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Öğe Ant Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planning(Mdpi, 2018) Sakalli, Umit Sami; Atabas, IrfanIn this paper, a tactical Production-Distribution Planning (PDP) has been handled in a fuzzy and stochastic environment for supply chain systems (SCS) which has four echelons (suppliers, plants, warehouses, retailers) with multi-products, multi-transport paths, and multi-time periods. The mathematical model of fuzzy stochastic PDP is a NP-hard problem for large SCS because of the binary variables which determine the transportation paths between echelons of the SCS and cannot be solved by optimization packages. In this study, therefore, two new meta-heuristic algorithms have been developed for solving fuzzy stochastic PDP: Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The proposed meta-heuristic algorithms are designed for route optimization in PDP and integrated with the GAMS optimization package in order to solve the remaining mathematical model which determines the other decisions in SCS, such as procurement decisions, production decisions, etc. The solution procedure in the literature has been extended by aggregating proposed meta-heuristic algorithms. The ACO and GA algorithms have been performed for test problems which are randomly generated. The results of the test problem showed that the both ACO and GA are capable to solve the NP-hard PDP for a big size SCS. However, GA produce better solutions than the ACO.Öğe Brass alloy blending problem from quality and cost perspectives: A multi-objective optimization approach(GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2021) Birgoren, Burak; Sakalli, Umit SamiBrass alloy is a composition of copper and zinc and it also includes lead, iron, tin, aluminum, nickel, antimony if necessary. One of the basic problems in brass casting is to determine which pure and scrap materials will be mixed at what quantities; this problem is known as the blending problem. The ingredient ratios of pure materials are exactly known, however they are expensive. The scrap materials are cheaper than the pure ones with varying ingredient ratios. Stochastic mathematical models aiming to minimize blend cost have been developed in the literature. In the solutions of these models, some of the ingredient ratios exactly equal to the specification limits. Because of the variation, some of them may violate the specification limits and cause quality problems in the actual blends. There is only one study in the literature to solve the quality problem by maximizing the process capability index. However, the blend cost increases when the process capability index maximized. In this study, a multiobjective stochastic mathematical model, which aims both to minimize blend cost and to maximize process capability index, has been developed. The developed model has been converted to a deterministic non-linear counterpart by using chance-constrained programming. Then, fuzzy programming is used to transform the multiobjective model into a single objective one. A solution procedure has been proposed to use it effectively in real life applications. The developed model and solution procedure have been tested by the data supplied from a brass factory. The solution of the numerical example has shown that the developed model and solution procedure can be used successfully in real life applications.Öğe Can the uncertainty in brass casting blending problem be managed? A probability/possibility transformation approach(Pergamon-Elsevier Science Ltd, 2011) Sakalli, Umit Sami; Baykoc, Omer FarukProblems about the uncertainty in raw material compositions are a critical issue for the blending problems. It is feared that uncertainty in raw material compositions would often cause percent values of the actual blend to go out of specification limits. In this paper, the aleatory and epistemic uncertainties have been handled simultaneously in a blending optimization problem for brass casting. The aleatory and epistemic uncertainties are modeled by using probability and possibility theories respectively. However, the probabilistic and the possibilistic uncertainties are different from the each other. Therefore to solve the mathematical model, including these uncertainties, a transformation of any type of uncertainty to the other is needed. In this study, probabilistic uncertainties are transformed to the possibilistic uncertainties by considering Rong and Landelma's (2008) and the Dubois, Prade, and Sandri (1993) and Dubois, Foulloy, Mauris, and Prade (2004) transformation approaches. This transformation process converts the former model to a possibilistic model. Then the possibilistic models, obtained from each transformation, are solved by using a cuts approach. The solutions of the two possibilistic models have shown that the model, which uses Dubois's transformation, prepares blends with lower cost than the other model, which uses Rong and Landelma's transformation. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Joint optimization of quality and cost in brass casting using stochastic programming(Taylor & Francis Ltd, 2019) Sakalli, Umit Sami; Birgoren, BurakA critical process in brass casting is the blending of pure and scrap materials to satisfy specified metal ratios. The primary focus in such blending problems has always been cost minimization. The optimal blends produced by mathematical models use large amounts of scrap materials, which are cheaper but have high variations in ingredient ratios. This gives rise to quality problems. This study aims at joint optimization of cost and quality. A chance-constrained nonlinear mathematical model is developed for maximizing the minimum process capability level for a fixed cost. Then parametric programming is used to run the model for different costs to produce a Pareto-optimal frontier. An application to data from a brass factory showed that the frontier is highly nonlinear, enabling the decision maker to select a competitive process capability and cost value combination. The proposed approach is applicable to any blending problem in which ingredient amounts have statistical variation.