Computing (R, S) policies with correlated demand

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good

Detecting outliers in multivariate volatility models:A wavelet procedure

It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers

The dynamic bowser routing problem

We investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions on construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the bowser truck as well as fuel shortages. Motivated by a practical case study elicited in the context of a project we recently conducted at Crossrail, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible distance. We investigate deterministic and stochastic variants of this problem and introduce effective and scalable mathematical programming models to tackle these cases. We demonstrate the effectiveness of our approaches in the context of an extensive computational study designed around data collected on site as well as supplied by our project partners. Keywords: Routing; Dynamic Bowser Routing Problem; Stochastic Bowser Routing Problem; Mixed-Integer Linear Programming; Construction

Purification and genetic characterization of gassericin E, a novel co-culture inducible bacteriocin from Lactobacillus gasseri EV1461 isolated from the vagina of a healthy woman

This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Project AGL2012-33400 and AGL2013-41980-P, and by the Junta de Andalucía Excellence Project AGR-07345. These projects included FEDER funds. AMB was the recipient of a post-doctoral grant awarded by the Junta de Andalucía as part of the Project AGR-07345.Background: Lactobacillus gasseri is one of the dominant Lactobacillus species in the vaginal ecosystem. Some strains of this species have a high potential for being used as probiotics in order to maintain vaginal homeostasis, since they may confer colonization resistance against pathogens in the vagina by direct inhibition through production of antimicrobial compounds, as bacteriocins. In this work we have studied bacteriocin production of gassericin E (GasE), a novel bacteriocin produced by L. gasseri EV1461, a strain isolated from the vagina of a healthy woman, and whose production was shown to be promoted by the presence of certain specific bacteria in co-culture. Biochemical and genetic characterization of this novel bacteriocin are addressed. Results: We found that the inhibitory spectrum of L. gasseri EV1461 was broad, being directed to species both related and non-related to the producing strain. Interestingly, L. gasseri EV1461 inhibited the grown of pathogens usually associated with bacterial vaginosis (BV). The antimicrobial activity was due to the production of a novel bacteriocin, gassericin E (GasE). Production of this bacteriocin in broth medium only was achieved at high cell densities. At low cell densities, bacteriocin production ceased and only was restored after the addition of a supernatant from a previous bacteriocin-producing EV1461 culture (autoinduction), or through co-cultivation with several other Gram-positive strains (inducing bacteria). DNA sequence of the GasE locus revealed the presence of two putative operons which could be involved in biosynthesis and immunity of this bacteriocin (gaeAXI), and in regulation, transport and processing (gaePKRTC). The gaePKR encodes a putative three-component regulatory system, involving an autoinducer peptide (GaeP), a histidine protein kinase (GaeK) and a response regulator (GaeR), while the gaeTC encodes for an ABC transporter (GaeT) and their accessory protein (GaeC), involved in transport and processing of the bacteriocin. The gaeAXI, encodes for the bacteriocin gassericin E (GasE), a putative peptide bacteriocin (GaeX), and their immunity protein (GaeI). Conclusions: The origin of the strain (vagina of healthy woman) and its ability to produce bacteriocins with inhibitory activity against vaginal pathogens may be an advantage for using L. gasseri EV1461 as a probiotic strain to fight and/or prevent bacterial infections as bacterial vaginosis (BV), since it could be better adapted to live and compete into the vaginal environment.Publisher PDFPeer reviewe

A simple heuristic for perishable inventory control under nonstationary stochastic demand

In this paper, we study the single-item single-stocking location non-stationary stochastic lot sizing problem for a perishable product. We consider fixed and proportional ordering cost, holding cost and penalty cost. The item features a limited shelf life, therefore we also take into account a variable cost of disposal. We derive exact analytical expressions to determine the expected value of the inventory of different ages. We also discuss a good approximation for the case in which the shelf-life is limited. To tackle this problem, we introduce two new heuristics that extend Silver’s heuristic and compare them to an optimal Stochastic Dynamic Programming policy in the context of a numerical study. Our results demonstrate the effectiveness of our approach

Multi-group support vector machines with measurement costs:A biobjective approach

Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modeled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. We introduce a Biobjective Mixed Integer Problem, for which Pareto optimal solutions are obtained. Those Pareto optimal solutions correspond to different classification rules, among which the user would choose the one yielding the most appropriate compromise between the cost and the expected misclassification rate