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.

Financial inclusion and poverty: evidence from Turkish household survey data

Abstract: Even though poverty is highly felt in developing economies, the lack of relevant and complete micro-level data limits understanding which households are more exposed to poverty and the role of fina...

Strategies can be expensive too! The value spread and asset allocation in global equity markets

Abstract: Is the value spread useful for forecasting returns on quantitative equity strategies for country selection? To test this, we examine a sample of 120 country-level equity strategies replicat

Using International Accreditation in Higher Education to Effect Changes in Organisational Culture: A Case Study from a Turkish University.

Abstract: International accreditation is now a significant yet controversial issue in global higher education. This case study looked at the experience of an intensive English language preparatory programme within a university in Turkey going through an accreditation by a foreign institution, and assessed to what extent the project managed to foster changes in its organisational culture. While it was clear that most felt the accreditation had a significant impact, there was much less clear evidence of changes in organisational culture.

A populated local search with differential evolution for blocking flowshop scheduling problem

Abstract: This paper presents a populated local search algorithm through a differential evolution algorithm for solving the blocking flowshop scheduling problem under makespan criterion. Iterated greedy and iterated local search algorithms are simple but extremely effective in solving scheduling problems. However, these two algorithms have some parameters to be tuned for which it requires a design of experiments with expensive runs. In this paper, we propose a novel multi-chromosome solution representation for both local search and differential evolution algorithm which is responsible for providing the parameters of IG and ILS algorithms. In other words, these parameters are learned by the differential evolution algorithm in order to guide the local search process. We also present the greedy randomized adaptive search procedure (GRASP) for the problem on hand. The performance of the populated local search algorithm with differential evolution algorithm and the GRASP heuristic is tested on Taillard's benchmark suite and compared to the best performing algorithms from the literature. Ultimately, 90 out of 120 problem instances are further improved.