A disztribúció kiszervezéséhez kapcsolódó ellátási és működési kockázatok (Supply and operational risks in the outsourcing of distribution management)

A cikk célja, hogy elemző bemutatását adja az ellátási láncok működéséhez, különösen a disztribúciós tevékenység kiszervezéséhez kapcsolódó működési kockázatoknak. Az írás első része az irodalomkutatás eredményeit feldolgozva az ellátási láncok kockázati kitettségének növekedése mögött rejlő okokat törekszik feltárni, s röviden bemutatja a vállalati kockázatkezelés lehetséges lépéseit e téren. A cikk második gondolati egysége mélyinterjúk segítségével összefoglalja és rendszerezi a disztribúció kiszervezéséhez kapcsolódó kockázatokat, számba veszi a kapcsolódó kockázatkezelési lehetőségeket, s bemutatja a megkérdezett vállalatok által alkalmazott kockázat-megelőzési alternatívákat. ______ The aim of this paper is to introduce operational risks of supply chains, especially risks deriving from the outsourcing of distribution management. Based on literature review the first part of the paper talks about the potential reasons of increasing global supply chain risks, and the general business activities of risk assessment. Analyzing the results of semi-structured qualitative interviews, the second part summarizes the risks belonging to the outsourcing of distribution and introduces the potential risk assessment and avoidance opportunities and alternatives in practice.

Electronic Channels of Distribution: Challenges and Solutions for Hotel Operators

This paper addresses the issues of hotel operators identifying effective means of allocating rooms through various electronic channels of distribution. Relying upon the theory of coercive isomorphism, a think tank was constructed to identify and define electronic channels of distribution currently being utilized in the hotel industry. Through two full-day focus groups consisting of key hotel executives and industry practitioners, distribution channels were identified as were challenges and solutions associated with each.

Strontium isotope analysis of seawater and at a mixture of planktic foraminifera during the Paleocene and Early Eocene from different holes of ODP Leg 2008

We refined the strontium isotope seawater curve for the Paleocene and early Eocene by analysis of samples recovered from the Walvis Ridge during Ocean Drilling Project (ODP) Leg 208. The highest 87Sr/86Sr values occurred in the earliest Paleocene at 65 Ma and generally decreased throughout the Paleocene, reaching minimum values between 53 and 51 Ma in the early Eocene before beginning to increase again at 50 Ma. A plausible explanation for the 87Sr/86Sr decrease between 65 and 51 Ma is increased rates of hydrothermal activity and/or the eruption and weathering of large igneous provinces (e.g., Deccan Traps and North Atlantic). Strontium isotope variations closely parallel sea level and benthic d18O changes during the late Paleocene and early Eocene, supporting previous studies linking tectonic reorganization and increased volcanism to high sea level, high CO2, and warm global temperatures.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Segregation predictions for a binary mixture of granular material under flow conditions in a continuum mechanics framework

Abstract not available

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

The Effects of Instrumental Touching on Infant Pain Perception and the Effects of Eutectic Mixture of Local Anesthetics (EMLA) on the Reduction of Pain

Background: Premature infants, who have to spend the first week of their lives in neonatal intensive care units (NICUs), experience pain and stress in numerous cases, and they are exposed to many invasive interventions. The studies have shown that uncontrolled pain experienced during early life has negative and long-term side effects, such as distress, and such experiences negatively affect the development of the central nervous system Objectives: The purpose of the study was to examine the effects of touching on infant pain perception and the effects of eutectic mixture of local anesthetic (EMLA) on the reduction of pain. Patients and Methods: Data for the study were collected between March and August 2012 from the neonatal clinic of a university hospital located in eastern Turkey. The population of the study consisted of premature infants who were undergoing treatment, completed the first month and who were approved for Hepatitis B vaccine. The study consisted of two experimental groups and one control group. Information forms, intervention follow-up forms, and Premature Infant Pain Profile (PIPP) were used to collect the data. EMLA cream was applied on the vastus lateralis muscles of the first experimental group before the vaccination. The second experimental group was vaccinated by imitation (placebo), without a needle tip or medicine. Vaccination was carried out using instrumental touch in this group. A routine vaccination was applied in the control group. Results: Mean pain scores of the group to which EMLA was applied were lower in a statistically significant way (P < 0.05) compared to the pain scores of the other groups. Moreover, it was determined that even though invasive intervention was not applied to the newborns, the touching caused them to feel pain just as in the placebo group (P < 0.005). Conclusions: The results demonstrated that EMLA was an effective method for reducing pain in premature newborns, and the use of instrumental touch for invasive intervention stimulated the pain perception in the newborns.

