Browsing by Author "Birim, S"

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    EVALUATING VENDOR MANAGED INVENTORY SYSTEMS: HOW INCENTIVES CAN BENEFIT SUPPLY CHAIN PARTNERS
    Birim, S; Sofyalioglu, C
    In a vendor managed inventory (VMI) system, the effects of financial incentives on the entire supply chain (SC) and on the individual firms are investigated in this study. To this end, order management, order replenishment and inventory control activities of a two-echelon SC are examined via modeling using discrete event simulation. By determining the appropriate parameters for the incentives with scenario analysis, balanced profit distribution between buyers and a supplier in VMI is established. Simulation outputs of the traditional model, VMI only and VMI with incentives models are compared based on profits with paired comparisons. In VMI with incentives, both buyers, and the supplier experience higher benefits than the traditional system. This study provides a new method which eliminates the unbalanced benefit distribution due to VMI and offers almost equal benefits to the participating firms. With financial incentives, firms are encouraged to share information with each other to work in a coordinated SC.
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    Vehicle routing problem with cross docking: A simulated annealing approach
    Birim, S
    Cross docking is a valuable logistics strategy given that it provides less inventory holding costs, less transportation costs and fast customer deliveries. Cross docking should also be considered in strategic planning since it provides competitive advantage by reducing firm costs. An efficient vehicle routing may even increase the benefits of the cross docking. This study presents a vehicle routing problem in a cross docking setting with heterogeneous vehicles having different capacities. All the routes begin and end at the cross dock and all the pickup and delivery sites are visited by only one vehicle. The aim of this study is to find the routes that minimize total transportation costs and the fixed costs of the vehicles. A simulated annealing algorithm is proposed to solve the problem. The proposed simulated annealing heuristic presents reasonable solutions in terms of computational time, best cost values and the convergence pattern on the best cost. (C) 2016 The Authors. Published by Elsevier Ltd.
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    ANALYSIS AND EVALUATION OF THE SUSTAINABLE PROTECTION AND CONTROLLED USAGE ENVIRONMENTS WITH THE SUPPORT OF GIS USING T-TEST: CASE STUDY OF CESME, IZMIR, TURKEY
    Birim, S; Ankaya, F
    Turkey is not only prosperous with a unique natural heritage but the cultural heritage of history of mankind and civilization. In Turkey, there are hundreds of thousands of hectares of natural protected environments with boundaries. Loss of site characteristics and dissolution caused by boundary restriction and change of status are particularly vital problems facing the hundreds of thousands of hectares naturally protected environments conserved by laws in Turkey. In the country, natural protected area is divided into three categories; 1) Sensitive area to be strictly protected, 2) Qualified natural protection areas 3) Sustainable protection and controlled usage areas. This study examines the evaluation of the Sustainable Protection and Controlled Usage Environments in Cesme (Izmir / Turkey) district to determine the protection strategy and accurate determination based on scientific data. Based on this strategy, in Cesme, Izmir, Turkey, 17 polygons which were specified as Sustainable Protection and Controlled Usage Areas (SPCUA) by GIS method are reevaluated by 3 specialists who were asked to answer the questions using yes, no, or partly in the chart. yes is given 2 points, no is given 0 points and partly is given 1 point. The goal of this paper is to examine whether there is a substantial difference between the groups specified by GIS method Using dependent sample T-Test. The result of the analysis showed there is no significant difference between the GIS method and the evaluations of all the specialists.
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    The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
    Birim, S; Kazancoglu, I; Mangla, SK; Kahraman, A; Kazancoglu, Y
    In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory (LSTM),-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer's real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.