Optimal Solution Approaches to the Chance Constrained Mathematical Model of the Stochastic Demand Vehicle Routing Problem with Simulated Annealing Algorithm

Year 2023, Volume: 28 Issue: 1, 252 - 270, 30.04.2023

Adem Şehitoğlu Şakir İşleyen

https://doi.org/10.53433/yyufbed.1174742

Abstract

Supply chain and logistics management have an important place in the global economy, from the production stage of the raw material to the delivery to the end customer. Vehicle routing problems play an important role, providing the distribution network of finished products from a central warehouse to the end customer. Vehicle routing problems are modelled as more complex and stochastic every day to take the most effective decisions. Stochastic vehicle routing problems are probabilistically modelled from the uncertainty of customer demands, time, routes, and service parameters. The stochastic demand vehicle routing problem is one of the problems in which the customer demands are not known beforehand, and the service vehicle is known after it reaches the customer. In this study, a chance-constrained model with stochastic demand was created with the routes, demands and coordinates followed by a bread factory in Van during the distribution of bread to the markets. The factory's route information is compared with the near-optimal problem solution obtained from a meta-heuristic, Annealing Simulation algorithm. According to the results, the routes obtained from the algorithm gave better results than the routes followed by the bakery.

Keywords

Chance constrained, Optimization, Simulated annealing, Vehicle routing with stochastic demand

Stokastik Talepli Araç Rotalama Probleminin Şans Kısıtlı Matematiksel Modeline Tavlama Benzetimi Algoritması ile Optimal Çözüm Yaklaşımları

Abstract

Tedarik zinciri ve lojistik yönetimi ham maddenin üretim aşamasından son müşteriye ulaşmasına kadar küresel ekonomide önemli bir yere sahip olmuştur. Bitmiş ürünlerin merkezi bir depodan son müşteriye kadar dağıtım ağını sağlayan araç rotalama problemleri önemli bir rol oynamaktadır. Araç rotalama problemleri en etkili kararları alabilmek için her geçen gün daha karmaşık ve stokastik olarak modellenmektedir. Stokastik araç rotalama problemleri müşteri taleplerinin, zamanın, yolların ve hizmet gibi parametrelerinin belirsizliğinden olasılıksal olarak modellenmektedir. Stokastik talepli araç rotalama problemi, müşteri taleplerinin önceden bilinmediği hizmet aracının müşteriye ulaştıktan sonra tam olarak bilindiği problemlerdendir. Bu çalışmada, Van’da bir ekmek fabrikasının marketlere ekmek dağıtımı sırasında izlediği rotalar, talepler ve koordinatlar ile stokastik talepli şans kısıtlı bir model oluşturulmuştur. Fabrikanın kendi rota bilgileri, bir meta sezgisel olan Tavlama Benzetimi algoritmasından elde edilen optimale yakın problem çözümü ile karşılaştırılmıştır. Sonuçlara göre algoritmadan elde edilen rotalar fırının izlediği rotalardan daha iyi sonuçlar vermiştir.

Keywords

Optimizasyon, Stokastik talepli araç rotalama, Şans kısıtı, Tavlama benzetimi

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