A Novel Method for Optimal Solution of Fuzzy Chance Constraint Single-Period Inventory Model

A method is proposed for solving single-period inventory fuzzy probabilistic model (SPIFPM) with fuzzy demand and fuzzy storage space under a chance constraint. Our objective is to maximize the total profit for both overstock and understock situations, where the demand D~j for each product j in the objective function is considered as a fuzzy random variable (FRV) and with the available storage space area W~, which is also a FRV under normal distribution and exponential distribution. Initially we used the weighted sum method to consider both overstock and understock situations. Then the fuzziness of the model is removed by ranking function method and the randomness of the model is removed by chance constrained programming problem, which is a deterministic nonlinear programming problem (NLPP) model. Finally this NLPP is solved by using LINGO software. To validate and to demonstrate the results of the proposed model, numerical examples are given

Optimization of Inventory Level Using Fuzzy Probabilistic Exponential Two Parameters Model

Inventory control is an important factor in trading activities. Inventory control aims to ensure product availability. Several factors affect the level of inventory including the level of demand factor, maximum inventory, and the level of deterioration. If the influencing factors cannot be defined with certainty and follow a certain statistic distribution then the fuzzy probabilistic approach can be applied. This research discusses the problem of optimizing the inventory of red chillies at the retail level. The level of deterioration is assumed to follow an exponential distribution and demand follows a Pareto distribution. Statistical parameters are estimated using the Maximum likelihood method and cost parameters are expressed by triangular fuzzy numbers. Based on the calculation results for several beta values, the highest total cost is 405143.6 rupiah, a maximum inventory level of 15 kg, and an order cycle time of 0.923 days

Supply chain inventory control for the iron and steel industry

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Multi-Source Backlogged Probabilistic Inventory Model for Crisp and Fuzzy Environment

This paper proposed a multi-item multi-source probabilistic periodic review inventory model under a varying holding cost constraint with zero lead time when: (1) the stock level decreases at a uniform rate over the cycle. (2) some costs are varying. (3) the demand is a random variable that follows some continuous distributions as (two-parameter exponential, Kumerswamy, Gamma, Beta, Rayleigh, Erlang distributions).The objective function under a constraint is imposed here in crisp and fuzzy environment. The objective is to find the optimal maximum inventory level for a given review time that minimize the expected annual total cost. Furthermore, a comparison between given distributions is made to find the optimal distribution that achieves the model under considerations. Finally, a numerical example is applied

Volume flexible multi items inventory system with imprecise environment

This paper addresses a multi items volume flexible system for time dependent decaying items with the concept of machine breakdown and imprecise environment. In this study, partially backlogged shortages have been discussed. All the costs are fuzzified with signed distance method. Numerical examples are given to illustrate the theoretical results and sensitivity analysis is given to validate the results for various parameters

Development of Fuzzy Inventory Model under Decreasing Demand and increasing Deterioration Rate

This research study proposed an inventory model with both the time varying variable deterioration and demand rate under the fuzzy environment. Fuzzy set theory is generally consider with imprecision and uncertainty nature of quantitative coefficients. In this system, we assumed the linearly increasing and decreasing function of time  for deterioration and demand respectively. In this research work, we discuss a fuzzy inventory model solving by signed distance method where demand follow time varying.&nbsp

Fuzzy inventory model on various lead time demand using multi criteria classification approach: A literature review

Inventory cost has become one of the major contributions to enterprise inefficiency.To minimize total cost, an enterprise is urged to manage an effective and efficient inventory system.In this case, an appropriate inventory model is in need. This study aims to propose an optimal inventory model by examining ABC multi-criteria classification approach using FANP (Fuzzy Analytical Network Process) and TOPSIS (Technique of Order Preferences by Similarity to the Ideal Solution) method. This study proposed a continuous review inventory model

Fuzzy Continuous Review Inventory Model using ABC Multi-Criteria Classification Approach: A Single Case Study

Abstract. Inventory is considered as the most expensive, yet important,to any companies. It representsapproximately 50% of the total investment. Inventory cost has become one of the majorcontributorsto inefficiency, therefore it should be managed effectively. This study aims to propose an alternative inventory model,  by using ABC multi-criteria classification approach to minimize total cost. By combining FANP (Fuzzy Analytical Network Process) and TOPSIS (Technique of Order Preferences by Similarity to the Ideal Solution), the ABC multi-criteria classification approach identified 12 items of 69 inventory items as “outstanding important class†that contributed to 80% total inventory cost. This finding  is then used as the basis to determine the proposed continuous review inventory model.This study found that by using fuzzy trapezoidal cost, the inventory  turnover ratio can be increased, and inventory cost can be decreased by 78% for each item in “class A†inventory.Keywords:ABC multi-criteria classification, FANP-TOPSIS, continuous review inventory model lead-time demand distribution, trapezoidal fuzzy numberÂ

Fuzzy production planning models for an unreliable production system with fuzzy production rate and stochastic/fuzzy demand rate

In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models