Set Of Customers Research Articles

КУЛЬТУРА У ВИВЧЕННІ ІНОЗЕМНИХ МОВ

Language is the bearer of human thoughts and reasoning. Culture should become an integral part of teaching and learning foreign languages. Both society and its culture change over time. The language of this culture is being invigorated by new elements. Effective integration will help the teacher professionally present the content of his lesson by selecting appropriate cultural tools that will facilitate teaching and learning.Language and culture are inseparable, so any change in language threatens culture. Because language is a means of preserving the culture of the people, promoting social interaction and unity of both. Use of language means the transfer of people’s culture. Language expresses, preserves, and transmits the entire set of patterns, behaviors, beliefs, traditions, and customs of the thinking patterns of one group of people different from another.It has been repeatedly found that many students who have studied Russian and have visited Russia have differed significantly from those who did not have the opportunity. A foreign language learner also learns the cultural knowledge and skills necessary to be competent in learning a foreign language. Therefore, it is considered necessary to include culture in a foreign language curriculum, as this helps to avoid the stereotypical notion that language is not part of culture.Teaching culture creates awareness of the geographical environment, the historical or political development of a foreign culture, its customs and the literary achievements of its members. The task of the teacher is to stimulate students’ interest in the target culture and to promote the creation of a foreign language class. Love for one’s language is an aspect of cultural consciousness. Everything that a person thinks is expressed in language and embodied in our lives. The main purpose of teaching culture in a foreign language class is to raise students’ awareness and develop their interest in the target culture on their own.

Rice cultivar diversity, associated indigenous knowledge and management practices in a lowland village landscape from north-eastern India

The study of the diversity of cultivated plant populations maintained by farmers is of great importance for evolutionary and conservation biology. In the present study, we assessed the landrace diversity of rice grown in a part of Manipur, India with reference to socio-economic characteristics of the households and their knowledge systems. Over the period 2009–2011 information was collected following the participatory rural appraisal methodology including transect walks, personal interviews, key informant interviews and group discussions. In all 14 cultivars were grown by the sampled households during the period and most varieties had some meaning in local language. Farmers distinguished cultivars in terms of the colour of grain/husk, stickiness of cooked grains, food quality after puffing and boiling, length of maturity period and responses to pests, pathogens, fertilizers and hydrological conditions. Relative cropped area of different cultivars varied during the study period. The local people followed a set of social customs for the management of the seed resources. None of the farmers listed any consistent trend of climate change or change in yields with time. Increasing stress on off-farm economy, lack of recognition to the socio-cultural capital in agricultural/rural development programmes and lack of specific policies providing incentive for on-farm conservation are causing depletion of agrobiodiversity.

Behavior Based Price Personalization Under Vertical Product Dfferentiation

We study price personalization in a two period duopoly with vertically differentiated products. In the second period a firm knows the purchase history of all customers, as in the standard Behavior Based Price Discrimination models. However in the second period it also has detailed personal information on its own customers, enabling it to quote personalized prices. The analysis reveals that there exists a natural market (nm) for each firm, defined as the set of customers that cannot be poached by the rival in period two. Since in equilibrium all contestable consumers belong to the largest nm, poaching will only be one way. The firm with the largest nm, has highest profits, but not necessarily the largest market share. All consumers gain from price personalization..Profits are lower than under uniform pricing. Quality choice is well defined for the low quality and a quality dfferential arises, though the exact choice for the high quality depends upon the cost specification.

THE SUBJECT FIELD OF INSTITUTIONAL EDUCATION: THE SENSE AND PROSPECTS OF DEVELOPMENT IN UKRAINE

The development of modern civilization attests to its decisive role in the progressive development of institutions. They identified the difference between Western civilization and the rest of the world. Confirmation of the institutional advantages of the West was its early industrialization. The genesis and formation of institutionalism in its ideological and conceptualmethodological orientation occurs as a process alternative to neoclassic in the context of world heterodoxia, which quickly spread in social science. Highlighting institutional education as a separate area of sociocultural activity is determined by the factor of differentiation of institutional theory as a whole. A feature of institutional education is its orientation toward the individual and his/her transformation into a personality. The content of institutional education is revealed through the analysis of the institution, which includes a set of established customs, traditions, ways of thinking, behavioral stereotypes of individuals and social groups. The dynamics of socio-political, economic transformations in Ukraine requires a review of the foundations of national education and determination of the prospects for its development in the 21st century in the context of institutionalism.

On the different survival rates of Portuguese microbusinesses – the case of projects supported by microcredit

ABSTRACT In spite of their importance to the micro-entrepreneur’s life, Portuguese microbusinesses exhibit a short duration in activity. However, until now there has been scarce attention about the determinants characterizing the microbusiness and/or the micro-entrepreneur. This paper is based on a very comprehensive survey distributed to the microcredit borrowers in Portugal. The main empirical findings suggest that more mature entrepreneurs, with family support, a permanent set of customers, and an awareness of the quality of their goods and services exhibit lower hazard rates of finishing the business. Conversely, high-fixed costs, bad business locations, and periods of economic crisis significantly reduce the duration of the business.

The Plugin Hybrid Electric Vehicle routing problem: A power-management strategy model

This paper presents the Plugin Hybrid Electric Vehicle (PHEV) routing problem that finds the optimal set and sequence of customers visited by PHEVs to minimize total energy consumption. PHEVs use electricity and gasoline as their two energy sources. A power management model finds the optimal draw of power from the two sources along the vehicle’s path. To solve the PHEVRP, we present an exact branch-and-price and a heuristic algorithm. We derive the complexity order of the algorithms and show that the heuristic becomes faster at larger battery capacities. We present a case study situated in the City of Toronto and show that the PHEVs use electricity in congested downtown regions and gasoline in free-flow conditions of highways.

An arc flow formulation to the multitrip production, inventory, distribution, and routing problem with time windows

AbstractThe multitrip production, inventory, distribution, and routing problem with time windows (MPIDRPTW) is an integrated problem that combines a production and distribution problem, a multitrip vehicle routing problem, and an inventory routing problem. In the MPIDRPTW, a set of customers, which have a time‐varying demand during a finite planning horizon, is served by a single production facility. The distribution is accomplished by a fleet of homogeneous vehicles that deliver the customer orders within their specific time windows. Production management has to be done according to the inventories at the facility and at the customers. An exact arc flow model based on a graph is proposed to solve the MPIDRPTW, where the nodes represent instants of time. The main goal of the problem is to minimize the costs associated with the entire system. The proposed approach was implemented and a set of experimental tests were conducted based on a set of adapted instances from the literature.

Directions of securing and regulating the food security of the Black Sea region

The paper suggests the measures stimulating the interregional cooperation in the Black Sea region by expanding the scope of intra-industrial and cooperation ties to effectively use the agro-industrial capacity of the state to ensure food security. The main purpose of the regional mechanism of increasing the food security level should be to establish stable regional food production and distribution chains by ensuring efficient use of the agro-industrial capacity of the region and increasing the purchasing power of the population to ensure access to quality food. The paper suggests: 1. State regulation of food security of the region in the international food trade to be carried out in the following areas: ensuring the protection of economic interests of agricultural and food producers through a set of customs and non-tariff measures in accordance with Ukraine's international obligations; development of import substitution measures to guarantee the food independence of the region. 2. State regulation of food security of the region in the economic domain to be carried out on the basis of: implementation of the financial and lending policy of support for producers of agricultural products and foodstuffs; development of the regional food market with improved logistics and infrastructure support and establishment of interregional trade relations; implementation of food pricing policy and control over its implementation. 3. State regulation of food security of the region in the social domain to be carried out on the basis of: ensuring the availability of quality and sufficient food for every inhabitant of the region; increasing the level of effective demand for food by raising living standards; monitoring the quality and safety of food; ensuring the state food reserve to ensure the stability of food supply and regulation of supply and demand. 4. State regulation of food security of the region in interaction with the scientific and educational sphere to be carried out on the basis of: economic support for the development of innovations in the agro-industrial complex of the region; stimulating the introduction of innovative scientific and technical developments by producers of agricultural products and food products; ensuring an increase in the level of education of participants in the agro-industrial complex.

Solving a Periodic Capacitated Vehicle Routing Problem using Simulated Annealing Algorithm for a Manufacturing Company

