Purpose: Seaports are regarded as significant actors in global logistics and supply chains since a large part of the cargoes carried over the globe are being processed there. When the cold chain broken down during transport and storage in the ports, the humidity, nutrition, temperature and time conditions to be required for the growth of the bacteria occur, and rapid reproduction occurs and the properties of the products are rapidly deteriorating. It is imperative that especially medicines, some chemical substances and foodstuffs need to be transported without breaking the cold chain in the logistics. The monitoring and control of the temperature and humidity level is important in the time period between the loading of these containers in special areas in ports, the loading of freight in open areas, or the loading of freight on roads and railway carriages. For this reason, precise monitoring and control of the system is vital in the port logistics management. Method: In this study, an IoT-enabled system is designed for Container Ports by developing software, interface and equipment that will enable remote monitoring of temperature, humidity and other necessary key status parameters. Findings: The developed IoT-based system provides audible and visual warning, e-mail and SMS communication, similar to a monitoring screen such as a heart graph monitor, when the instant values of the refrigerated container are transmitted to the database, when the defined upper and lower values are approached. All these data and major change information are archived in the database and retrospective situation analysis and data analysis can be performed. Conclusion: Using technologies such as Wireless Sensor Network (WSN) and RFID, an IoT-enabled Cold Chain Logistics system has been proposed that provides real-time monitoring of products in containers at ports, providing DS services to logistics providers and customers. In this context, it was explained how the ambient parameter values were collected in real time using WSN and IEEE 802.15.4, how the collected data was sent to the server via the GSM gateway. In the port scenario, activation devices such as IEEE 802.15.4 and RFID were modeled using the OPNET simulator. The developed model was carried out in accordance with the principles of EPCglobal Gen 2. With the proposed approach, smart solutions provide a smarter flow of information. The results show that IoT- enabled cold chain systems have a great potential for managing, monitoring, receiving and determining abnormal events related to temperature-sensitive products in real time.

An efficient store layout presents merchandise to attract customer attention and encourages customers to walk down more aisles which exposes them to more merchandise, which has been shown to be positively correlated with the sales. It is one of the most effective in-store marketing tactics which can directly influence customer decisions to boost store sales and profitability. The recent development of Artificial Intelligence techniques, especially with its sub-fields in Computer Vision and Deep Learning, has enabled retail stores to take advantage of existing CCTV infrastructure to extract in-store customer and business insights. This research aims to conduct a comprehensive review on existing approaches in store layout design and modern AI techniques that can be utilized in the layout design task. Based on this review, we propose an AI-powered store layout design framework. This framework applies advanced AI and data analysis techniques on top of existing CCTV video surveillance infrastructure to understand, predict and suggest a better store layout.

Simulation is one of the most effective methods in the Design of Manufacturing Systems (MS). Typical reasons for simulation of a manufacturing system includes evaluating the capacity and equipment utilization, identifying bottlenecks in the system, comparing the performance of alternative designs. Simulation is often coupled with Artificial Intelligence (AI) techniques to provide an efficient decision making framework. In this study, Artificial Neural Networks (ANN) are used for the design of a manufacturing system. Four different priority rules are used: EDD, SPT, CR and FCFS. As a result four different design alternatives are obtained from trained ANN. Performance measures of a manufacturing system are given to the ANN which then gives a design alternative. The design alternatives are evaluated in terms of performance measures and then the best design alternative is selected from four different alternatives.