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Inventory plays an important role in the production process. One of the primary reasons why inventory management modeling is essential for the industry is because it will suffer immensely if there are insufficient food products to stock during the shutdown period. By determining the combined optimal cost of the retailers and wholesalers, this research significantly improves the service of the supply chain from wholesaler to retailer. The stochastic number for the imperfect perishable items is provided in this inventory study. By altering the parameter values, the uniform distribution is used to calculate these damaged items. This approach identifies the backordering quantity for both regular and uncertain fish band circumstances. The cost of maintaining the inventory will rise significantly of increased wastage due to a rise in deteriorating, which will result in the loss of perishable food items. The primary goal of this research paper is to transport them without being destroyed until they reach their desired consumers. By determining the back ordering quantity during a shutdown, one can decrease the overall expenses incurred by the retailers. These computational complexity measures are proven in a fuzzy uncertain environment. The main goal of this paper is to analyze the variation of demand during the unanticipated period and find the optimum total cost of the perishable products. The growth of production in a particular area at a particular time, interconnect with another large number of products in the same area and is calculated by Verhulst's demand with time depended on proficiency rate. Concerning the existing Verhulst's demand pattern for the production process, this paper introduced that for perishable items in a fuzzy unanticipated situation. A bountiful system analysis is performed to find the cost function under fuzzy environment and the sensitivity analysis is carried out to perform the key representation constant. [ABSTRACT FROM AUTHOR]

High utility mining is gaining prominence, and with the increasing set of business intelligence models, the scope of such significant practices is high. Rather than focusing only on profitability as one key utility metric, today's organizations believe in having more robust levels of the multi-objective filtering process. In this manuscript, a contemporary model of the high utility mining process is proposed, wherein the multiple averages are used for grading the recommendation of the itemsets for merchandise. The model's key advantage is its dynamic approach. The goods-related period of the average time interval can be flexible, alongside the fusion of multiple utility thresholds of diversified features chosen for itemsets recommendation. The performance analysis has been carried out by using a multi-fold cross-validation strategy. The results obtained for cross-validation show that the proposed model is outperforming the contemporary models with significant precision, specificity, sensitivity, and accuracy having values 97%, 95%, 98%, and 97% in respective order. Whereas, the contemporary models HUPM-MUO and MOEA-FHUI have obtained 93% and 90%, 88% and 82%, 89%, and 84%, and 89% and 83% in respective order of the corresponding metrics. The experimental study of the model denotes the effectiveness and ease with which the solution can generate results and produce significant output in the real-time environment for more dynamic and periodic decisions by different organizations. [ABSTRACT FROM AUTHOR]

A new closed loop supply chain network design problem is considered in this study. In comparison to the literature, the problem considers a more complete set of stages in the network e.g. simultaneous consideration of supplier, plant, distribution center, customer, and collection, recovering, recycling, and disposal centers. The problem makes location, capacity, allocation, demand mode and material/product flow decisions for optimizing net benefit including sales revenue, fixed establishing cost of facilities, transportation cost, material purchasing cost, production cost, and inventory holding cost. As a novelty, for the first time multi-mode demand satisfaction is considered in a closed loop supply chain problem. As another contribution, in order to be close to real-world situations, the problem is tackled in a fuzzy environment by using trapezoidal fuzzy parameters which yield a trapezoidal fuzzy objective function value. As solution methodology, considering the fuzzy objective function, the problem is reformulated as a multi-objective fuzzy mixed integer linear problem and is crisped using credibility measure of the fuzzy constraints. Finally, the crisp multi-objective version of the problem is solved by several hybrid fuzzy programming approaches to obtain a good efficient solution. Applying several numerical test problems, the SO method performs better than other approaches. [ABSTRACT FROM AUTHOR]

This paper applies the credibility distribution of fuzzy variables in uncertainty theory to formulate a fuzzy optimization model, so as to study the coordination mechanism of a distribution supply chain system with fuzzy demand. This practical system, focusing on the demand side of the supply chain, comprises multi-period, multi-manufacturer, multi-retailer, and multi-consumer. Next, using a novel modified sequence quadratic programming algorithm, the optimal order quantity, sales volume of the retailers, and the maximum expected return of the distribution supply chain are obtained. The effects of the wholesale price, retail price, and inventory cost on the expected return are also analyzed. Finally, the usfulness of the proposed modified sequence quadratic programming algorithm is compared against the conventional MATLAB optimal toolbox and genetic algorithm. Our computational results validate in favour of the proposed algorithm in terms of the computational time, number of iterations, and convergence rate. [ABSTRACT FROM AUTHOR]

This article considers an EOQ model for a delayed deteriorating item in which the demand varies with time and follows a power pattern. Shortages are allowed with partially backlogged and lost sales. We develop a mathematical model for the problem.The proposed model aims at minimizing the total inventory cost which depends on the length of time with positive and negative inventory level. Numerical examples with the effect of various changes in some possible parameters combination of the model are given to illustrate the effectiveness of the model and to gain some managerial decision. [ABSTRACT FROM AUTHOR]

The aim of this paper is to formulate and solve an optimal control problem under finite time horizon in fuzzy environment using fuzzy variational principle. Here an imperfect/defective item is produced to meet a time-dependent demand for a finite time period having no stock at both ends. The unit production cost is a function of production rate and also dependent on raw material cost, development costs due to durability and wear-tear cost. The cost function which consists of revenue, production and holding costs is formulated as a Fixed-Final Time and Fixed State System optimal control problem with finite time horizon. Here production rate is unknown and considered as a control variable and stock level is taken as a state variable. It is formulated to optimize the production rate so that total cost is minimum. For the fuzzy model, the production rate, stock level, inventory cost and development cost are taken as fuzzy. The models are solved by using conventional Variational Principle for crisp model and Fuzzy Variational Principle (FVP) for fuzzy model. For simulation, Mathematica-9.0 and the non-linear optimization technique Generalised Reduced Gradient Method (LINGO 11.0) have been used. The optimum results are illustrated both numerically and graphically. For the fuzzy model, the membership functions of fuzzy outputs are presented. The results of crisp model are also obtained from the fuzzy model. [ABSTRACT FROM AUTHOR]

The problem of delivering blood products from community blood centers to the demand points including hospital blood banks falls within the context of perishable inventory-routing problems (PIRP). This is due to the fact that the delivery should be made on the right time with the right delivery quantity at the right place such that the total possible perished items as well as routing and inventory costs are minimized. However, some unique characteristics of blood logistics including assigned and unassigned inventories, crossmatch release period, transfusion to crossmatch ratio and older-first policy have made the problem more difficult than the routine PIRPs and thus proposing a new modeling of the problem is required. In this paper, we first develop a mixed integer programming formulation for blood inventory-routing problem. Then, to cope with uncertainties, a novel robust possibilistic programming (RPP) approach is proposed. Afterward, a novel iterative branch-and-cut is developed to solve a number of numerical examples to optimality. Finally, by implementing a test scenario on the data inspired from a real Iranian blood supply chain, the significance and applicability of the proposed model and RPP approach is proven. [ABSTRACT FROM AUTHOR]