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Gonzalez, E., Epstein, L. D., & Godoy, V. (2012). Optimal number of bypasses: minimizing cost of calls to wireless phones under Calling Party Pays. Ann. Oper. Res., 199(1), 179–191.
Abstract: In telecommunications, Calling Party Pays is a billing formula that prescribes that the person who makes the call pays its full cost. Under CPP landline to wireless phone calls have a high cost for many organizations. They can reduce this cost at the expense of installing wireless bypasses to replace landline to wireless traffic with wirelesstowireless traffic, when the latter is cheaper than the former. Thus, for a given timehorizon, the cost of the project is a tradeoff between traffic towireless and the number of bypasses. We present a method to determine the number of bypasses that minimizes the expected cost of the project. This method takes into account hourly varying traffic intensity. Our method takes advantage of parallels with inventory models for rental items. Examples illustrate the economic value of our approach.

Rojas, F., & Leiva, V. (2016). Inventory management in food companies with statistically dependent demand. Acad.Rev. Latinoam. Adm., 29(4), 450–485.
Abstract: Purpose – The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”. Design/methodology/approach – The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm. Findings – When compared to the nonoptimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system). Research limitations/implications – The multivariate modeling can be improved by using (a) other nonGaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) timedependence, through autoregressive timeseries structures and moving average; (c) random modelling of leadtime; and (d) demands for components with values equal to zero using zeroinflated or adjusted probability distribution. Practical implications – Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally. Originality/value – The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

Rojas, F., Wanke, P., Coluccio, G., VegaVargas, J., & HuertaCanepa, G. F. (2020). Managing slowmoving item: a zeroinflated truncated normal approach for modeling demand. PeerJ Comput. Sci., 6, 22 pp.
Abstract: This paper proposes a slowmoving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zeroinflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multicriteria elements (total cost, fillrate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zeroinflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slowmoving item management for service companies, allows for the verification of decisionmaking using multiple criteria.
