scholarly journals A Partial Backlogging Inventory Model for Deteriorating Item under Fuzzy Inflation and Discounting over Random Planning Horizon: A Fuzzy Genetic Algorithm Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Dipak Kumar Jana ◽  
Barun Das ◽  
Tapan Kumar Roy
Keyword(s):  
Genetic Algorithm ◽  
Planning Horizon ◽  
Inventory Model ◽  
Expected Profit ◽  
Partial Backlogging ◽  
Inventory Models ◽  
Time Value Of Money ◽  
Time Value ◽  
Value Of Money ◽  
Random Planning Horizon

An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA) and fuzzy simulation-based genetic algorithm (FSGA) are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented.Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon.

scholarly journals Learning Effect on Inventory Model for Deteriorating and Ameliorating Items under Inflation and Partial Backlogging

2021 ◽  
pp. 1-15
Author(s):  
Biswaranjan Manda
Keyword(s):  
Inventory Management ◽  
Learning Effect ◽  
Inventory Model ◽  
Natural Phenomenon ◽  
Partial Backlogging ◽  
Related Factors ◽  
Time Value Of Money ◽  
Time Value ◽  
Special Cases ◽  
Value Of Money

Abstract Now-a-days, learning’s awareness is increasing in various disciplines because effect of learning has a direct impact on profit or loss, and it is a promotional deemed effective tool for inventory management. The basic concept of the inventory model is that 100% of the articles in an ordered lot are of good quality but this concept is not practically justifiable for the production process owing to product deterioration and related factors and so deterioration of items cannot be ignored. Again due to lack of considering the influence of demand, the ameliorating items for the amount of inventory is increasing gradually and it is a natural phenomenon observing in much life stock models. In addition, as the deep financial crisis continues to haunt the global economy, the effects of inflation and time value of money cannot be oblivious to an inventory system. Again another important factor is shortages which no retailer would prefer, and in practice are partially backlogged and partially lost. In order to convert the lost sales into sales, the retailer offers such customers an incentive, by charging them the price prevailing at the time of placing an order, instead of the current inflated price. Therefore, bearing in mind these facts, the present paper develops an inventory model for a retailer dealing with deteriorating and ameliorating items with stock dependent demand under the influence of inflation and time-value of money over a fixed planning horizon where holding cost follows the learning curve. Finally, a numerical example is provided to illustrate the proposed model. Comparative study of the optimal solutions with respect to major parameters under different special cases is carried out graphically and some managerial inferences have been presented. Subject classification: AMS Classification No. 90B05. Keywords: Inventory, Learning effect, Deteriorating, Ameliorating, Inflation, Time-value of money, Shortages and Partial backlogging.

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.

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