Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

This paper takes into account the continuous-review reorder point-lot size (r,Q) inventory model under stochastic demand, with the backorders-lost sales mixture. Moreover, to reflect the practical circumstance in which full information about the demand distribution lacks, we assume that only an estimate of the mean and of the variance is available. Contrarily to the typical approach in which the lead-time demand is supposed Gaussian or is obtained according to the so-called minimax procedure, we take a different perspective. That is, we adopt the maximum entropy principle to model the lead-time demand distribution. In particular, we consider the density that maximizes the entropy over all distributions with given mean and variance. With the aim of minimizing the expected total cost per time unit, we then propose an exact algorithm and a heuristic procedure. The heuristic method exploits an approximated expression of the total cost function achieved by means of an ad hoc first-order Taylor polynomial. We finally carry out numerical experiments with a twofold objective. On the one hand we examine the efficiency of the approximated solution procedure. On the other hand we investigate the performance of the maximum entropy principle in approximating the true lead-time demand distribution

Evacuation model based on game theory - simulation and design

In order to improve the safety during emergency situations that may happen in public or working places, there is the necessity of a better evacuation systems design. Since it is not sufficient for evaluating the goodness of an evacuation process only to keep to codes and handbooks’ rules, it is necessary the use of additional tools in order to achieve a more realistic evacuation calculation. A very powerful method is that based on computerized evacuation simulations. In this thesis an evacuation simulation model based on game theory is presented. This model involves several parameters and aspects attempting to obtain a satisfactory representation of the reality. Moreover, a simulative study to find the best emergency exits positioning, - in terms of minimum egress time -, among different possible locations into a generic layout has been done

An ensemble-learning model for failure rate prediction

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Efficient near-optimal procedures for some inventory models with backorders-lost sales mixture and controllable lead time, under continuous or periodic review

This paper considers a number of inventory models with backorders-lost sales mixture, stockout costs, and controllable lead time. The lead time is a linear function of the lot size and includes a constant term that is made of several components. These lot-size-independent components are assumed to be controllable. Both single- and double-echelon inventory systems, under periodic or continuous review, are considered. To authors knowledge, these models have never been previously studied in literature. The purpose of this paper is to analyse and optimize these novel inventory models. The optimization is carried out by means of heuristics that work on an ad hoc approximation of the cost functions. This peculiarity permits to exploit closed-form expressions that make the optimization procedure simpler and more readily applicable in practice than standard approaches. Finally, numerical experiments investigate the efficiency of the proposed heuristics and the sensitivity of the developed models

Optimising replenishment policy in an integrated supply chain with controllable lead time and backorders-lost sales mixture

This paper aims to optimize the inventory replenishment policy in an integrated supply chain consisting of a single supplier and a single buyer. The system under consideration has the features such as backorders-lost sales mixture, controllable lead time, stochastic demand, and stockout costs. The underlying problem has not been studied in the literature. We present a novel approach to formulate the optimization problem, which is able to satisfy the constraint on the number of admissible stockouts per time unit. To solve the optimization problem, we propose two algorithms: an exact algorithm and a heuristic algorithm. These two algorithms are developed based on some analytical properties that we established by analysing the cost function in relation to the decision variables. The heuristic algorithm employs an approximation technique based on an ad-hoc Taylor series expansion. Extensive numerical experiments are provided to demonstrate the effectiveness of the proposed algorithms

A periodic review policy with quality improvement, setup cost reduction, backorder price discount, and controllable lead time

This paper explores a periodic review inventory model under stochastic demand. The setup (or ordering) cost and the lead time are controllable. The model considers an imperfect production process, whose quality can be improved by means of an investment. A backorder price discount to motivate customers to wait for backorders is included. The demand in the protection interval is first assumed Gaussian; then, the distribution-free approach is adopted. The objective is to determine the review period, the setup cost, the quality level, the backorder price discount, and the length of lead time that minimize the long-run expected total cost per time unit. A solution method for each case is presented. Numerical experiments show that substantial savings can be achieved if the quality level, the setup cost and the lead time are controlled, and if a backorder price discount is applied. A sensitivity analysis is finally carried out

Extracellular Vesicles Derived from Endothelial Progenitor Cells Protect Human Glomerular Endothelial Cells and Podocytes from Complement- and Cytokine-Mediated Injury