Goal: This paper aims to implement a periodic capacitated vehicle routing problem with simulated annealing algorithm using a real-life industrial distribution problem and to recommend it to industry practitioners. The authors aimed to achieve high-performance solutions by coding a manually solved industrial problem and thus solving a real-life vehicle routing problem using Julia language and simulated annealing algorithm.
 Design / Methodology / Approach: The vehicle routing problem (VRP) that is a widely studied combinatorial optimization and integer programming problem, aims to design optimal tours for a fleet of vehicles serving a given set of customers at different locations. The simulated annealing algorithm is used for periodic capacitated vehicle routing problem. Julia is a state-of-art scientific computation language. Therefore, a Julia programming language toolbox developed for logistic optimization is used.
 Results: The results are compared to savings algorithms from Matlab in terms of solution quality and time. It is seen that the simulated annealing algorithm with Julia gives better solution quality in reasonable simulation time compared to the constructive savings algorithm.
 Limitations of the investigation: The data of the company is obtained from 12 periods with a history of four years. About the capacitated vehicle routing problem, the homogenous fleet with 3000 meters/vehicle is used. Then, the simulated annealing design parameters are chosen rule-of-thumb. Therefore, better performance can be obtained by optimizing the simulated annealing parameters.
 Practical implications: In this study, a furniture roving parts manufacturing company that have 30 customers in Denizli, an industrial city in the west part of Turkey, is investigated. Before the scheduling implementation with Julia, the company has no effective and efficient planning as they have been using spreadsheet programs for vehicle scheduling solutions. In this study, the solutions with Julia are used in practice for the distribution with higher utilization rate and minimum number of vehicles. The simulated annealing and savings algorithms are compared in terms of solution time and performance. The savings algorithm has produced better solution time, the simulated annealing approach has minimum total distance objective value, minimum number of required vehicles, and maximum vehicle utilization rate for the whole model. Thus, this paper can contribute to small scale business management in the sense of presenting a digitalization solution for the vehicle scheduling solution. Also, Julia application of simulated annealing for vehicle scheduling is demonstrated that can help both academicians and practitioners in organizations, mainly in logistics and distribution problems.
 Originality / Value: The main contribution of this study is a new solution method to capacitated vehicle routing problems for a real-life industrial problem using the advantages of the high-level computing language Julia and a meta-heuristic algorithm, the simulated annealing method.
 Keywords: Capacitated vehicle routing problem, Simulated annealing algorithm, Julia programming language.

Tackling the Multi-Objective Vehicle Routing Problem With Uncertain Demands

In this article, the single capacitated vehicle routing problem with time windows and uncertain demands is studied. Having a set of customers whose actual demand is not known in advance, needs to be serviced. The goal of the problem is to find a set of routes with the lowest total travel distance and tardiness time, subject to vehicle capacity and time window constraints. Two uncertainty types can be distinguished in the literature: random and epistemic uncertainties. Because several studies focalized upon the random aspect of uncertainty, the article proposes to tackle the problem by considering dominance relations to handle epistemic uncertainty in the objective functions. Further, an epistemic multi-objective local search-based approach is proposed for studying the behavior of such a representation of demands on benchmark instances generated following a standard generator available in the literature. Finally, the results achieved by the proposed method using epistemic representation are compared to those reached by a deterministic version. Encouraging results have been obtained.

A Hybrid Swarm Intelligence Algorithm for Vehicle Routing Problem With Time Windows

The Vehicle Routing Problem with Time Windows (VRPTW) has drawn considerable attention in the last decades. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within capacity and time window constraints. As a combinatorial optimization problem, VRPTW is proved NP-hard and is best solved by heuristics. In this paper, a hybrid swarm intelligence algorithm by hybridizing Ant Colony System (ACS) and Brain Storm Optimization (BSO) algorithm is proposed, to solve VRPTW with the objective of minimizing the total distance. In the BSO procedure, both inter-route and intra-route improvement heuristics are introduced. Experiments are conducted on Solomon's 56 instances with 100 customers benchmark, the results show that 42 out of 56 optimal solutions (18 best and 24 competitive solutions) are obtained, which illustrates the effectiveness of the proposed algorithm.

Алгоритм машинного обучения для решения задачи формирования рекомендаций товаров и услуг

The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation system. Methods based on collaborative filtering and content-based filtering are considered. A combined algorithm for identifying relationships on sets has been developed, which combines the advantages of the analyzed approaches. The complexity of the algorithm is estimated. Recommendations are given on the efficient implementation of the algorithm in order to reduce the amount of memory used. Using the book recommendation problem as an example, the application of this combined algorithm is shown. This algorithm can be used for a “cold start” of a recommender system, when there are no labeled quality samples of training more complex models.

