Solution Of Order Batching Problem With Association Rules And Genetic Algorithm: A Case Study In Pharmacy Warehouse

Yıl 2016, , 129 - 142, 30.10.2016

Furkan Yener , Harun Reşit Yazgan , Serap Ercan Cömert , Sena Kır , Yağmur Kaya

https://doi.org/10.22532/jtl.267841

Cited By: 1

Öz

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.

Anahtar Kelimeler

Warehouse Management, Data Mining, Genetic Algorithm, Order Batching

Birliktelik Kuralları ve Genetik Algoritma İle Sipariş Yığınlama Probleminin Çözümü: Bir Ecza Deposunda Uygulama

Öz

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.

Anahtar Kelimeler

Depo Yönetimi, Veri madenciliği, Genetik Algoritma, Sipariş Yığınlama

Kaynakça

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