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    Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization
    (Sage Publications Ltd, 2017) Gocken, Mustafa; Dosdogru, Ayse Tugba; Boru, Asli; Geyik, Faruk
    A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, and the presence of complex interactions between supply chain members, can become quite challenging to optimize and requires a complex model. At this point, the Simulation Optimization (SO) model gains a better understanding of the complex and messy phenomenon of the inventory control of supply chain members. By creating SO models for Distribution Center (DC)s and Suppliers, we wish to present flexible and comprehensible research on the important decision of whether to minimize the differences between total overordering cost and total underordering cost (Model 1) or to minimize the total supply chain cost (Model 2). We also try to point out several important issues: the optimal value of the initial inventory, the reorder point, and the order-up-to level in continuous (s, S) policy for each DC and each Supplier; whether SO models can successfully integrate the supplier selection and continuous (s, S) policy for the supply chain environment; how to apply statistical analysis skills to compare these SO models with a greater level of detail. According to the cost analysis, the total supply chain cost of Model 1 is improved approximately 22% with Model 2. Also, Model 2 is the best one according to quantity-based analysis, order-based analysis, probability-based analysis, and lead-time-based analysis. Model 2 can be successfully applied for the actual situation of the supply chain inventory system and companies can obtain a remarkable amount of saving while increasing their competitive edge.
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    Öğe
    Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility
    (Springer London Ltd, 2015) Dosdogru, Ayse Tugba; Gocken, Mustafa; Geyik, Faruk
    Flexibility is an important task for effectively utilizing resources in a manufacturing system and responding demands rapidly. In manufacturing systems, there exist different types of flexibility levels. In this study, the stochastic flexible job shop scheduling problem is considered to measure the impact of routing flexibility on shop performance. Thus, an integrated genetic algorithm-Monte Carlo method is proposed to analyze the effect of routing flexibility. To make the problem more realistic, system parameters (processing times, operation sequences, etc.) are generated randomly via Monte Carlo. An experimental design is utilized to analyze main and interaction effects of the factors considered (i.e., number of parts, number of machines, number of operations, and flexibility levels) by using a genetic algorithm which is specifically designed for the stochastic flexible job shop scheduling problem. In developed genetic algorithm, different initial strategies which not only improve solution quality but also decrease solution time are used. Makespan is specified as the objective function to be minimized. Results are analyzed with a full factorial analysis of variance. Comprehensive discussions of results are given case by case.
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    Öğe
    (R, s, S) inventory control policy and supplier selection in a two-echelon supply chain: An Optimization via Simulation approach
    (Institute of Electrical and Electronics Engineers Inc., 2016) Gocken, Mustafa; Boru, Asli; Dosdogru, Ayse Tugba; Geyik, Faruk
    Existing literature proves that Optimization via Simulation (OvS) is relatively easy to develop regardless of the complexity of the problem and provide a much more realistic solution methodology without assumption. Hence, we used OvS to determine optimal (R, s, S) policy for Distribution Center (DC)s and suppliers and to properly select the suppliers for DCs under stochastic environmental condition and lost sales system. Determining the optimal parameters, especially determining reorder point and order-up-to level, are major challenges for (R, s, S) policy and hence, their optimal values are determined by means of OvS. Also, initial inventories of DCs and suppliers are considered because the initial conditions of a simulation are crucial aspects of simulation modeling. The proposed OvS model can be helpful for managers to understand better the scope of both the problem at hand and opportunities associated with inventory management. © 2015 IEEE.
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    Öğe
    (R, s, S) INVENTORY CONTROL POLICY AND SUPPLIER SELECTION IN A TWO-ECHELON SUPPLY CHAIN: AN OPTIMIZATION VIA SIMULATION APPROACH
    (IEEE, 2015) Gocken, Mustafa; Boru, Asli; Dosdogru, Ayse Tugba; Geyik, Faruk
    Existing literature proves that Optimization via Simulation (OvS) is relatively easy to develop regardless of the complexity of the problem and provide a much more realistic solution methodology without assumption. Hence, we used OvS to determine optimal (R, s, S) policy for Distribution Center (DC) s and suppliers and to properly select the suppliers for DCs under stochastic environmental condition and lost sales system. Determining the optimal parameters, especially determining reorder point and order-up-to level, are major challenges for (R, s, S) policy and hence, their optimal values are determined by means of OvS. Also, initial inventories of DCs and suppliers are considered because the initial conditions of a simulation are crucial aspects of simulation modeling. The proposed OvS model can be helpful for managers to understand better the scope of both the problem at hand and opportunities associated with inventory management.