Study of Industrial Model for Five-Input, Five-Stage Queueing Network

In this paper, we have developed an industrial model for textile industry with five-input, five-stage queueing network, wherein system receives orders from clients that are waiting to be served. The aim of this paper is to compute the optimal path that will provide the least response time for delivery of items to the final destination, through the five stages under queueing network. The mean number of items that can be delivered is minimum response time constitute the optimal capacity of the network. The last node in each stage of the network can be executed in the least possible response time. Various performance indices were carried out such as mean number of item in the system, mean number of item in queue, mean response time, mean waiting time. We have established the equivalent queueing network to analyze the various performance measures with numerical illustration and graph.

Customer Behavior Prediction using K-Means Clustering Algorithm

Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide customers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer's purchase behavior can be predicted. From large number of association rules with sufficient coverage, the customer's purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on effectiveness and utility of the approach. The results show that the proposed approach can gain a well insight into customers' segmentation and thus their behavior can be predicted.

Inventory Control and Big Data

This chapter provides the relation between automated inventory control and generation of big data using the process. Conversion from manual to automated inventory process leads to generation and management of too much data. Possible boons and banes of the conversion of inventory control system to automated one are discussed in detail. In the initial sections explanation about inventory control and benefits of automating is given. Then overall architecture of big data and its management is discussed .Finally, tradeoff between the usage of automated inventory control system with its benefits and generation of too much data and handling it, is discussed.

Some Studies in Multi-Storage Inventory System

In this chapter, a multi-storage inventory system has been considered to develop a deterministic inventory model in finite planning horizon. Realistically, it is shown that due to large stock and insufficient space of existing own warehouse (OW); excess items are stored in single rented warehouse (RW). Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. Here, the replenishment cycle lengths are of equal length, the demand rate is a continuous linear increasing function of time and partially backlogged shortages are allowed in all cycles. In each cycle, the replenishment cost is assumed to be dependent linearly on lot size and the stocks of RW are also transported to OW in continuous release pattern. The model is formulated as a constrained non-linear mixed integer cost objective function under single management. Finally, results with a sensitivity analysis have been shown with the help of a real coded GA.

Optimal Ordering for Deteriorating Items of Fixed-Life with Quadratic Demand and Two-Level Trade Credit

This chapter studies the retailer's ordering policies when items in the stocking system has fixed life time and subject to deteriorate with time. The demand is considered to be quadratically decreasing. The supplier offers credit period to the retailer which in turn is partially passed on to customer. The retailer is the decision maker and the objective is to minimize the total cost of the system by ordering optimum purchase quantity. Numerical examples are given to find the best possible scenario for the retailer. Sensitivity analysis is carried out to derive player's insights.

EPQ Inventory Models under mλ-Measure

This chapter considers an EPQ model with and without shortages under linear combination of Possibility measure and Necessity measure. Based on the possibility measure and necessity measure, m? -measure is introduced and some important properties are discussed. To capture the real life situation, various EPQ model parameters for instance, demand, setup cost, holding cost and backorder cost are characterized as Trapezoidal Fuzzy Number. Two fuzzy chance-constrained programming models are constructed under m?-measure. The objective is to determine optimistic and pessimistic values of the fuzzy objective function with some predefined degree of m?-measure. Using fuzzy arithmetical operations under Function Principle, the fuzzy problem is first transferred to an equivalent crisp problem. An analytical approach is developed to resolve the reduced models. To investigate the characteristics of the proposed model and to obtain the optimal decision under different situations, numerical illustrations are presented along with a sensitivity analysis.

Deteriorating Inventory Model under Permissible Delay in Payments and Fuzzy Environment

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.

Lot Size Model for Reverse Logistics with Quadratic Demand

Taking care of nature is the prime responsibility of the companies nowadays. This chapter proposes inventory model with reverse logistics for environmental concerns. Customers return used products and after remanufacturing such products they become as good as new products. Now, demand is satisfied by newly produced as well as remanufactured products. Quadratic demand is discussed in the present inventory model. Such demand increases initially and after sometimes it shows decreasing pattern. Shortages are also allowed to take place. Optimal cost is worked out for the present system. Numerical example and sensitivity analysis are given to validate mathematical model and to find critical inventory parameters. Based on it, managerial issues are discussed.

Optimal Inventory Classification using Data Mining Techniques

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

Application of Genetic Algorithms in Inventory Control

The genetic algorithm (GA) is an adaptive heuristic search procedures based on the mechanics of natural selection and natural genetics. Inventory control is widely used in the area of mathematical sciences, management sciences; system science, industrial engineering, production engineering etc. but they have wide differences in mathematical and computation maturity. This chapter enables the reader to understand the basic theory of genetic algorithm and how to apply the genetic algorithms for optimizing the parameters in inventory control The current and future trend of the research with the definition of key terms of genetic algorithm has also incorporated in this chapter.