Microstructure and hardness characterisation of laser coatings produced with a mixture of AISI 420 stainless steel and Fe-C-Cr-Nb-B-Mo steel alloy powders

Fe-C-Cr-Nb-B-Mo alloy powder and AISI 420 SS powder are deposited using laser cladding to increase the hardness for wear resistant applications. Mixtures from 0 to 100 wt.% were evaluated to understand the effect on the elemental composition, microstructure, phases, and microhardness. The mixture of carbon, boron and niobium in the Fe-C-Cr-Nb-B-Mo alloy powder introduces complex carbides into a Fe-based matrix of AISI 420 SS which increases its hardness. Hardness increased linearly with increasing Fe-C-Cr-Nb-B-Mo alloy, but substantial micro-cracking was observed in the clad layer at additions of 60 wt.% and above; related to a transition from a hypoeutectic alloy containing α-Fe/α' dendrites with an (Fe,Cr)2B and γ-Fe eutectic to primary and continuous carbo-borides M2B (where M represents Fe and Cr) and M23(B,C)6 carbides (where M represents Fe, Cr, Mo) with MC particles (where M represents Nb and Mo). The highest average hardness, for an alloy without micro-cracking, of 952 HV was observed in a 40 wt.% alloy. High stress abrasive scratch testing was conducted on all alloys at various loads (500, 1500, 2500 N). Alloy content was found to have a strong effect on the wear mode and the abrasive wear rate, and the presence of micro-cracks was detrimental to abrasive wear resistance.

Personality and innate immune defenses in a wild bird: evidence for the pace-of-life hypothesis

We tested the two main evolutionary hypotheses for an association between immunity and personality. The risk-of-parasitism hypothesis predicts that more proactive (bold, exploratory, risk-taking) individuals have more vigorous immune defenses because of increased risk of parasite exposure. In contrast, the pace-of-life hypothesis argues that proactive behavioral styles are associated with shorter lifespans and reduced investment in immune function. Mechanistically, associations between immunity and personality can arise because personality differences are often associated with differences in condition and stress responsiveness, both of which are intricately linked with immunity. Here we investigate the association between personality (measured as proactive exploration of a novel environment) and three indices of innate immune function (the non-specific first line of defense against parasites) in wild superb fairy-wrens Malurus cyaneus. We also quantified body condition, hemoparasites (none detected), chronic stress (heterophil:lymphocyte ratio) and circulating corticosterone levels at the end of the behavioral test (CORT, in a subset of birds). We found that fast explorers had lower titers of natural antibodies. This result is consistent with the pace-of-life hypothesis, and with the previously documented higher mortality of fast explorers in this species. There was no interactive effect of exploration score and duration in captivity on immune indices. This suggests that personality-related differences in stress responsiveness did not underlie differences in immunity, even though behavioral style did modulate the effect of captivity on CORT. Taken together these results suggest reduced constitutive investment in innate immune function in more proactive individuals.

Assessing the contribution of microgrids to the reliability of distribution networks

The emergence of microgeneration has recently lead to the concept of microgrid, a network of LV consumers and producers able to export electric energy in some circumstances and also to work in an isolated way in emergency situations. Research on the organization of microgrids, control devices, functionalities and other technical aspects is presently being carried out, in order to establish a consistent technical framework to support the concept. The successful development of the microgrid concept implies the definition of a suitable regulation for its integration on distribution systems. In order to define such a regulation, the identification of costs and benefits that microgrids may bring is a crucial task. Actually, this is the basis for a discussion about the way global costs could be divided among the different agents that benefit from the development of microgrids. Among other aspects, the effect of microgrids on the reliability of the distribution network has been pointed out as an important advantage, due to the ability of isolated operation in emergency situations. This paper identifies the situations where the existence of a microgrid may reduce the interruption rate and duration and thus improve the reliability indices of the distribution network. The relevant expressions necessary to quantify the reliability are presented. An illustrative example is included, where the global influence of the microgrid in the reliability is commented.