Development of a neural network mathematical model for demand forecasting in fluctuating markets

Research has shown that Neural Networks (NNs) when trained appropriately are the best forecasting system compared to conventional techniques. Research has shown that there is no system to accurately forecast sudden changes in demand for a given product. This paper reports on the development of a recovery method when a sudden change in demand has taken place. This error in forecasting demand leads to either excessive inventories of the product or shortages of it and can lead to substantial financial losses for the company producing or marketing the product. Two recovery methods have been developed and described in this paper: RZ recovery and Exponential Smoothing (ES). In the RZ recovery once a sudden change has taken place, a ‘soft’ Poke-Yoke (PY) system is setup warning the company that the normal forecasting system can no longer be relied upon and a recovery system needs to be initiated, with re-forecasting initiated

Nutrient recovery from digestates : techniques and end-products

In nitrate vulnerable zones application of animal manure to land is limited. Digestate from anaerobic digestion plants competes with manure for nutrient disposal on arable land, which forms a serious hinder for the biogas sector to develop in these regions. Hence, one of its biggest challenges is to find cost-effective and sustainable ways for digestate processing or disposal. Furthermore, primary phosphorus resources are becoming scarce and expensive and will be depleted within a certain time. This urges the need to recycle P from secondary sources, like digestate or manure. From a sustainability point of view, it seems therefore no more than logical that digestate processing techniques switched their focus to nutrient recovery rather than nutrient removal. This paper gives an overview of digestate processing techniques, with a special focus on nutrient recovery techniques. In this paper nutrient recovery techniques are delineated as techniques that (1) create an end-product with higher nutrient concentrations than the raw digestate or (2) separate the envisaged nutrients from organic compounds that are undesirable in the end-product, with the aim to produce an end-product that is fit for use in chemical or fertiliser industry or as a mineral fertiliser replacement. Various nutrient recovery techniques are described, with attention for some technical bottlenecks and the current state of development. Where possible, physicochemical characteristics of the endproducts are given

Lot-sizing for inventory systems with product recovery

We study inventory systems with product recovery. Recovered itemsare as-good-as-new and satisfy the same demands as new items. Thedemand rate and return fraction are deterministic. The relevantcosts are those for ordering recovery lots, for orderingproduction lots, for holding recoverable items in stock, and forholding new/recovered items in stock. We derive simple formulaethat determine the optimal lot-sizes for theproduction/procurement of new items and for the recovery ofreturned items. These formulae are valid for finite and infiniteproduction rates as well as finite and infinite recovery rates,and therefore more general than those in the literature.Moreover, the method of derivation is easy and insightful.product returns;recovery;lot sizing;EOQ/EPQ

Valuation of inventories in systems with product recovery

Valuation of inventories has different purposes, in particularaccounting and decision making, and it is not necessary for a firmto use the same valuation method for both purposes. In fact, it isnot uncommon to use accounting books as well as management books.In this chapter, we will only consider inventory values from theperspective of decision making. More specifically, we will analyzethe effect of inventory valuation on inventory control decisions(and not the corresponding financial results) for systems withproduct recovery.

Product line architecture recovery with outlier filtering in software families: the Apo-Games case study

Software product line (SPL) approach has been widely adopted to achieve systematic reuse in families of software products. Despite its benefits, developing an SPL from scratch requires high up-front investment. Because of that, organizations commonly create product variants with opportunistic reuse approaches (e.g., copy-and-paste or clone-and-own). However, maintenance and evolution of a large number of product variants is a challenging task. In this context, a family of products developed opportunistically is a good starting point to adopt SPLs, known as extractive approach for SPL adoption. One of the initial phases of the extractive approach is the recovery and definition of a product line architecture (PLA) based on existing software variants, to support variant derivation and also to allow the customization according to customers’ needs. The problem of defining a PLA from existing system variants is that some variants can become highly unrelated to their predecessors, known as outlier variants. The inclusion of outlier variants in the PLA recovery leads to additional effort and noise in the common structure and complicates architectural decisions. In this work, we present an automatic approach to identify and filter outlier variants during the recovery and definition of PLAs. Our approach identifies the minimum subset of cross-product architectural information for an effective PLA recovery. To evaluate our approach, we focus on real-world variants of the Apo-Games family. We recover a PLA taking as input 34 Apo-Game variants developed by using opportunistic reuse. The results provided evidence that our automatic approach is able to identify and filter outlier variants, allowing to eliminate exclusive packages and classes without removing the whole variant. We consider that the recovered PLA can help domain experts to take informed decisions to support SPL adoption.This research was partially funded by INES 2.0; CNPq grants 465614/2014-0 and 408356/2018-9; and FAPESB grants JCB0060/2016 and BOL2443/201

On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation

We study classic streaming and sparse recovery problems using deterministic linear sketches, including l1/l1 and linf/l1 sparse recovery problems (the latter also being known as l1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A in R^{m x n} and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: * A proof that linf/l1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(eps^{-2}*min{log n, (log n / log(1/eps))^2}). We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. * A new lower bound for the number of linear measurements required to solve l1/l1 sparse recovery. We show Omega(k/eps^2 + klog(n/k)/eps) measurements are required to recover an x' with |x - x'|_1 <= (1+eps)|x_{tail(k)}|_1, where x_{tail(k)} is x projected onto all but its largest k coordinates in magnitude. * A tight bound of m = Theta(eps^{-2}log(eps^2 n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover |x|_2 +/- eps|x|_1. For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of l1/l1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems