Furkan Yener

This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm’s execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA’s parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives.

A DC motor widely uses for sensitive speed and position in industry. Stability and productivity of a system are important for controlling of a DC motor speed. Stable of speed which affected from load fluctuation and environmental factors. Therefore, it is important for the speed value which is required as constant and to keep it as its value. In this study, it is aimed that the speed value which is achieved as required value and keeping it as constant using Proportional, Integral and Derivative (PID) controller for tuning parameters. Firstly, Ziegler-Nichols (ZN) is one of a traditional method used. PID parameters are determined with responses of open-loop under running system. Later, parameters of the PID are estimated using two metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). As a result, three algorithms’ results are compared based on five criteria. The PSO algorithm produces better results than Genetic Algorithm for each criteria.

Tr en Bu çalışmada, bir ecza deposunun günlük sipariş toplama stratejisi incelenmiş ve uygun bir yaklaşım geliştirilmiştir. Ürünlerin ve siparişlerin hacimsel olarak küçük olduğu, toplayıcı kapasitesinin göreceli olarak büyük olduğu depolar için uygun olan, sipariş yığınlama stratejisi kullanılmıştır. Günlük siparişler, siparişler arası ilişkiyi en büyükleyen bir matematiksel modelin yardımıyla sipariş yığınlarına ayrılmış. Birden çok siparişin bir araya gelmesiyle oluşan yığınlar içerisinde benzer ürünleri içeren siparişler bulunduğu için toplama sırasında ki kayıp zaman ve mesafeden tasarruf sağlanacaktır. Matematiksel modelin kesin çözümünün zaman alması nedeniyle genetik algoritma kullanılarak sonuç üretilmiştir. Farklı toplama stratejileri sonucunda kat edilen toplam mesafe benzetim yardımıyla hesaplanarak, istatistiki olarak sonuçların güvenilirliği ve performansları sonuç bölümünde tartışılmıştır. In this study, daily strategy of order picking is examined and proposed a new approach in a pharmacy warehouse. If the goods and orders are relatively small for the picker’s capacity, the order batching strategy should put to use. Daily orders was divided to order batches using mathematical model maximised the relationship among the orders. The order batches which are consist of more than one orders save up time and distance during the order picking period. Optimum solution of mathematical model take up time so that Genetic algorithm is used for order batching problem. Travelled the total distance is simulated in existing and batching strategy, the confidingness and performance of the results are tested statistically and argued in conclusion.