Forecast Methods and Periodic Review Inventory Model for Supply Planning to Reduce Food Waste

Food waste is an important economic and resource problem in all countries around the world. Particularly, the restaurant sector highly contributes to food waste and limited efforts or studies have been performed to overcome this problem. In this context, the present study addresses an alternative to improve the supply planning for perishable products in the restaurant sector through the application of specific forecasting methods and a stochastic inventory control model. For this purpose, a real enterprise within this economic sector was considered. Our findings support that monthly forecasts can be more appropriate for accurate demand estimation and supply planning of perishable products, which is important to reduce unnecessary products. Also, the periodic review inventory control model can lead to a more appropriate supply scheme to reduce the waste of surplus food. These findings and the proposed techniques can be used for other economic entities to reduce product waste due to poor supply planning.

Modeling the Hidden Risk of Polyethylene Contaminants within the Supply Chain

Inventory management is very important to support the supply chain of the manufacturing and service industries. All inventories involve warehousing; however, most of the products and packages are associated to plastic which is the main generator of polyethylene (phthalate) pollution in the air and water resources. In fact, phthalate has been identified as the cause of serious health conditions and its impact within the operation of logistic processes has not been studied. In this work, we perform research on the generation of phthalate as the control on these emissions is important to adjust the supply strategy to reduce the human risk exposure and contamination of the environment. For this purpose, generation of phthalate is modeled through the use of artificial neural networks (ANNs) and its impact on the supply strategy is assessed through its integration within a stochastic inventory control model. As presented, it is possible to adjust the supply strategy to reduce the cumulative generation of phthalate within the warehouse and thus reduce its impact on human health and environment sustainability.

Provably Near-Optimal Approximation Schemes for Implicit Stochastic and Sample-Based Dynamic Programs

In this paper, we address two models of nondeterministic discrete time finite-horizon dynamic programs (DPs): implicit stochastic DPs (the information about the random events is given by value oracles to their cumulative distribution functions) and sample-based DPs (the information about the random events is deduced by drawing random samples). Such data-driven models frequently appear in practice, where the cumulative distribution functions of the underlying random variables are either unavailable or too complicated to work with. In both models, the single-period cost functions are accessed via value oracle calls and assumed to possess either monotone or convex structure. We develop the first near-optimal relative approximation schemes for each of the two models. Applications in stochastic inventory control (that is, several variants of the so-called newsvendor problem) are discussed in detail. Our results are achieved by a combination of Bellman equation calculations, density estimation results, and extensions of the technique of K-approximation sets and functions introduced by Halman et al. (2009) [Halman N, Klabjan D, Mostagir M, Orlin J, Simchi-Levi D (2009) A fully polynomial time approximation scheme for single-item stochastic inventory control with discrete demand. Math. Oper. Res. 34(3):674–685.].