Combining Acquisition Planning with Inventory Management Under Uncertain Demand

2008 ◽  
Vol 46 (2) ◽  
pp. 129-135
Author(s):  
Guoqing Zhang
Keyword(s):  
Inventory Management ◽  
Uncertain Demand ◽  
Acquisition Planning

scholarly journals Modelling Inventory Management System at Distribution Company: Case Study

2010 ◽  
Vol 42 (1) ◽  
pp. 87-93 ◽  
Author(s):  
Oksana Soshko ◽  
Vilmars Vjakse ◽  
Yuri Merkuryev
Keyword(s):  
Inventory Management ◽  
Optimization Model ◽  
Management System ◽  
Current Problem ◽  
Simulation Models ◽  
Uncertain Demand ◽  
Inventory Models ◽  
Inventory Management System ◽  
Distribution Company

Modelling Inventory Management System at Distribution Company: Case Study The paper presents a case study on improving inventory management at the distribution company which operates in Latvia. The case study is focused on application of different modelling approaches in inventory management under uncertain demand, namely inventory models, simulation models and optimization model. The functionality of each model as well as its benefits for the current problem is discussed in the end of the paper.

Inventory management under highly uncertain demand

2007 ◽  
Vol 35 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Guillermo Gallego ◽  
Kaan Katircioglu ◽  
Bala Ramachandran
Keyword(s):  
Inventory Management ◽  
Uncertain Demand

scholarly journals Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

2020 ◽  
Vol 10 (21) ◽  
pp. 7851
Author(s):  
Przemysław Ignaciuk ◽  
Łukasz Wieczorek
Keyword(s):  
Inventory Management ◽  
Numerical Experiments ◽  
Fitness Function ◽  
Management Policy ◽  
Uncertain Demand ◽  
Satisfaction Level ◽  
Contact Points ◽  
Operational Costs ◽  
Distribution Process ◽  
Continuous Genetic Algorithm

Globalization opens up new perspectives for handling goods distribution in logistic networks. However, establishing an efficient inventory policy is challenging by virtue of the analytical and computational complexity. In this study, the goods distribution process that was governed by the order-up-to policy, implemented in either a distributed or centralized way, was investigated in the logistic systems with complex interconnection topologies. Uncertain demand may be imposed at any node, not just at conveniently chosen contact points, with a lost-sales assumption that introduces a non-linearity into the node dynamics. In order to adjust the policy parameters, the continuous genetic algorithm (CGA) was applied, with the fitness function incorporating both the operational costs and customer satisfaction level. This study investigated how to select the parameters of the popular inventory management policy when operating in the non-trivial networked structures. Moreover, precise guidelines for the CGA tuning in the considered class of problems were provided and evaluated in extensive numerical experiments.

Discrete Event Simulation in Inventory Management

2019 ◽  
pp. 977-988
Author(s):  
Linh Nguyen Khanh Duong ◽  
Lincoln C. Wood
Keyword(s):  
Inventory Management ◽  
Discrete Event ◽  
Simulation Models ◽  
Uncertain Demand ◽  
Perishable Products ◽  
Chain Management ◽  
Perishable Inventory ◽  
Event Simulation ◽  
Multiple Products ◽  
Alternative Product

Perishability and substitutability are two key attributes that cannot be ignored in supply chain management. Once produced, perishable products have a finite shelf life. When expired, they are either partially or wholly value-less. The more time that perishable inventory is in storage, the less time it is available for sale to customers. Product substitution is a possibility when considering multiple products. Research indicates that an alternative product is willingly chosen by customers if the preferred one is out of stock. Managers must decide on the replenishment time and replenishment quantity for each item within product subcategory to maximize expected profits under uncertain demand while minimizing the instances of running out of inventory (i.e., a stock out). The combination of these factors often requires simulation models to be developed to understand the behavior of the system as the parameters change. Simulation can incorporate stochasticity and complexity while providing detailed output for further analysis and optimization work.

scholarly journals A Multiproduct Single-Period Inventory Management Problem under Variable Possibility Distributions

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Zhaozhuang Guo ◽  
Shengnan Tian ◽  
Yankui Liu
Keyword(s):  
Inventory Management ◽  
Carbon Emission ◽  
Deterministic Model ◽  
Optimization Method ◽  
Uncertain Demand ◽  
Optimal Order ◽  
Management Problem ◽  
Single Period ◽  
Possibility Distributions ◽  
Order Quantities

In multiproduct single-period inventory management problem (MSIMP), the optimal order quantity often depends on the distributions of uncertain parameters. However, the distribution information about uncertain parameters is usually partially available. To model this situation, a MSIMP is studied by credibilistic optimization method, where the uncertain demand and carbon emission are characterized by variable possibility distributions. First, the uncertain demand and carbon emission are characterized by generalized parametric interval-valued (PIV) fuzzy variables, and the analytical expressions about the mean values and second-order moments of selection variables are established. Taking second-order moment as a risk measure, a new credibilistic multiproduct single-period inventory management model is developed under mean-moment optimization criterion. Furthermore, the proposed model is converted to its equivalent deterministic model. Taking advantage of the structural characteristics of the deterministic model, a domain decomposition method is designed to find the optimal order quantities. Finally, a numerical example is provided to illustrate the efficiency of the proposed mean-moment credibilistic optimization method. The computational results demonstrate that a small perturbation of the possibility distribution can make the nominal optimal solution infeasible. In this case, the decision makers should employ the proposed credibilistic optimization method to find the optimal order quantities.

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