Objectives Of Service Providers Research Articles

Two-echelon multi-commodity multimodal vehicle routing problem considering user heterogeneity in city logistics

The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.

A Study on ‘Why Outsourcing in Health Care’ by Friedman Two-way Analysis of Variance Method

Outsourcing is not new and is growing in consonance with advancement of technical innovations, speed of digital communications, transformation and changes in the business environment and with that of the Gross Domestic Product (GDP) of world as whole. It is being used by the business mangers as more of a strategic weapon to garner the competitive advantage and mitigate the competitive forces of the business. While outsourcing has many success stories, when used properly, with an objective of service providers, as partner in progress, is not free from negative consequence. Hence, understanding that outsourcing help the business leaders, the practicing owners, practicing administrators, decision makers in health care sector those who battle on a day–to–day basis to effectively manage the available scare resources, by making a conscious approach, while deciding in favor of outsourcing. An attempt has been made in this research to test the reasons in favor of outsourcing in health care sector.

Quality of service (QoS) parameters prediction for web services using hybrid neural network and ensemble methods

Web services are an essential part of the everyday operation for a number of organizations. Applications within organizations frequently depend on web services to fulfill customers needs. Failed and slow online application depending on web services can cost a lot to organizations in terms of customer dissatisfaction. Estimation and improvement of the quality of service (QoS) parameters of web service serve as the primary objective of service providers. We hypothesize that analyzing the source code behind the web services helps in computing the QoS parameters. In this paper, we analyze the source code using thirty-seven different source code metrics (SCMs) to measure the internal structure of the software system. In order to develop a model, these SCMs are treated as input for predicting QoS parameters using nine different prediction techniques and three different ensemble methods. Since SCM act as an important parameter for assessing the performance of prediction techniques, hence for dimensionality reduction and removing irrelevant features, four distinct feature reduction techniques are considered in the study during implementation. Two distinct evaluation criteria such as MMRE and RMSE, are considered for evaluating and comparing the performance of the developed QoS parameters prediction models. The experimental results reveal that the weighted-based ensemble method produces better results. The results also depict that the ME, Ca, IC, DAM, and MFA metrics are commonly found relevant metrics for QoS parameters prediction. Furthermore, we also observe that the selected set of metrics achieves better results compared to all metrics.

Window Based State Monitoring in Cloud Datacenter using VM Concept

Abstract Cloud Computing provides solution to licensing, distribution, configuration, and operation of enterprise application associated with the traditional IT infrastructure. Thus to define Quality of Service (QoS) clients and service providers need to establish a Service Level Agreement (SLA). The main objective of service providers is to improve Customer Satisfaction Level (CSL). In this paper, we present a method to minimize the number of reports generated by adapting the method of Window based State Monitoring framework and thereby reporting only the continuous state violations. In previous works, the issue of SLA violation has not received thorough analysis. In this work, we will keep track of the violation in processing a user request and the CPU utilization during the completion of the task. This will play a major role in avoiding the performance degradation encountered by a VM. Another major contribution of this paper is committed to detailed analysis of the state of Cloud environments and datacenter resources with relevant energy consumption during the processing time of a user request.

Solving Complex Logistics Problems with Multi-Artificial Intelligent System

The economy, which has become more information intensive, more global and more technologically dependent, is undergoing dramatic changes. The role of logistics is also becoming more and more important. In logistics, the objective of service providers is to fulfill all customers' demands while adapting to the dynamic changes of logistics networks so as to achieve a higher degree of customer satisfaction and therefore a higher return on investment. In order to provide high quality service, knowledge and information sharing among departments becomes a must in this fast changing market environment. In particular, artificial intelligence (AI) technologies have achieved significant attention for enhancing the agility of supply chain management, as well as logistics operations. In this research, a multi-artificial intelligence system, named Integrated Intelligent Logistics System (IILS) is proposed. The objective of IILS is to provide quality logistics solutions to achieve high levels of service performance in the logistics industry. The new feature of this agile intelligence system is characterized by the incorporation of intelligence modules through the capabilities of the case-based reasoning, multi-agent, fuzzy logic and artificial neural networks, achieving the optimization of the performance of organizations.