What Turkey expects from logistics outsourcing ?

Copyright @ 2011 Yasar UniversityThe economies of the world have become increasingly interdependent, and organizations have come under tremendous pressure to maximize productivity and profitability. Creating value through outsourcing has emerged as a popular competitive strategy for firms of all sizes in all types of industries. The aim of this research is to investigate the use of third party logistics in Turkish companies from the users’ perspective to identify the types of logistics services outsourced, problems encountered in outsourcing these services, logistics costs, decision makers in outsourcing logistics activities, and information sources used in the decision-making process. A structured survey was selected as the tool for data collection. The field study involved face-to-face interviews with 204 companies out of top 500 companies ranked in terms of turnover that are registered with industrial associations and chambers of commerce in Turkey. Moreover, a decision support system based on Bayesian Causal Map is proposed for 3PLs in order to assist them in their service proposals for different sectors. This study is a first attempt to reveal and compare the outsourcing perception of the companies in different sectors, to expose the firms’ underlying motives as well as the respective importance of these motives for outsourcing logistics activities in Turkey. The use of Bayesian Causal Map based on the survey results provides an important guide to the 3PL providers to pick a suitable strategy and to prioritize their operational activities in different sectors in such a way to achieve a competitive advantage

Design Architecture-Based on Web Server and Application Cluster in Cloud Environment

Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The major idea is to design a web server in the map phase using the jetty web server which will give a fast and efficient way of searching data in MapReduce paradigm. For real time processing on Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in web server with multi-level index keys. The web server uses to handle traffic throughput. By web clustering technology we can improve the application performance. To keep the work down, the load balancer should automatically be able to distribute load to the newly added nodes in the server

An efficient genetic algorithm for large-scale planning of robust industrial wireless networks

An industrial indoor environment is harsh for wireless communications compared to an office environment, because the prevalent metal easily causes shadowing effects and affects the availability of an industrial wireless local area network (IWLAN). On the one hand, it is costly, time-consuming, and ineffective to perform trial-and-error manual deployment of wireless nodes. On the other hand, the existing wireless planning tools only focus on office environments such that it is hard to plan IWLANs due to the larger problem size and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh industrial indoor environments. To fill this gap, this paper proposes an overdimensioning model and a genetic algorithm based over-dimensioning (GAOD) algorithm for deploying large-scale robust IWLANs. As a progress beyond the state-of-the-art wireless planning, two full coverage layers are created. The second coverage layer serves as redundancy in case of shadowing. Meanwhile, the deployment cost is reduced by minimizing the number of access points (APs); the hard constraint of minimal inter-AP spatial paration avoids multiple APs covering the same area to be simultaneously shadowed by the same obstacle. The computation time and occupied memory are dedicatedly considered in the design of GAOD for large-scale optimization. A greedy heuristic based over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as benchmarks. In two vehicle manufacturers with a small and large indoor environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD outputted up to 25% less APs than a random OD algorithm. Furthermore, the effectiveness of this model and GAOD was experimentally validated with a real deployment system

Remotely hosted services and 'cloud computing'

Emerging technologies for learning report - Article exploring potential of cloud computing to address educational issue

High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.

Proposal to Strenghern Health Information System [HIS]

\ud The HMIS Program described in this document aims at improving and strengthening the current Health Management Information System (HMIS) in Tanzania, known as MTUHA. The consortium behind the HMIS Program is headed by the Ministry of Health & Social Welfare (MOHSW) and consists of the following additional partners; Ifakara Health Research and Development Centre, University of Dar es Salaam and the University of Oslo, representing national and international capacity in HMIS. The HMIS Program is linked to the Payment for performance (P4P) funding scheme which is initiated by the Norway Tanzania Partnership Initiative. The P4P has a focus on maternal and child health and relies upon quality indicators on performance in these areas from health facilities and districts. The provision of quality data and indicators on MDG 4 & 5 is therefore a key target for the HMIS Program. The chosen approach is, however, to derive these data from the HMIS and not to establish a separate data collection structure, hence the HMIS Program. Quality information by way of essential indicators, such as for monitoring the Millennium Development Goals 4 & 5, are crucial for health services delivery and program management as well as for M&E. Currently, however, the HMIS is not providing such needed data of sufficient completeness, timeliness and quality, leading health programs and funding agencies to establish their own structures for data collection, and thus creating fragmentation and adding to the problem. The HMIS Program aims at changing this negative trend and turning the HMIS into the key source of shared essential quality information in Tanzania by; focusing on action oriented use of information for management at each level of the health services and by providing timely quality information to all stakeholders, including all health programs and funding agencies in the HMIS strengthening process – making it an all-inclusive national process, focusing on capacity development; on-site support and facilitation, short courses and continuous education, building capacity in the MOHSW and establishing a national network of HMIS support, and by building on experience, methods and tools from Africa’s “best practices” HMIS, such as South Africa – and Zanzibar Within this proposal the aim is to carry out the HMIS strengthening process in 1/3 of the districts in the country, 7 regions, during the first 3 years. The objective, however, is to cover the entire country during the 5 years duration of the NTPI. By aiming at quick and tangible results, the expectation is that other funding agencies will join forces and thereby ensuring national coverage.\ud \u

Complex Care Management Program Overview - Technology

This report provides an overview of technology based complex care management programs, including:Cook County Health and Hospitals System - Computer Assisted Quality of Life and Symptom Assessment of Complex PatientsUniversity of Missouri - TigerPlaceWenatchee Valley Medical Center - Health Buddy -- Patient Telemonitoring Progra