Glomerulonephritis are renal inflammatory processes characterized by increased permeability of the Glomerular Filtration Barrier (GFB) with consequent hematuria and proteinuria. Glomerular endothelial cells (GEC) and podocytes are part of the GFB and contribute to the maintenance of its structural and functional integrity through the release of paracrine mediators. Activation of the complement cascade and pro-inflammatory cytokines (CK) such as Tumor Necrosis Factor α (TNF-α) and Interleukin-6 (IL-6) can alter GFB function, causing acute glomerular injury and progression toward chronic kidney disease. Endothelial Progenitor Cells (EPC) are bone-marrow-derived hematopoietic stem cells circulating in peripheral blood and able to induce angiogenesis and to repair injured endothelium by releasing paracrine mediators including Extracellular Vesicles (EVs), microparticles involved in intercellular communication by transferring proteins, lipids, and genetic material (mRNA, microRNA, lncRNA) to target cells. We have previously demonstrated that EPC-derived EVs activate an angiogenic program in quiescent endothelial cells and renoprotection in different experimental models. The aim of the present study was to evaluate in vitro the protective effect of EPC-derived EVs on GECs and podocytes cultured in detrimental conditions with CKs (TNF-α/IL-6) and the complement protein C5a. EVs were internalized in both GECs and podocytes mainly through a L-selectin-based mechanism. In GECs, EVs enhanced the formation of capillary-like structures and cell migration by modulating gene expression and inducing the release of growth factors such as VEGF-A and HGF. In the presence of CKs, and C5a, EPC-derived EVs protected GECs from apoptosis by decreasing oxidative stress and prevented leukocyte adhesion by inhibiting the expression of adhesion molecules (ICAM-1, VCAM-1, E-selectin). On podocytes, EVs inhibited apoptosis and prevented nephrin shedding induced by CKs and C5a. In a co-culture model of GECs/podocytes that mimicked GFB, EPC-derived EVs protected cell function and permeselectivity from inflammatory-mediated damage. Moreover, RNase pre-treatment of EVs abrogated their protective effects, suggesting the crucial role of RNA transfer from EVs to damaged glomerular cells. In conclusion, EPC-derived EVs preserved GFB integrity from complement- and cytokine-induced damage, suggesting their potential role as therapeutic agents for drug-resistant glomerulonephritis

A retrospective case series of ultrasound-guided suprascapular nerve pulsed radiofrequency treatment for hemiplegic shoulder pain in patients with chronic stroke

Purpose: Hemiplegic shoulder pain (HSP) is the most common pain condition after stroke. Pulsed radiofrequency (PRF) treatment of the suprascapular nerve (SSN) effectively relieves shoulder pain conditions. To date, there is no study about the effects of PRF treatment for HSP. Thus, our aim was to report on a case series about its use in chronic stroke. Patients and methods: Six chronic stroke patients with HSP (visual analog scale [VAS] score for pain 6530 mm) underwent ultrasound-guided SSN PRF treatment. All were evaluated before treatment and at 4 and 16 weeks of follow-up. The main outcome was VAS score. Secondary outcomes were Modified Ashworth Scale, shoulder passive range of motion (PROM), Disability Assessment Scale (DAS), Fugl-Meyer Assessment, and EuroQol-5 dimension questionnaire (EuroQol-5D) scores. Results: As compared with baseline, improvement was observed in the following parameters: VAS for pain (at 4 weeks, P=0.023; at 16 weeks, P=0.023); shoulder PROM for abduction (at 4 weeks, P=0.023; at 16 weeks, P=0.024), flexion (at 4 and 16 weeks, P=0.024), extension (at 4 and 16 weeks, P=0.02), and external rotation (4 and 16 weeks, P=0.02); DAS for hygiene (at 4 and 16 weeks, P=0.024), dressing (at 4 weeks, P=0.02; at 16 weeks, P=0.024), and pain (at 4 weeks, P=0.024; at 16 weeks, P=0.023); and EuroQol-5D (at 4 and 16 weeks, P=0.024). Conclusion: Our observations support the use of ultrasound-guided SSN PRF treatment for HSP in chronic stroke patients

Local Structure and Magnetism of Fe2O3 Maghemite Nanocrystals: The Role of Crystal Dimension

Here we report on the impact of reducing the crystalline size on the structural and magnetic properties of γ-Fe2O3 maghemite nanoparticles. A set of polycrystalline specimens with crystallite size ranging from ~2 to ~50 nm was obtained combining microwave plasma synthesis and commercial samples. Crystallite size was derived by electron microscopy and synchrotron powder diffraction, which was used also to investigate the crystallographic structure. The local atomic structure was inquired combining pair distribution function (PDF) and X-ray absorption spectroscopy (XAS). PDF revealed that reducing the crystal dimension induces the depletion of the amount of Fe tetrahedral sites. XAS confirmed significant bond distance expansion and a loose Fe-Fe connectivity between octahedral and tetrahedral sites. Molecular dynamics revealed important surface effects, whose implementation in PDF reproduces the first shells of experimental curves. The structural disorder affects the magnetic properties more and more with decreasing the nanoparticle size. In particular, the saturation magnetization reduces, revealing a spin canting effect. Moreover, a large effective magnetic anisotropy is measured at low temperature together with an exchange bias effect, a behavior that we related to the existence of a highly disordered glassy magnetic phase