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.

Lean and Sustainable Warehousing

Abstract: This chapter depicts a picture of sustainable warehousing from the perspective of the time-phased impact of warehouses on economic, environmental, and social dimensions of sustainability. The authors present sustainability issues in warehouses within three levels: macro, meso, and micro. In the macro level, they review the effect of warehouse location and construction on sustainability. In the meso level, they discuss how to deal with warehouse layout problem, the effects of aisle, and material handling equipment choices on sustainability. Last, they briefly present what warehouse managers can do for sustainable warehousing in a short amount of time. Hence, the authors aim to provide a holistic approach to make warehouses sustainable. Last but not the least, they also present supportive and strengthening theoretical and practical studies to resolve barriers in front of sustainable warehousing.

Necessary conditions for partially observed optimal control of general McKean–Vlasov stochastic differential equations with jumps

Abstract: In this paper, we establish necessary conditions of optimality for partially observed optimal control problems of Mckean–Vlasov type. The system is described by a controlled stochastic differential...

Control of an Inverted Pendulum by Reinforcement Learning Method in PLC Environment

Abstract: The aim of this study is to implement Q-learning algorithm to move an inverted pendulum from the downright position to upright position in a PLC environment. Instead of using classical control algorithms that need a linear model of the system to be controlled, we used model-free control algorithm, i.e. Q-learning, and relaxed the linearity assumption. We demonstrate that reinforcement learning can be successfully used in industrial machine learning applications to learn complex control policies without having a detailed model of the controlled system. An experimental set up is designed using PLC controlled mechanical parts, and the code is written in PLC. After about three hours of learning stage, the Q learning algorithm successfully moved inverted pendulum from downright position to upright position and keep it in balanced upright position.

A Bi-Criteria Single Machine Scheduling with Rate-Modifying-Activity

Abstract: In this paper, we consider a single machine scheduling problem with two criteria: minimizing both total flow time with total tardiness and minimize maximum tardiness with number of tardy jobs. Unlike the classical scheduling problems, we use a job position deterioration, which means that the job processing time increases as a function of the job position. Besides deteriorated jobs, we also consider rate-modifying-activities which alter the efficiency of the deteriorating processor. This is the first paper, to combine both time dependent processing times and problems with rate-modifying-activity in the bi-criteria objectives. To solve the new type of problem, we introduce a new scheduling mathematical model which is based on one developed Ozturkoglu and Bulfin [1]. To analyze the efficiency of the mathematical model, we use three different approaches. According to computational results, up to 50 jobs can be solved in less than one minute. Keywords: Single-Machine Scheduling, Bi-criteria, Deteriorated Jobs, Rate-Modifying- Activity