This paper presents the optimization of a single period inventory problem(SPIP) in particular a multi-item newsboy problem, where both fuzziness and randomness occur. Due to lack of data, the demand is subjectively determined. So demand is considered as a fuzzy random variable and the purchasing cost as a fuzzy number. The optimum order quantity and the expected profit have been obtained by using Buckley's concept of minimization of fuzzy numbers.The technique developed to transform a fuzzy single period inventory model into a crisp model and has been subjected to numerical verification.

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 demandD~jfor each productjin the objective function is considered as a fuzzy random variable (FRV) and with the available storage space areaW~, 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.

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In this study, multi-objective inventory model of deteriorating and perishable items is developed under space and budget constraints. Demand is stock dependent and power function of time. This model is completely a new model in the sense that the model is applied to those items whose deterioration rate is maximum. Shortages are allowed in each cycle. The main aim of this paper is to find different time points for each cycle where shortage occurs and inventory depletes respectively so that both total cost and shortage cost can be minimized simultaneously. The model is developed in both crisp and fuzzy environment. In fuzzy environment, the objectives are considered as fuzzy constraints. For this the decision maker needs to establish an aspiration level for the objective functions which he wants to achieve as far as possible. This paper aims to use fuzzy non-linear programming (FNLP) and intuitionistic fuzzy optimization (IFO) techniques for the multi-objective inventory model. Comparison is based on different optimization techniques in different environment using numerical examples. Graph of the objective functions are provided. System diagram of the model and algorithm for solving the model are provided. Also, sensitivity analysis is made using different parameters of the model.

Atrial fibrillation (Afib) is associated with 15%‐25% of strokes. Wearable devices like Apple watch can generate an electrocardiogram and can increase detection of Afib in general population. Early diagnosis and treatment can decrease the risk of strokes.