It is clearly known that urban freight transportation has a significant role on sustainable development of urban areas. The persistent growth of the costs of freight transportation and as a result of congestion, environmental pollution and increasing inefficient usage of land in urban areas are forcing users and public authorities to develop alternative logistic solutions to relieve the freight traffic problem. Establishing logistics centers is one of these alternative solutions. Logistics centers are specific centers that various logistic based activities like distribution, storage, transportation, consolidation, handling, customs clearance, imports, exports, transit processes, infrastructural services, insurance, banking and similar commercial activities are performed. These centers are defined for national and international all logistic and related operations. Logistic centers must be settled near production and commercial centers, highways, railways, airports and if possible seaports. In this study we proposed a fuzzy TOPSIS approach to a logistics center location selection problem in eastern anatolia region of Turkey.
Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model.
The most important mode of long-distance transportation for people to use time efficiently is air transportation. One of the most significant factors determining air traffic performance is the scheduling created by companies. When creating schedules, various parameters are used, mainly the demand for the determined route, and the availability of airplanes and flight crews. Known climatic conditions on the route are generally neglected. Within the scope of this study, the creation of flight schedules, which are normally the preparation of flight schedules by considering the monthly, daily, and hourly constraints that could cause flight delays, were determined by also considering weather conditions. In this determination process, 32-year flight data between 1987 and 2018 were used as a basis. Apache Spark, one of the big data technologies, was used to determine these constraints. The scheduling was optimized by using the constraints used to prevent flight delays. It was observed in the results obtained that simulated annealing (SA) achieved the most optimal results compared to the genetic algorithm (GA) and the artificial bee colony (ABC) algorithm. As the dimensions of the searched space increased, solving the problem with metaheuristic approaches was more advantageous in terms of time compared to the classical method.
Consumption is increasing day by day due to rapid developments in technology and increasing world population. Companies should review their production processes in order to keep up with the consumption rate and increase their competitiveness as well as profit margins. This research shows a case study of simulation application to improve solar panel manufacturing process. The aim is to achieve goals such as increasing system efficiency, reducing inventory and shortening delivery time. Efficient manufacturing process design may reduce work-in-process (WIP), waste, rework and faults, which may reduce the total number of products and yields an increase in the system cost. Inefficiency points were determined in the process based on the obtained observations of the manufacturing system. Various scenarios have been proposed to design such an efficient production system. According to the proposed scenarios, an increase in the utilization rates of machinery, personnel and the total number of final products has been achieved.
Günümüzde teknolojinin gelişimi ile birlikte artık günlük hayatta kullanılan kıyafet ve aksesuarlara akıllı algılayıcıların eklenmesiyle giyilebilir teknolojik ürünler elde edilmektedir. Giyilebilir teknoloji ürünlerinden biri olan akıllı bileklikler sportif faaliyetlerin, uyku takibinin, sağlık durumunun kayıt altına alınmasına ve yaşamın elde edilen bu raporlara göre düzenlenmesine yardımcı olmaktadır. Her geçen gün gelişen teknolojiye bağlı olarak alternatif sayısının artması, sporcuların hangi model ve özelliklere sahip akıllı bilekliği tercih edeceği konusunda zorlanmalarına neden olmaktadır. Bu çalışmada, sporcuların akıllı bileklik tercih sürecini kolaylaştırmak için çok kriterli karar verme tekniklerinden BULANIK DEMATEL (Decision Making Trial And Evaluation Laboratory) yöntemi kriterler arası ilişkileri dikkate alarak kriter ağırlıklarını belirlemek için kullanılmış ve EDAS yöntemi ise (Evaluation Based On Distance From Average Solution) alternatifler arasından seçim yapmak için kullanılmıştır. 4 kişilik bir uzman grubun değerlendirmesiyle elde edilen veriler incelediğinde, DEMATEL yönteminin sonuçlarına göre akıllı bileklik seçiminde en önemli kriterin akıllı bilekliklerin kullanım kolaylığı olduğu ortaya çıkmıştır. Kullanım kolaylığından sonra sırasıyla, estetik, fonksiyonel özellik, ağırlık ve batarya kullanım süresi kriterlerinin en önemli kriterler oldukları belirlenmiştir. EDAS yönteminin sonuçlarına göre ise 2 en iyi performansa sahip akıllı bileklik ortaya çıkmıştır.
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