Metaheuristic for the Capacitated Multiple Traveling Repairman Problem

The Capacitated Multiple Traveling Repairmen Problem (CmTRP) is an extension of the Multiple Traveling Repairmen Problem (mTRP). In the CmTRP, the number of vehicles is dispatched to serve a set of customers, while each vehicle’s capacity is limited by a predefined-value as well as each customer is visited exactly once. The goal is to find a tour that minimizes the sum of waiting times. The problem is NP-hard because it is harder than the mTRP. Even finding a feasible solution is also NP-hard problem. To solve medium and large size instances, a metaheuristic algorithm is proposed. The first phase constructs a feasible solution by combining between the Nearest Neighborhood Search (NNS) and Variable Neighborhood Search (VNS), while the optimization phase develops the feasible solution by the General Variable Neighborhood Search (GVNS). The combination maintains the balance between intensification and diversification to escape local optima. The proposed algorithm is implemented on benchmark instances from the literature. The results indicate that the developed algorithm obtains good feasible solutions in a short time, even for the cases with up to 200 vertices.

Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem

We consider the problem of routing a team of unmanned aerial vehicles (drones) being used to take surveillance observations of target locations, where the value of information at each location is different and not all locations need be visited. As a result, this problem can be described as a stochastic team orienteering problem (STOP), in which travel times are modelled as random variables following generic probability distributions. The orienteering problem is a vehicle-routing problem in which each of a set of customers can be visited either just once or not at all within a limited time period. In order to solve this STOP, a simheuristic algorithm based on an original and fast heuristic is developed. This heuristic is then extended into a variable neighbourhood search (VNS) metaheuristic. Finally, simulation is incorporated into the VNS framework to transform it into a simheuristic algorithm, which is then employed to solve the STOP. [Received 5 January 2019; Revised 15 June 2019; Accepted 13 October 2019]

Implementation of ATLAS Distributed Computing monitoring dashboards using InfluxDB and Grafana

For the last 10 years, the ATLAS Distributed Computing project has based its monitoring infrastructure on a set of custom designed dashboards provided by CERN. This system functioned very well for LHC Runs 1 and 2, but its maintenance has progressively become more difficult and the conditions for Run 3, starting in 2021, will be even more demanding; hence a more standard code base and more automatic operations are needed. A new infrastructure has been provided by CERN, based on InfluxDB as the data store and Grafana as the display environment. ATLAS has adapted and further developed its monitoring tools to use this infrastructure for data and workflow management monitoring and accounting dashboards, expanding the range of previous possibilities with the aim to achieve a single, simpler, environment for all monitoring applications. This document describes these tools and the data flows for monitoring and accounting.

Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection

Recent years have shown a noticeable rise in the number of incidents with drones, related to both civilian and military installations. While drone neutralization techniques have become increasingly effective, detection most often relies on professional equipment, which is too expensive to be used for all critical nodes and applications. Therefore, there is a need for drone detection systems that could work on low performance hardware. Its critical component consists of an object detection system. In this article, we introduce a new object detection dataset, built entirely to train computer vision based object detection machine learning algorithms for a task of binary object detection to enable automated, industrial camera based detection of multiple drone objects using camera feed. The dataset expands existing multiclass image classification and object detection datasets (ImageNet, MS-COCO, PASCAL VOC, anti-UAV) with a diversified dataset of drone images. In order to maximize the effectiveness of the model, real world footage was utilized, transformed into images and hand-labelled to create a custom set of 56821 images and 55539 bounding boxes. Additionally, semi-automated labelling was proposed, tested and proved to be very useful for object detection applications. The dataset was divided into train and test subsets for further processing and used to generate 603 easily deployable Haar Cascades as well as 819 high performing Deep Neural Networks based models. They were used to test different object detection methods to determine the long term feasibility of a large scale drone detection system utilizing machine learning algorithms. The study has shown that Haar Cascade can be used as the Minimum Viable Product model for mediocre performance but fails to scale up effectively for a larger dataset compared to the Deep Neural Network model.

Market Segmentation Customer Satisfaction-Product Service Management

It is very relevant for a better management to calculate some satisfied index of consumers of a particular market, and of a consumer service. To achieve this, we add study of PCA, factor and regression to our data points and then by normalization we create a best suited model. Eventually, the model explains the effectiveness of a given set of customers ' happy list. All these measurements are given in a systematic order to measure happy index, including the source code.

