scholarly journals A Study of an EOQ Model under Lock Fuzzy Environment

Mathematics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 75 ◽  
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Sankar Prasad Mondal
Keyword(s):  
Inventory Management ◽  
Numerical Study ◽  
Management Problem ◽  
Final State ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Computer Based ◽  
The Cost ◽  
Cost Vector

The present article was developed for the economic order quantity (EOQ) inventory model under daytime, non-random, uncertain demand. In any inventory management problem, several parameters are involved that are basically flexible in nature with the progress of time. This model can be split into three different sub-models, assuming the demand rate and the cost vector associated with the model are non-randomly uncertain (i.e., fuzzy), and these may include some of the retained learning experiences of the decision-maker (DM). However, the DM has the option of revising his/her decision through the application of the appropriate key vector of the fuzzy locks in their final state. The basic novelty of the present model is that it includes a computer-based decision‐making process involving flowchart algorithms that are able to identify and update the key vectors automatically. The numerical study indicates that when all parameters are assumed to be fuzzy, the double keys of the fuzzy lock provide a more accurate optimum than other methods. Sensitivity analysis and graphical illustrations are made for better justification of the model.

A study of an EOQ model with public-screened discounted items under cloudy fuzzy demand rate

2021 ◽  
pp. 1-12
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Madhumangal Pal ◽  
Sankar Prasad Mondal
Keyword(s):  
Uncertain Demand ◽  
Order Quantity ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Imperfect Items ◽  
Demand Rate ◽  
Computer Based ◽  
Fuzzy Degree ◽  
Graphical Illustration

This article deals with an economic order quantity inventory model of imperfect items under non-random uncertain demand. Here we consider the customers screen the imperfect items during the selling period. After a certain period of time, the imperfect items are sold at a discounted price. We split the model into three cases, assuming that the demand rate increases, decreases, and is constant in the discount period. Firstly, we solve the crisp model, and then the model is converted into a fuzzy environment. Here we consider the dense fuzzy, parabolic fuzzy, degree of fuzziness and cloudy fuzzy for a comparative study. The basic novelty of this paper is that a computer-based algorithm and flow chart have been given for the solution of the proposed model. Finally, sensitivity analysis and graphical illustration have been given to check the validity of the model.

scholarly journals Two decision makers’ single decision over a back order EOQ model with dense fuzzy demand rate

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Madhumangal Pal
Keyword(s):  
Decision Maker ◽  
Fuzzy Set ◽  
Raw Materials ◽  
Numerical Study ◽  
Joint Decision ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Back Order ◽  
Graphical Illustration

<p>In this article we develop an economic order quantity (EOQ) model with backlogging where the decision is made jointly from two decision maker supposed to view one of them as the industrialist (developer) and the other one as the responsible manager. The problem is handled under dense fuzzy environment. In fuzzy set theory the concept of dense fuzzy set is quite new which is depending upon the number of negotiations/ turnover made by industrial developers with the supplier of raw materials and/or the customers.  Moreover, we have discussed the preliminary concept on dense fuzzy sets with their corresponding membership functions and appropriate defuzzification method. The numerical study explores that the solution under joint decision maker giving the finer optimum of the objective function. A sensitive analysis, graphical illustration and conclusion are made for justification the new approach.</p>

scholarly journals Two decision makers’ single decision over a back order EOQ model with dense fuzzy demand rate

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Madhumangal Pal
Keyword(s):  
Decision Maker ◽  
Fuzzy Set ◽  
Raw Materials ◽  
Numerical Study ◽  
Joint Decision ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Back Order ◽  
Graphical Illustration

<p>In this article we develop an economic order quantity (EOQ) model with backlogging where the decision is made jointly from two decision maker supposed to view one of them as the industrialist (developer) and the other one as the responsible manager. The problem is handled under dense fuzzy environment. In fuzzy set theory the concept of dense fuzzy set is quite new which is depending upon the number of negotiations/ turnover made by industrial developers with the supplier of raw materials and/or the customers.  Moreover, we have discussed the preliminary concept on dense fuzzy sets with their corresponding membership functions and appropriate defuzzification method. The numerical study explores that the solution under joint decision maker giving the finer optimum of the objective function. A sensitive analysis, graphical illustration and conclusion are made for justification the new approach.</p>

ANALYSIS OF THE MANAGEMENT OF MATERIAL AND TECHNICAL ASSETS OF ENTERPRISES

2021 ◽  
Vol 116 (1) ◽  
pp. 293-299
Author(s):  
Aizhan Talgatkyzy Syrkebai
Keyword(s):  
Inventory Management ◽  
Realistic Model ◽  
Process Approach ◽  
Sustainable Competitive Advantage ◽  
Order Quantity ◽  
Specific Product ◽  
Abc Classification ◽  
Eoq Model ◽  
Demand Rate ◽  
Abc Analysis

Variability of demand for goods, depending on their availability and cost is a frequently observed phenomenon in the sphere of production and consumption. This paper discusses the possibility of using the ABC analysis for the inventory management system. This type of analysis makes it possible to apply various methods of inventory management for increasing the income and reducing the costs of particular products for manufacturers. As is known, the management process approach emphasizes the systematic study of management by defining management functions in an organization and then examining each in detail. There is a general agreement on the planning, organizing, and controlling functions. Traditional inventory management systems, such as the Lean Order Quantity (EOQ) model, are based on the assumptions of constant demand rate, constant inventory cost, and instant order. The EOQ assumption of instant order receipt means that the entire order quantity, that is, all units of the purchased lot is immediately received from the supplier. The Economic Production Quantity (EPQ) model weakens this assumption by including incremental order receipt, i.e. debit. In recent years, major advances in the sphere of management include the following elements: process approach management science decision support approach, scientific approach to human resources development, and sustainable competitive advantage. These four approaches complement one another in current practice and provide a useful framework for project management. This paper presents a realistic model of the goods production and their inventory, since ABC analysis or ABC classification is an integral part of materials management. According to this approach, the inventory is divided into three categories depending on income generation. The ABC analysis helps entrepreneurs identify the main types of products in the warehouse, prioritize the goods management based on their cost, and analyze customer demand for a specific product.

