Ortak Kısıtlı Rota Kapsama Problemlerinin Çözümü İçin Melez Genetik Algoritma Yaklaşımı

Ortak Kısıtlı Rota Kapsama Problemleri (OKRKP’ler) tam kamyon yükü hizmeti satın alma işbirliği ağlarında ortaya çıkan NP-Zor ayrıt rotalama problemleridirler. Bu problemlerde amaç, işbirliği yapan birden fazla gönderici firmanın tam kamyon yükü gönderi rotalarını, birden fazla gönderici firmadan gönderi rotası ve boş kamyon hareketleri içerebilecek ve göndericilerin çevrim paylaşmak istediği azami ortak sayılarını aşmadan kapsayan en kısa toplam uzunluklu yönlü çevrimler kümesini bulmaktır. Bu makale, OKRKP’lerin çözümü için geliştirilen; genetik algoritma, yerel arama ve geniş komşuluk arama yaklaşımlarının birleşiminden oluşan bir melez genetik algoritma (MGA) yaklaşımını sunmaktadır. Bu yaklaşım, NP-Zor RKP’lerin çözümü için önerilen ilk meta-sezgisel çözüm yaklaşımıdır.  Önerilen MGA, daha önce literatürdeki çalışmalarda kullanılan problem örnekleri üzerinde denenmiştir. Deneylerde kullanılan büyük ölçekli problem örneklerinin önemli bir kısmında bilinen en iyi çözümlerden daha iyi çözümler elde edilmiştir. 

A Hyrid Genetic Algorithm Approach for Solving Partner Constrained Lane Covering Problems

Partner Constrained Lane Covering Problems (PCLCPs) are NP-Hard arc routing problems arising in collaborative truckload transportation procurement networks. The objective in these problems is to cover a set of full truckload shipment lanes of multiple shippers using cyles, each of which may include lanes from different shippers and empty truck movements, with minimum total cost such that each shipper does not share cycles with more than a prespecified number of partners. This paper presents a hybrid genetic algorithm (HGA) approach that combines genetic algorithm, local search and large neighborhood search approaches for solving PCLCPs.  This approach is the first meta-heuristic that has been proposed for solving NP-Hard LCPs. The proposed MGA has been tested on instances that were previously used in the literature. It has improved the previous best known solutions of a significant portion of the large scale instances that were tested. 

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