Two echelon vehicle routing problem with drones in last mile delivery

In recent years, drone routing and scheduling has become a highly active area of research. This research introduces a new routing model that considers a synchronized truck-drone operation by allowing multiple drones to fly from a truck, serve one or multiple customers, and return to the same truck for a battery swap and package retrieval. The model addresses two levels (echelons) of delivery: primary truck routing from the main depot to serve assigned customers and secondary drone routing from the truck, which behaves like a moveable intermediate depot to serve other sets of customers. The model takes into account both trucks' and drones’ capacities with the objective of finding optimal routes of both trucks and drones that minimizes the total arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by formulated mixed integer programming (MIP) for the small-size problem, and two efficient heuristic algorithms are designed to solve the large-size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS). Numeric results from the experiment compare the performance of both heuristics against the MIP method in small/medium-size instances from the literature. A sensitivity analysis is conducted to show the delivery time improvement of the proposed model over the previous truck-drone routing models.

All-Cause and Cause-Specific Mortality Among People Using Extramedical Opioids

Extramedical opioid use has escalated in recent years. A better understanding of cause-specific mortality in this population is needed to inform comprehensive responses. To estimate all-cause and cause-specific crude mortality rates (CMRs) and standardized mortality ratios (SMRs) among people using extramedical opioids, including age- and sex-specific estimates when possible. For this systematic review and meta-analysis, MEDLINE, PsycINFO, and Embase were searched for studies published from January 1, 2009, to October 3, 2019, and an earlier systematic review on this topic published in 2011. Cohort studies of people using extramedical opioids and reporting mortality outcomes were screened for inclusion independently by 2 team members. Data were extracted by a team member and checked by another team member. Study quality was assessed using a custom set of items that examined risk of bias and quality of reporting. Data were pooled using random-effects meta-analysis models. Heterogeneity was assessed using stratified meta-analyses and meta-regression. Outcome measures were all-cause and cause-specific CMRs and SMRs among people using extramedical opioids compared with the general population of the same age and sex. Of 8683 identified studies, 124 were included in this analysis (100 primary studies and 24 studies providing additional data for primary studies). The pooled all-cause CMR, based on 99 cohorts of 1 262 592 people, was 1.6 per 100 person-years (95% CI, 1.4-1.8 per 100 person-years), with substantial heterogeneity (I2 = 99.7%). Heterogeneity was associated with the proportion of the study sample that injected opioids or was living with HIV infection or hepatitis C. The pooled all-cause SMR, based on 43 cohorts, was 10.0 (95% CI, 7.6-13.2). Excess mortality was observed across a range of causes, including overdose, injuries, and infectious and noncommunicable diseases. The findings suggest that people using extramedical opioids experience significant excess mortality, much of which is preventable. The range of causes for which excess mortality was observed highlights the multiplicity of risk exposures experienced by this population and the need for comprehensive responses to address these. Better data on cause-specific mortality in this population in several world regions appear to be needed.

Integrated planning of inbound and outbound logistics with a Rich Vehicle Routing Problem with backhauls

This paper addresses the integration of the planning decisions concerning inbound logistics in an industrial setting (from the suppliers to the mill) and outbound logistics (from the mill to customers). The goal is to find the minimum cost routing plan, which includes the cost-effective outbound and inbound daily routes (OIRs), consisting of a sequence of deliveries of customer orders, pickup of a full truck-load at a supplier, and its delivery to the mill. This study distinguishes between three planning strategies: opportunistic backhauling planning (OBP), integrated inbound and outbound planning (IIOP) and decoupled planning (DIOP), the latter being the commonly used, particularly in the case of the wood-based panel industry under study. From the point of view of process integration, OBP can be considered as an intermediate stage from DIOP to IIOP. The problem is modelled as a Vehicle Routing Problem with Backhauls, enriched with case-specific rules for visiting the backhaul, split deliveries to customers and the use of a heterogeneous fleet. A new fix-and-optimise matheuristic is proposed for this problem, seeking to obtain good quality solutions within a reasonable computational time. The results from its application to the wood-based panel industry in Portugal show that IIOP can help to reduce total costs in about 2.7%, when compared with DIOP, due to better use of the delivery truck and a reduction of the number of dedicated inbound routes. Regarding OBP, fostering the use of OIRs does not necessarily lead to better routing plans than DIOP, as it depends upon a favourable geographical configuration of the set of customers to be visited in a day, specifically, the relative distance between a linehaul that can be visited last in a route, a neighboring backhaul, and a mill. The paper further provides valuable managerial insights on how the routing plan is impacted by the values of business-related model parameters which are set by the planner with some degree of uncertainty. Results suggest that increasing the maximum length of the route will likely have the largest impact in reducing transportation costs. Moreover, increasing the value of a reward paid for visiting a backhaul can foster the percentage of OIR in the optimal routing plan.