An Imperfect Production Model for Breakable Multi-Item with Dynamic Demand and Learning Effect on Rework over Random Planning Horizon

2021 ◽  
Vol 14 (12) ◽  
pp. 574
Author(s):  
Amalesh Kumar Manna ◽  
Leopoldo Eduardo Cárdenas-Barrón ◽  
Barun Das ◽  
Ali Akbar Shaikh ◽  
Armando Céspedes-Mota ◽  
...  
Keyword(s):  
Learning Effect ◽  
Planning Horizon ◽  
Inventory Model ◽  
Inventory System ◽  
Production Model ◽  
Inventory Models ◽  
Imperfect Production ◽  
Production Inventory ◽  
Random Planning Horizon ◽  
Production Inventory System

In recent times, in the literature of inventory management there exists a notorious interest in production-inventory models focused on imperfect production processes with a deterministic time horizon. Nevertheless, it is well-known that there is a high influence and impact caused by the learning effect on the production-inventory models in the random planning horizon. This research work formulates a mathematical model for a re-workable multi-item production-inventory system, in which the demand of the items depends on the accessible stock and selling revenue. The production-inventory model allows shortages and these are partial backlogged over a random planning horizon. Also, the learning effect on the rework policy, inflation, and the time value of money are considered. The main aim is to determine the optimum production rates that minimize the expected total cost of the multi-item production-inventory system. A numerical example is solved and a detailed sensitivity analysis is conducted in order to study the production-inventory model.

Multi-item EPQ model with learning effect on imperfect production over fuzzy-random planning horizon

2016 ◽  
Vol 4 (1) ◽  
pp. 80-110 ◽  
Author(s):  
Amalesh Kumar Manna ◽  
Barun Das ◽  
Jayanta Kumar Dey ◽  
Shyamal Kumar Mondal
Keyword(s):  
Learning Effect ◽  
Planning Horizon ◽  
Imperfect Production ◽  
Epq Model ◽  
Random Planning Horizon ◽  
Fuzzy Random

Modeling condition-based maintenance and replacement strategies for an imperfect production-inventory system

2016 ◽  
Vol 232 (10) ◽  
pp. 1858-1871 ◽  
Author(s):  
Guoqing Cheng ◽  
Binghai Zhou ◽  
Faqun Qi ◽  
Ling Li
Keyword(s):  
Product Quality ◽  
Preventive Maintenance ◽  
Inventory System ◽  
Single Type ◽  
Maintenance Cost ◽  
Total Cost ◽  
Penalty Cost ◽  
Imperfect Production ◽  
Production Inventory ◽  
Production Inventory System

In this article, we consider an imperfect production-inventory system which produces a single type of product to meet the constant demand. The system deteriorates stochastically with usage and the deterioration process is modeled by a non-stationary gamma process. The production process is imperfect which means that the system produces some non-conforming items and the product quality depends on the degradation level of the production system. To prevent the system from deteriorating worse and improve the product quality, preventive maintenance is performed when the level of the system degradation reaches a certain threshold. However, the preventive maintenance is imperfect which cannot restore the system as good as new. Hence, the aging system will be replaced by a new one after some production cycles. The preventive maintenance cost, the replacement cost, the production cost, the inventory holding cost and the penalty cost of lost sales are considered in this article. The objective is to minimize the total cost per unit item which depends on two decision variables: the preventive maintenance threshold and the time at which the system is replaced. We derive the explicit expression of the total cost per unit item and the optimal joint policy can be obtained numerically. An illustrative example and sensitivity analysis are given to demonstrate the proposed model.

scholarly journals Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jianqiao Ge ◽  
Songtao Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Inventory Model ◽  
Inventory System ◽  
Tracking Accuracy ◽  
Uncertain Environment ◽  
Fast Tracking ◽  
Production Inventory ◽  
Fuzzy Neural ◽  
Production Inventory System

In order to achieve the actual inventory effectively tracking the target inventory under uncertain environment, this paper investigates an adaptive inventory controller for the production-inventory system. First, an uncertain production-inventory model is constructed, and then, the uncertainty of the production-inventory model is approximated by a fuzzy neural network. Secondly, in terms of the design of adaptive control law, the adaptive inventory controller is developed. Under the adaptive inventory controller, the actual inventory can track the target inventory in real time and the production-inventory system can be robustly stable in uncertain environment. Finally, the results of three simulation experiments show that the proposed adaptive inventory controller can realize both the fast tracking speed and the high tracking accuracy.

scholarly journals Planning Horizon for Production Inventory Models with Production Rate Dependent on Demand and Inventory Level

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jennifer Lin ◽  
Henry C. J. Chao ◽  
Peterson Julian
Keyword(s):  
Production Rate ◽  
Optimal Solution ◽  
Planning Horizon ◽  
Inventory Model ◽  
Inventory Level ◽  
Inventory Models ◽  
New Production ◽  
Production Inventory ◽  
Infinite Planning Horizon ◽  
Finite Planning Horizon

This paper discusses why the selection of a finite planning horizon is preferable to an infinite one for a replenishment policy of production inventory models. In a production inventory model, the production rate is dependent on both the demand rate and the inventory level. When there is an exponentially decreasing demand, the application of an infinite planning horizon model is not suitable. The emphasis of this paper is threefold. First, while pointing out questionable results from a previous study, we propose a corrected infinite planning horizon inventory model for the first replenishment cycle. Second, while investigating the optimal solution for the minimization problem, we found that the infinite planning horizon should not be applied when dealing with an exponentially decreasing demand. Third, we developed a new production inventory model under a finite planning horizon for practitioners. Numerical examples are provided to support our findings.

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