Simulation-Optimisation Approach to Stochastic Inventory Control with Perishability

In order to tailor inventory control to urgent needs of grocery retail, the discrete-event simulation model with realistic perishability mechanics is proposed in the paper. The model is stochastic and operates with multiple products under constrained total inventory capacity. Besides, the model under consideration is distinguished by quantity discount, uncertain replenishment lags and lost sales. The paper presents both mathematical description and algorithmic implementation. An optimisation framework based on a genetic algorithm is also proposed for deriving an optimal control policy. The proposed approach contributes to the field of industrial engineering by providing a simple and flexible way to compute nearly-optimal inventory control parameters.

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Simulation optimization of an inventory control model for a reverse logistics system

Decision Science Letters ◽

10.5267/j.dsl.2021.9.001 ◽

2022 ◽

Vol 11 (1) ◽

pp. 43-54 ◽

Cited By ~ 1

Author(s):

Hanane Rachih ◽

Fatima Zahra Mhada ◽

Raddouane Chiheb

Keyword(s):

Inventory Control ◽

Reverse Logistics ◽

New Products ◽

Discrete Event ◽

Control Policy ◽

Control Model ◽

Logistics System ◽

Event Simulation ◽

Inventory Control Policy ◽

Inventory Control Model

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

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Structure of Optimal Solutions to Periodic-Review Total-Cost Stochastic Inventory Control Problems

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3003977.3003985 ◽

2016 ◽

Vol 44 (2) ◽

pp. 21-23

Author(s):

Eugene A. Feinberg ◽

Yan Liang

Keyword(s):

Inventory Control ◽

Control Problems ◽

Optimal Solutions ◽

Periodic Review ◽

Total Cost ◽

Stochastic Inventory ◽

Stochastic Inventory Control

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Metamodelling of Inventory-Control Simulations Based on a Multilayer Perceptron

Transport and Telecommunication Journal ◽

10.2478/ttj-2019-0021 ◽

2019 ◽

Vol 20 (3) ◽

pp. 251-259 ◽

Cited By ~ 4

Author(s):

Ilya Jackson ◽

Jurijs Tolujevs ◽

Sebastian Lang ◽

Zhandos Kegenbekov

Keyword(s):

Control System ◽

Inventory Control ◽

Multilayer Perceptron ◽

Simulation Optimization ◽

Activation Function ◽

Control Problems ◽

Perishable Products ◽

Stochastic Inventory ◽

Stochastic Inventory Control ◽

Rectified Linear Unit

Abstract Inventory control problems arise in various industries, and each single real-world inventory is replete with non-standard factors and subtleties. Practical stochastic inventory control problems are often analytically intractable, because of their complexity. In this regard, simulation-optimization is becoming more and more popular tool for solving complicated business-driven problems. Unfortunately, simulation, especially detailed, is both time and memory consuming. In the light of this fact, it may be more reasonable to use an alternative cheaper-to-compute metamodel, which is specifically designed in order to approximate an original simulation. In this research we discus metamodelling of stochastic multiproduct inventory control system with perishable products using a multilayer perceptron with a rectified linear unit as an activation function.

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