Bu çalışmada literatürde çalışılan en önemli kombinatoryal eniyileme problemlerinden biri olan stokastik araç rotalama problemi (SARP) ele alınmıştır. Bilindiği üzere klasik araç rotalama probleminde, araçların kapasiteleri ve müşterilerin talepleri bilinmektedir yani problem deterministiktir. Gerçek hayat problemlerinde problem parametreleri farklı durumlara göre değişkenlik gösterdiğinden, parametrelerin kesin değerlerinin bilinmesine az rastlanmaktadır. Bu yüzden belirtilen klasik araç rotalama probleminin belirsizlik koşulları altında formüle edilmesine ihtiyaç duyulmaktadır. Ele alınan çalışmada, müşteri taleplerinin belirsiz olduğu durumlar için, araç rotalama problemi analiz edilmiştir ve talepler stokastik olarak modelde değerlendirilmiştir. Değişken talep durumlarını incelemek için düzgün, üstel ve Poisson olmak üzere 3 farklı dağılım kullanılarak, bu dağılımların problemin çözümleri üzerindeki etkileri incelenmiştir. Hesaplama sonuçları için GAMS yazılımı kullanılmıştır ve çalışmanın sonunda ele alınan problemin stokastik ve deterministik modellerinin sonuçları kıyaslanmıştır.

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In this paper, we study three types of heterogeneous fixed fleet vehicle routing problems, which are capacitated vehicle routing problem, open vehicle routing problem and split delivery vehicle routing problem. We propose new multiobjective linear binary and mixed integer programming models for these problems, where the first objective is the minimization of a total routing and usage costs for vehicles, and the second one is the vehicle type minimization, respectively. The proposed mathematical models are all illustrated on test problems, which are investigated in two groups: small-sized problems and the large-sized ones. The small-sized test problems are first scalarized by using the weighted sum scalarization method, and then GAMS software is used to compute efficient solutions. The large-sized test problems are solved by utilizing the tabu search algorithm.2010 Mathematics Subject Classification. Primary: 90C08, 90C11; Secondary: 90C29.

Summary As a widely used plastic material polyvinyl chloride (PVC) accounts for a significant amount of plastic waste but also offers great potential in conversion to chemical feedstock via pyrolysis process. However, development of a sensitive mathematical approach is required for proper process design and monitoring of thermochemical conversion processes. In this work, we attempt to develop an artificial neural network (ANN) model for estimation of mass loss as a function of temperature and heating rate during pyrolysis and combustion of PVC. For this purpose, pyrolysis and combustion characteristics were quantified using thermogravimetric analysis, then non‐isothermal kinetics were analysed by iso‐conversional models. The results of ANN models show that this method helps predict complex systems with high regression coefficient (R2) values. The best performed model analysed by ANN for pyrolysis was NN 7 with R2 = 0.9993, the best performed model for combustion was NN 10 with R2 = 0.9982. Comparison of experimental results to ANN predictions indicates that ANNs with a quick propagation algorithm can be an effective approach for modelling complex non‐linear systems such as thermal degradation of thermoplastics.

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