Aykut KENTLİ, Vedat DAL, Ali Fuat ALKAYA

HAZIR GİYİM SANAYİNDE ÜRETİM HATLARINDA MAKİNE DEĞİŞİM SÜRESİNİN EN AZA İNDİRİLMESİ

Bu çalışmada bir hazır giyim firması için makine değişimlerinin düzenleyen çizelgeleme sorunu ele alınmıştır ve sipariş edilen ürünlerin üretimi için gerekli makine değişimleri dikkate alarak, toplam kurulum süresini en aza indirmek için bir sezgisel metot kullanılmıştır. En kısa işlem süresi ve en erken teslim tarihi gibi sık kullanılan sezgisel metotlar ile geçerliliği olan bir çizelgeleme için kullanılabilir, ancak genellikle bu durumda en iyi sonuca yakın çizelgeler üretilemez. Bu çalışmada sorunu iki alt problem bölerek bir çözüm yöntemi geliştirdik. İlk problemin çözüm sonuçları ikincisinin giriş verileri olarak kullanılmıştır. Bu süreçte, birinci problemin çözümünde yeni geliştirilmiş bir sezgisel metot kullanıldı. İkinci problem açık ve asimetrik bir seyyar satıcı problem olarak formüle edildi. İkinci problem Genetik (GA) ve Benzetilmiş Tavlama (BT) algoritmaları kullanılarak çözüldü. Deney sonuçları önerilen algoritmanın makine değişimlerini göz önüne alan çizelgeleme sorununun çözümünde etkin olduğunu göstermiştir

Anahtar Kelimeler:

Hazır Giyim Sanayi, Kombinatoryal optimizasyon, Değişim zamanı, BT, GA

MINIMIZING MACHINE CHANGEOVER TIME IN PRODUCT LINE IN AN APPAREL INDUSTRY

This study deals with a scheduling problem with machine changeovers in a apparel company and presents a heuristic to minimize the total setup time subject to machine changeovers in models ordered. Commonly used heuristics such as shortest processing time and earliest due date can be used to calculate a feasible schedule quickly, but usually do not produce schedules that are close to optimal in these environments. A solution approach for the problem is developed by dividing it into two subproblems. Solution of the first problem is given as an input for the second problem. In this process, an originally developed heuristic is applied for the first problem and the second problem is formulated as an open and asymmetric traveling salesman problem. The second problem is solved by Genetic (GA) and Simulated Annealing (SA) algorithms. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the scheduling problem with machine changeovers

Keywords:

Apparel Industry, Combinatorial optimization, Changeover time, SA, GA,

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