Table 3.
A literature overview of modeling approaches for dimensioning safety stocks based on the variation of demand.
| References | OR method/Modeling technique(s) | Main performance criteria |
|---|---|---|
| Agrell [42] | Decision analysis/Convex NLP | 1. Holding costs; 2. Shortage freq.; 3. Stock-outs |
| Alstrom [49] | Statistics/Regression; Heuristic | 1. Holding and ordering costs; 2. Shortage freq. |
| Badinelli [39] | Optimization/Quadratic programming | 1. Holding, ordering and stock-out costs |
| Bahroun and Belgacem [33] | Simulation/Monte Carlo | 1. Service level; 2. Holding costs |
| Benton [34] | Simulation | 1. Service level |
| Braglia et al. [40] | Optimization/PV; Exact & Approximated minimization algorithms | 1. Total costs (inc. ordering, setup, transportation and holding costs) |
| Brander and Forsberg [51] | Analytic/Inventory theory; Basic period approach | 1. Holding and setup costs |
| Charnes et al. [58] | Analytic/Inventory theory | 1. Stock-out probability |
| Cheng et al. [46] | Optimization/MOPSO | 1. Holding and ordering costs; 2. Shortage freq. |
| Dar-El and Malmborg [30] | Analytic/Inventory theory | 1. Service level; 2. Holding costs |
| Disney et al. [53] | Analytic/Inventory theory | 1. Holding and backlog costs; 2. Availability |
| Gallego [32] | Optimization/Control theory; Simulation-based search method | 1. Holding, backorder and setup costs |
| Hayya et al. [50] | Statistics/Regression | 1. Holding, ordering and shortage costs |
| Hsueh [57] | Analytic/Closed-form expressions | 1. Holding and manufacturing orders costs |
| Inderfurth [55] | Analytic/Inventory theory; Control theory | 1. Holding, shortage and production costs |
| Inderfurth and Vogelgesang [54] | Analytic/Inventory theory; Control theory | 1. Holding and backlog costs |
| Jodlbauer and Reitner [38] | Optimization/2D-Newton method | 1. Service level; 2. Holding, setup and backorder costs |
| Jonsson and Mattsson [35] | Simulation/DES | 1. Service level; 2. Ordering costs |
| Kelle [28] | Analytic/Inventory theory | 1. Service level |
| Kelle and Silver [29] | Analytic/Inventory theory | 1. Service level |
| Kumar and Evers [59] | Analytic/Inventory theory | 1. (Estimated/simulated) variance |
| Lu et al. [56] | Analytic/Inventory theory; Fixed-point iteration method | 1. Service level; 2. Inventory levels |
| Man-Yi and Xiao-Wo [52] | Statistics/Credibility and Fuzzy theory | 1. Fill rate |
| Mertins and Lewandrowski [62] | Analytic/Inventory theory | 1. Inventory costs |
| Ozbay and Ozguven [36], [37] | Optimization/PVB; pLEPs | 1. Total costs (inc. storage, surplus, shortage and adjustment costs) |
| Srivastav and Agrawal [47] | Optimization/MOGA and MOPSO | 1. Ordering, holding and backorder costs; 2. Shortage freq.; 3. Stock-outs; |
| Srivastav and Agrawal [48], Tsou [44] | Optimization/MOPSO | 1. Ordering/holding costs; 2. Shortage freq.; 3. Stock-outs |
| Tsou [45] | Optimization/MOEMO and MOPSO | 1. Ordering/holding costs; 2. Shortage freq.; 3. Stock-outs |
| Tsou and Kao [43] | Optimization/MOEMO | 1. Ordering/holding costs; 2. Shortage freq.; 3. Stock-outs |
| Wangsa and Wee [41] | Optimization/Heuristic | 1. Total costs (inc. ordering, holding, shortage, setup and freight/transportation costs) |
| Vargas and Metters [31] | Optimization/DEA; Inventory theory | 1. Fill rate; 2. Holding and setup costs |
Nomenclature: NLP: Nonlinear programming; PV: Present Value; DES: Discrete Event Simulation; MOPSO: Multi-Objective Particle Swarm Optimization; PVB: Prékopa-Vizvari-Badics algorithm; pLEPs: p-level efficient points method; MOGA: Multi-Objective Genetic Algorithm; MOEMO: Multi-Objective ElectroMagnetism-like Optimization; DEA: Data Envelopment Analysis.