LOJİSTİK SEKTÖRÜNDE AĞIR TİCARİ ARAÇ SEÇİMİ PROBLEMİNE ÇOK ÖLÇÜTLÜ BİR YAKLAŞIM

Ulaştırma sektöründe faaliyet gösteren lojistik firmaların temel elamanlarından biri sahip oldukları yük taşıma araçlarıdır. Özellikle firma bünyelerine yeni araç alınması veya araç filosunun tamamen yenilenmesi söz konusu olduğunda, karar verici konumundaki yöneticiler için araç seçim problemleri, çok fazla ölçüte bağlı olarak değerlendirilmesi nedeniyle karar verilmesi gereken zor problemlerdir. Firmalar için çok büyük bir öneme sahip karar verme problemlerinin akılcı bir biçimde çözülebilmesi için bilimsel yöntemlerin kullanılması gerekmektedir. Bu çalışmada, bir firmanın araç filosuna katmayı düşündüğü yeni yük aracı alım sürecinin değerlendirilebilmesi için gereken seçim ölçütlerinin belirlenmesi ve bu ölçütlerin önem ağırlıkları doğrultusunda en iyi aracın seçilmesi modellenmiştir. Ağır ticari araç seçimi için önerilen 20 ölçütün ağırlıkları analitik ağ süreci (AAS) ile belirlenmiş ve araçların sıralaması PROMETHEE yöntemi ile yapılmıştır.

A MULTI CRITERIA APPROACH FOR HEAVY COMMERCIAL VEHICLE SELECTION PROBLEM IN LOGISTICS SECTOR

One of the basic elements of logistics firms operating in the transportation sector is their load carrying vehicles. Especially, in the case of taking new vehicles for a company or a total renewal of vehicle fleet is concerned, vehicle selection problems are too difficult for the managers who are at the position of decision making, because it depends on the evaluation of multi criteria. In order to be solved in a rational manner, scientific methods should be used to solve the decision making problems which have great importance for the firms. In this study, the determination of the selection criteria for the process of buying a new vehicle which a firm intends to add it to vehicle fleet and choosing the best vehicle in accordance with these importance weights of criteria has modeled. The importance weights of 20 criteria which are proposed for the selection of heavy commercial vehicles are determined by analytic network process (ANP), and vehicles are ranked by using PROMETHEE method.

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