Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Sosyal girişimcilik boyutunda kütüphaneler

Abstract: Rapidly changing technology is affecting the functions and working styles almost in every organizations and these changes are also influencing the libraries. Libraries have basic funcitons however functions and responsibilites of libraries go beyond getting information to the public with the beginning of digital age. Adapting to the changes may be possible by internalizing new methods into library management. The concept of social entrepreneurship can come to the forefront in terms of reorganizing library services. Creating social value may be possible by going beyond traditional duties and responsibilities of libraries. Though there are many fields that libraries can create social value in the society, few of them shine out for immediate action. These are promoting information literacy, providing true, complete and accurate information and extending Access to information in the rural.

An Application of Permutation Flowshop Scheduling Problem in Quality Control Processes

Abstract: This study involves in a real world application of permutation flowshop scheduling problem (PFSP) in a local company that produces apparel and garments. Various test jobs arrive in the quality control department and a schedule has to be prepared considering the test stations and processing times. A job consists of a series of operations on certain stations. Each station is designed to perform a particular test operation. Therefore, there are jobs waiting to go through a series of quality control operations on various stations. Each job has different processing times at stations. Moreover, different jobs may have different processing times at a particular test station. However, the operation sequences (routes) of all jobs are the same through the stations. It is not allowed to change the order of the jobs on different stations. This problem falls into the realm of permutation flow shop scheduling problem (PFSP) in the literature. Various mathematical models are defined in literature for the optimal solution of the problem. One of them was chosen and used to solve small-sized problems. However, as the size of the problem increases, and since the problem is shown to be NP-hard for three or more machines, the solution time increases rapidly and hence it becomes significantly hard to solve the problem in polynomial time. Therefore, a combination of two heuristic methods is employed to solve the problem in reasonable time. First, NEH (Nawaz, Enscore, Ham) method is used to obtain a good initial feasible or a near optimal solution and then iterated local search method (ILS) is engaged to improve the solution. This solution procedure is implemented in a decision support tool in order to develop efficient schedules for quality control jobs in the company. The performance of the procedure is evaluated and verified by comparing the solutions with the optimal solutions of small sized problems. The tool can also be used for educational purposes since it is user friendly and has ability to present outcomes in detail with proper graphics.

A discrete artificial bee colony algorithm for the assignment and parallel machine scheduling problem in DYO paint company

Abstract: This paper presents a discrete artificial bee colony algorithm to solve the assignment and parallel machine scheduling problem in DYO paint company. The aim of this paper is to develop some algorithms to be employed in the DYO paint company by using their real-life data in the future. Currently, in the DYO paint company; there exist three types of filling machines groups. These are automatic, semiautomatic and manual machine groups, where there are several numbers of identical machines. The problem is to first assign the filling production orders (jobs) to machine groups. Then, filling production orders assigned to each machine group should be scheduled on identical parallel machines to minimize the sum of makespan and the total weighted tardiness. We also develop a traditional genetic algorithm to solve the same problem. The computational results show that the DABC algorithm outperforms the GA on set of benchmark problems we have generated.

Design of smart urban drainage systems using evolutionary decision tree model

Abstract: Recently, as an alternative method for monitoring of drainage systems, Internet of Things (IoT) technology is initiated in smart cities. IoT is used for detection of the location of the sediment deposition within the drainage pipe system to alert for repairing before complete blocking. However, from the hydraulic point of view, it is reasonable to design the drainage and sewer pipes to prevent the deposition of the sediment based on the physical parameters. To this end, instead of detection of blockage location, monitoring the flow characteristics is of more importance to keep pipe bottom clean from sediment deposition. Accordingly, smart sensors mounted in the drainage and sewer pipes should read the flow velocity and alert once the flow reaches a velocity in which sediment deposition is occurred. In order to determine the sediment deposition velocity, this study models sediment transport in drainage systems by means of evolutionary decision tree (EDT) technique. EDT results are compared with conventional decision tree (DT) and evolutionary genetic programming (GP) techniques. A large number of experimental data covering wide ranges of sediment and pipe size were used for the modeling. Evaluation of the developed models in terms of verity of statistical indices showed the outperformance of the proposed EDT model. The EDT, DT and GP models were found superior to their traditional corresponding regression models existing in the literature. Results are helpful for determination of the flow characteristics at sediment deposition condition in drainage systems maintained using IoT technology in smart cities.