Fuzzification of EOQ Model Under the Condition of Permissible Delay in Payments

2012 ◽  
Vol 3 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Chandra K. Jaggi ◽  
Anuj Sharma ◽  
Reena Jain
Keyword(s):  
Fuzzy Numbers ◽  
Delay Period ◽  
Order Quantity ◽  
Distance Method ◽  
Permissible Delay In Payments ◽  
Signed Distance ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Delay In Payments ◽  
The Cost

This paper formulates an economic order quantity inventory model under the condition of permissible delay in payments in fuzzy environment. All the parameters of the model, excluding permissible delay period and cycle length, are taken to be trapezoidal Fuzzy numbers. The arithmetic operations are defined under the function principle. The cost function has been defuzzified using signed distance method and thereby solved to obtain the optimal replenishment period. The numerical example is presented to show the validity of the model followed by sensitivity analysis.

Deteriorating Inventory Model under Permissible Delay in Payments and Fuzzy Environment

2016 ◽  
pp. 77-99 ◽  
Author(s):  
Nita H. Shah ◽  
Sarla Pareek ◽  
Isha Sangal
Keyword(s):  
Solution Procedure ◽  
Inventory Model ◽  
Variable Cost ◽  
Selling Price ◽  
Gravity Method ◽  
Deterioration Rate ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Delay In Payments

This paper deals with the problem of determining the EOQ model for deteriorating items in the fuzzy sense where delay in payments is permissible. The demand rate, ordering cost, selling price per item and deterioration rate are taken as fuzzy numbers. The total variable cost in fuzzy sense is de-fuzzified using the centre of gravity method. The solution procedure has been explained with the help of numerical example.

EOQ Model with Permissible Delay in Payments under Fuzzy Environment

2014 ◽  
pp. 281-296
Author(s):  
Chandra K. Jaggi ◽  
Anuj Sharma ◽  
Reena Jain
Keyword(s):  
Cycle Length ◽  
Delay Period ◽  
Order Quantity ◽  
Distance Method ◽  
Permissible Delay In Payments ◽  
Signed Distance ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Delay In Payments ◽  
The Cost

This chapter introduces an economic order quantity inventory model under the condition of permissible delay in payments in fuzzy environment. All the parameters of the model, excluding permissible delay period and cycle length, are taken to be trapezoidal Fuzzy numbers. The arithmetic operations are defined under the function principle. The cost function has been defuzzified using signed distance method and thereby solved to obtain the optimal replenishment period. The numerical example is presented to show the validity of the model followed by sensitivity analysis.

scholarly journals A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate

2021 ◽  
Vol 4 (4) ◽  
pp. 81
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Madhumangal Pal ◽  
Sankar Prasad Mondal
Keyword(s):  
Fuzzy Set ◽  
Order Quantity ◽  
Inventory Cost ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Total Inventory ◽  
Growing Items ◽  
Graphical Illustration ◽  
The Ideal

In this article, the parabolic dense fuzzy set is defined, and its basic arithmetic operations are studied with graphical illustration. The lock set concept is incorporated in a parabolic dense fuzzy set. Then, it is applied to the problems of fishery culture via the modeling of an economic order quantity model. Here, the fingerlings are fed to reach the ideal size to fulfill the customer’s demand. The growth rate of the fingerlings is assumed as a linear function. After the sales of all fish, the pond is cleaned properly for a new cycle. Here, the model is solved in a crisp sense first. Then, we fuzzify the model considering the demand rate as a parabolic dense lock fuzzy number and obtain the result in a fuzzy environment. The main aim of our study was to find the quantity of the ordering items such that the total inventory cost gets a minimum value. Lastly, sensitivity analysis and graphical illustrations were added for better justification of our model.

scholarly journals Fuzzy Eoq Model with Shortages Using Kuhns-Tucker Conditions

2019 ◽  
Vol 8 (6) ◽  
pp. 822-827
Keyword(s):  
Fuzzy Membership Function ◽  
Lot Size ◽  
Total Cost ◽  
Penalty Cost ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Decision Variables ◽  
Proposed Model ◽  
Demand Rate ◽  
Optimum Solution

The work involves purchase inventory model with shortages under fuzzy environment. An EOQ model is formulated in which the input parameters like order cost, demand rate, carrying cost and penalty cost and the decision variables like the maximum invsentory level and the lot size are fuzzified using triangular fuzzy membership function. An optimum solution of the model is arrived by using Kuhn-Tucker conditions. The crisp values of the proposed model is obtained by defuzzifying the assumed model using Graded mean Integration (GMI) method. Finally the solutions are tabulated and an analsysis of the crisp and fuzzy values of the total cost has been done in this paper

scholarly journals Learning EOQ Model with Trade-Credit Financing Policy for Imperfect Quality Items under Cloudy Fuzzy Environment

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 246
Author(s):  
Mahesh Kumar Jayaswal ◽  
Mandeep Mittal ◽  
Osama Abdulaziz Alamri ◽  
Faizan Ahmad Khan
Keyword(s):  
Trade Credit ◽  
Credit Policy ◽  
Screening Process ◽  
Defective Items ◽  
Financing Policy ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Credit Financing ◽  
Quantity Sensitivity

An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model.

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