“…This does not mean that these two copulas are in complete agreement on the same centralization policy in 286 of 10,000 possible scenarios. (1) 0.000 (.) Values in parentheses indicate the rank of each variable according to its influence on the optimal solution and the taken decision based on the statistic of Kruskal-Wallis test.…”

“…Inventory management is a crucial component in controlling costs and ensuring delivery to customers with minimal delays and interruptions [1]. Uncertainty represents one of the most critical issues facing firms around the world [2].…”

“…Moreover, the fourth column presents the median values for these testing variables with respect to the 8.07% of the remaining simulated scenarios where the allocation rules with minimal costs diverged. (1) 0.000 of 0.5471 for scenarios where the independent system is preferable. For scenarios where the transshipment policy is chosen the median of Kendall's rate drops to 0.1138.…”

Rough approximation‐based inventory optimization with random prices for B2C companies during the promotion period

“…Alawneh and Zhang 37 proposed a new inventory strategy for dual-channel warehouse, and established a multi-item inventory model considering the capacity constraints, demand and lead time uncertainties. Perez et al 38 proposed an inventory management problem for a make-to-order supply chain that has inventory holding and manufacturing locations at each node.…”

“…It discussed a chance-constrained model with stochastic coefficients which is applied to a single-period multiitem multiobjective inventory problem. Unlike Perez et al, 38 it focuses not on finding optimal parameters for static inventory policies, but rather on determining and comparing different dynamic policy approaches to the inventory management problem.…”