# Algorithmic Approaches to Inventory Management Optimization

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## Abstract

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## 1. Introduction

- A deterministic linear programming model (DLP) that uses either the rolling horizon or shrinking horizon technique in order to determine optimal re-order quantities for each time period at each node in the supply network. Customer demand is modeled at its expectation value throughout the rolling/shrinking horizon time window.
- A multi-stage stochastic program (MSSP) with a simplified scenario tree, as described in Section 2.7. Shrinking and rolling horizon for the MSSP model are both implemented to decide the reorder quantity at each time period.
- A reinforcement learning model (RL) that makes re-order decisions based on the current state of the entire network.

## 2. Materials and Methods

#### 2.1. Problem Statement

#### 2.2. Sequence of Events

- Main network nodes (retailer, distributors, and producers) place replenishment orders to their respective suppliers. Replenishment orders are filled according to available production capacity and available feedstock inventory at the respective suppliers. The supply network is assumed to be centralized, such that replenishment orders never exceed what can be provided by the suppliers to each node.
- The main network nodes receive incoming feedstock inventory replenishment shipments that have made it down the product pipeline (after the associated lead times have transpired). The lead times between stages include both production times and transportation times.
- Single-product customer demand occurs at the retail node and it is filled according to the available inventory at that stage.
- One of the following occurs at the retailer node,
- (a)
- Unfulfilled sales are backlogged at a penalty. Backlogged sales take priority in the following period.
- (b)
- Unfulfilled sales are lost and a goodwill loss penalty is levied.

- Surplus inventory is held at each node at a holding cost. Inventory holding capacity limits are not included in the present formulation, but they can be easily added to the model, if needed. The IMP that is presented here is capacitated in the sense that manufacturing at production nodes is limited by both the production capacity and the availability of feedstock inventory at each node. Because the supply network operates as a make-to-order system, only feedstock inventories are held at the nodes. All of the product inventory is immediately shipped to the downstream nodes upon request, becoming feedstock inventory to those nodes (or simply inventory if the downstream node is a distributor/retailer). A holding (e.g., transportation) cost is also placed on any pipeline inventory (in-transit inventory).
- Any inventory remaining at the end of the last period (period 30 in the base case) is lost, which means that it has no salvage value.

#### 2.3. Key Variables

#### 2.4. Objective Function

#### 2.5. IMP Model

#### 2.5.1. Network Profit

#### 2.5.2. Inventory Balances

#### 2.5.3. Inventory Requests

#### 2.5.4. Market Sales

#### 2.5.5. Variable Domains

#### 2.6. Scenario Tree for Multistage Stochastic Programming

#### 2.7. Approximation for the Multistage Scenario Tree

#### 2.8. Perfect Information and Deterministic Model

#### 2.9. Reinforcement Learning Model

#### 2.10. Case Study

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

LP | Linear Programming |

DLP | Deterministic Linear Programming |

MILP | Mixed-integer Linear Programming |

2SSP | Two-stage Stochastic Programming |

MSSP | Multi-stage Stochastic Programming |

RL | Reinforcement Learning |

AI | Artificial Intelligence |

PPO | Proximal Policy Optimization |

MDP | Markov Decision Process |

IMP | Inventory Management Problem |

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**Figure 6.**Average total inventory at each main network node (lost sales mode). Shaded areas denote $\pm 1$ standard deviation of the mean value.

**Figure 7.**Average network flow with the RL policy (lost sales mode). Total flow is proportional to the edge thickness.

**Figure 8.**Average network flow with the MSSP-RH policy (lost sales mode). Total flow is proportional to the edge thickness.

Variable | Description |
---|---|

${a}_{t,j,k}$ | The reorder quantity requested to supplier node j by node k at the beginning of period t (the amount of material sent from node j to node k) |

${S}_{t,j,k}^{d}$ | The amount retailer j sells to market k in period $t-1$. Note: Retail sales are indexed at the next period since these occur after demand in the current period is realized. |

${S}_{t,j}^{o}$ | The on-hand inventory at node j just prior to when the demand is realized in period t. |

${S}_{t,j,k}^{p}$ | The in-transit (pipeline) inventory between node j and node k just prior to when the demand is realized in period t. |

${u}_{t,j,k}$ | The unfulfilled demand at retailer j associated with market k in period $t-1$. Note: indexing is also shifted since any unfulfilled demand occurs after the uncertain demand is realized. |

${R}_{t,j}$ | The profit (reward) in node j for period t. |

**Table 2.**Total reward comparison for the various models used to solve the IMP. Performance Ratio is defined as the ratio of the final cumulative profit of the perfect information model to that of the model used. DLP = Deterministic linear program; MSSP = Multi-stage stochastic program; RL = Reinforcement Learning; RH = rolling horizon; SH = shrinking horizon; Oracle = perfect information LP.

DLP-RH | DLP-SH | MSSP-RH | MSSP-SH | RL | Oracle | |
---|---|---|---|---|---|---|

Backlog | ||||||

Mean Profit | 791.6 | 825.3 | 802.7 | 847.7 | 737.2 | 861.3 |

Standard Deviation | 52.5 | 37.0 | 56.3 | 49.4 | 24.8 | 56.4 |

Performance Ratio | 1.09 | 1.04 | 1.07 | 1.02 | 1.17 | 1.00 |

Lost Sales | ||||||

Mean Profit | 735.8 | 786.9 | 790.6 | 830.6 | 757.8 | 854.9 |

Standard Deviation | 31.2 | 30.8 | 47.8 | 37.7 | 33.1 | 49.9 |

Performance Ratio | 1.16 | 1.09 | 1.08 | 1.03 | 1.13 | 1.00 |

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**MDPI and ACS Style**

Perez, H.D.; Hubbs, C.D.; Li, C.; Grossmann, I.E.
Algorithmic Approaches to Inventory Management Optimization. *Processes* **2021**, *9*, 102.
https://doi.org/10.3390/pr9010102

**AMA Style**

Perez HD, Hubbs CD, Li C, Grossmann IE.
Algorithmic Approaches to Inventory Management Optimization. *Processes*. 2021; 9(1):102.
https://doi.org/10.3390/pr9010102

**Chicago/Turabian Style**

Perez, Hector D., Christian D. Hubbs, Can Li, and Ignacio E. Grossmann.
2021. "Algorithmic Approaches to Inventory Management Optimization" *Processes* 9, no. 1: 102.
https://doi.org/10.3390/pr9010102