Determination of Optimal Reserve between Two Machines in Series with the Repair Time has Change of Distribution after the Truncation Point

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 577-585
Author(s):  
T. Vivekanandan ◽  
S. Sachithanantham
Keyword(s):  
Real Life ◽  
Random Variable ◽  
Repair Time ◽  
Inventory Level ◽  
Optimal Size ◽  
Truncation Point ◽  
In Series ◽  
Optimal Reserve ◽  
Optimal Inventory Level ◽  
Two Machines

In inventory control, suitable models for various real life systems are constructed with the objective of determining the optimal inventory level.  A new type of inventory model using the so-called change of distribution property is analyzed in this paper. There are two machines M1 and M2  in series and the output of M1 is the input of M2. Hence a reserve inventory between M1 and M2 is to be maintained. The method of obtaining the optimal size of reserve inventory, assuming cost of excess inventory, cost of shortage and when the rate of consumption of M2  is a constant, has already been attempted.  In this paper, it is assumed that the repair time of M1  is a random variable and the distribution of the same undergoes a change of distribution  after the truncation point X0 , which is taken to be a random variable.  The optimal size of the reserve inventory is obtained under the above said  assumption . Numerical illustrations are also provided.

scholarly journals Variable demand model for periodically reviewing with allowing refunding parts of the orders

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
O. M. Hollah
Keyword(s):  
Field Study ◽  
Lead Time ◽  
Real Life ◽  
Random Variable ◽  
Inventory Level ◽  
Periodic Review ◽  
Demand Model ◽  
Time Intervals ◽  
Lagrange Multiplier Technique ◽  
Periodic Review Inventory

AbstractDepending on a field study for one of the largest iron and paints warehouses in Egypt, this paper presents a new multi-item periodic review inventory model considering the refunding quantity cost. Through this field study, we found that the inventory level is monitored periodically at equal time intervals. Returning a part of the goods that were previously ordered is permitted. Also, a shortage is permissible to occur despite having orders, and it is a combination of the backorder and lost sales. This model has been applied in both crisp and fuzzy environments since the fuzzy case is more suitable for real-life than crisp. The Lagrange multiplier technique is used for solving the restricted mathematical model. Here, the demand is a random variable that follows the normal distribution with zero lead-time. Finally, the model is followed by a real application to clarify the model and prove its efficiency.

Markovian Analysis of Aging Distributed Systems Remaining Life

2014 ◽  
Author(s):  
Anna Bushinskaya ◽  
Sviatoslav Timashev
Keyword(s):  
Distributed Systems ◽  
Markov Process ◽  
Residual Life ◽  
Limit State ◽  
Real Life ◽  
Random Variable ◽  
Operating Pressure ◽  
Remaining Life ◽  
Failure Pressure ◽  
The Moment

Correct assessment of the remaining life of distributed systems such as pipeline systems (PS) with defects plays a crucial role in solving the problem of their integrity. Authors propose a methodology which allows estimating the random residual time (remaining life) of transition of a PS from its current state to a critical or limit state, based on available information on the sizes of the set of growing defects found during an in line inspection (ILI), followed by verification or direct assessment. PS with many actively growing defects is a physical distributed system, which transits from one physical state to another. This transition finally leads to failure of its components, each component being a defect. Such process can be described by a Markov process. The degradation of the PS (measured as monotonous deterioration of its failure pressure Pf (t)) is considered as a non-homogeneous pure death Markov process (NPDMP) of the continuous time and discrete states type. Failure pressure is calculated using one of the internationally recognized pipeline design codes: B13G, B31Gmod, DNV, Battelle and Shell-92. The NPDMP is described by a system of non-homogeneous differential equations, which allows calculating the probability of defects failure pressure being in each of its possible states. On the basis of these probabilities the gamma-percent residual life of defects is calculated. In other words, the moment of time tγ is calculated, which is a random variable, when the failure pressure of pipeline defect Pf (tγ) > Pop, with probability γ, where Pop is the operating pressure. The developed methodology was successfully applied to a real life case, which is presented and discussed.

scholarly journals A Fuzzy EPQ Model for Non-Instantaneous Deteriorating Items where Production Depends on Demand which is Proportional to Population, Selling Price as well as Advertisement

2019 ◽  
Vol 10 (5) ◽  
pp. 1679 ◽  
Author(s):  
Abhishek Kanti Biswas ◽  
Sahidul Islam
Keyword(s):  
Optimal Solution ◽  
Inventory Level ◽  
Selling Price ◽  
Distance Method ◽  
Defective Items ◽  
Holding Cost ◽  
Epq Model ◽  
Optimal Inventory Level ◽  
Cost Parameters ◽  
Total Average

The inventory system has been drawing more intrigue because this system deals with the decision that minimizes the total average cost or maximizes the total average profit. For any farm, the demand for any items depends upon population, selling price and frequency of advertisement etc. Most of the model, it is assumed that deterioration of any item in inventory starts from the beginning of their production. But in reality, many goods are maintaining their good quality or original condition for some time. So, price discount is availed for defective items. Our target is to calculate the total optimal cost and the optimal inventory level for this inventory model in a crisp and fuzzy environment. Here Holding cost taken as constant and no-shortages are allowed. The cost parameters are considered as Triangular Fuzzy Numbers and to defuzzify the model Signed Distance Method is applied. A numerical example of the optimal solution is given to clarify the model. The changes of different parameters effect on the optimal total cost are presented and sensitivity analysis is given.JEL Classification: C44, Y80, C61Mathematics Subject Classification: 90B05

Model Predictive Control of a Forecasting Production System with Deteriorating Items

2015 ◽  
Vol 6 (4) ◽  
pp. 19-37 ◽  
Author(s):  
Abdelhak Mezghiche ◽  
Mustapha Moulaï ◽  
Lotfi Tadj
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Production System ◽  
Objective Function Value ◽  
Inventory Level ◽  
Optimal Production ◽  
Control Approach ◽  
System Parameters ◽  
Demand Rate ◽  
Optimal Inventory Level

The authors consider in this paper an integrated forecasting production system of the tracking type. The demand rate during a certain period depends on the demand rate of the previous period. Also, the demand rate depends on the inventory level. Items on the shelves are subject to deterioration. Using a model predictive control approach, the authors obtain the optimal production rate, the optimal inventory level, the optimal demand rate, and the optimal objective function value, explicitly in terms of the system parameters. A numerical example is presented.

Convergence of the number of failed components in a Markov system with nonidentical components

2001 ◽  
Vol 38 (4) ◽  
pp. 882-897 ◽  
Author(s):  
Jean-Louis Bon ◽  
Eugen Păltănea
Keyword(s):  
Total Variation ◽  
Random Function ◽  
Random Variable ◽  
Quality Parameter ◽  
Repair Time ◽  
Total Variation Distance ◽  
Repairable Systems ◽  
Markov System ◽  
Variation Distance

For most repairable systems, the number N(t) of failed components at time t appears to be a good quality parameter, so it is critical to study this random function. Here the components are assumed to be independent and both their lifetime and their repair time are exponentially distributed. Moreover, the system is considered new at time 0. Our aim is to compare the random variable N(t) with N(∞), especially in terms of total variation distance. This analysis is used to prove a cut-off phenomenon in the same way as Ycart (1999) but without the assumption of identical components.

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