Öğe Obtaining a Multi-Factor Optimum Blend Using Scrap within the Scope of Sustainable and Environmentally Friendly Steel Production: Application in a Steel-Casting Company(Mdpi, 2024) Bas, Aydogan; Birgoren, Burak; Sakalli, Umit SamiThis study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles and pure elements, alongside their associated costs and environmental impacts in the production of GS52 steel in a foundry company. Through the GAMS program and further verification with Microsoft Excel, we demonstrate that the optimal blend significantly reduces raw material costs by prioritizing scrap (99.7%) over pure elements. Moreover, this optimized blend minimizes energy consumption and associated carbon emissions, thus contributing to a more sustainable and environmentally friendly steel production process. This study offers valuable insights and a practical framework for the steel industry to adopt cost-effective and eco-conscious practices, aligning with global efforts towards sustainable manufacturing.Öğe An optimization approach for brass casting blending problem under aletory and epistemic uncertainties(Elsevier Science Bv, 2011) Sakalli, Umit Sami; Baykoc, Omer FarukA critical process in brass casting is the determination of the materials and their quantities to be added into the blend. The reason of being critical is the uncertainty about metal percentages in scrap raw materials. In this paper, the aleatory and epistemic uncertainties, which are modeled by using probability and possibility theory, respectively, have been handled simultaneously in a blending optimization problem for brass casting and a solution approach that transforms the possibilistic uncertainties into probabilistic ones is proposed. A numerical example is performed by the data supplied from MKE brass factory in Turkey. The results of the example have showed that the proposed approach can be effectively used for solving blending problem including aleatory and epistemic uncertainties in brass casting and other scrap based production process. (C) 2011 Elsevier B.V. All rights reserved.Öğe Optimization of Production-Distribution Problem in Supply Chain Management under Stochastic and Fuzzy Uncertainties(Hindawi Ltd, 2017) Sakalli, Umit SamiProduction-Distribution Problem (PDP) in Supply Chain Management (SCM) is an important tactical decision. One of the challenges in this decision is the size and complexity of supply chain system (SCS). On the other side, a tactical operation is a mid-term plan for 6-12 months; therefore, it includes different types of uncertainties, which is the second challenge. In the literature, the uncertain parameters were modeled as stochastic or fuzzy. However, there are a few studies in the literature that handle stochastic and fuzzy uncertainties simultaneously in PDP. In this paper, the modeling and solution approaches of PDP which contain stochastic and fuzzy uncertainties simultaneously are investigated for a SCS that includes multiple suppliers, multiple products, multiple plants, multiple warehouses, multiple retailers, multiple transport paths, and multiple time periods, which, to the best of the author's knowledge, is not handled in the literature. The PDP contains deterministic, fuzzy, fuzzy random, and random fuzzy parameters. To the best of the author's knowledge, there is no study in the literature which considers all of them simultaneously in PDP. An analytic solution approach has been developed by using possibilistic programming and chance-constrained programming approaches. The proposed modeling and solution approaches are implemented in a numerical example. The solution has shown that the proposed approaches successfully handled uncertainties and produce robust solutions for PDP.Öğe A possibilistic aggregate production planning model for brass casting industry(Taylor & Francis Ltd, 2010) Sakalli, Umit Sami; Baykoc, Omer Faruk; Birgoren, BurakThis article discusses a possibilistic aggregate production planning (APP) model for blending problem in a brass factory; the problem computing optimal amounts of raw materials for the total production of several types of brass in a planning period. The model basically has a multi-blend model formulation in which demand quantities, percentages of the ingredient in some raw materials, cost coefficients, minimum and maximum procurement amounts are all imprecise and have triangular possibility distributions. A mathematical model and a solution algorithm are proposed for solving this model. In the proposed model, the Lai and Hwang's fuzzy ranking concept is relaxed by using 'Either-or' constraints. An application of the brass casting APP model to a brass factory demonstrates that the proposed model successfully solves the multi-blend problem for brass casting and determines the optimal raw material purchasing policies.Öğe A simulated annealing approach for reliability-based chance-constrained programming(Wiley, 2014) Sakalli, Umit SamiThe chance-constrained programming (CCP) is a well-known and widely used stochastic programming approach. In the CCP approach, determining the confidence levels of the constraints at the beginning of solution process is a critical issue for optimality. On one hand, it is possible to obtain better solutions at different confidence levels. On the other hand, the decision makers prefer to simplify their choices instead of grappling with the details such as determining confidence levels for all chance constraints. Reliability is an effective tool that enables the decision maker to look over the system integrity. In this paper, the CCP is considered as a reliability-based nonlinear multiobjective model, and a simulated annealing (SA) algorithm is developed to solve the model. The SA represents different solution alternatives at the different reliability degrees to the decision makers by performing different confidence levels. Thus, the decision makers have the opportunity to make more effective decisions. Copyright (C) 2013 John Wiley & Sons, Ltd.Öğe Stochastic optimization for blending problem in brass casting industry(Springer, 2011) Sakalli, Umit Sami; Baykoc, Omer Faruk; Birgoren, BurakA critical process in brass casting is blending of the raw materials in a furnace so that the specified metal ratios are satisfied. The uncertainties in raw material compositions may cause violations of the specification limits and extra cost. In this study, we proposed a chance-constrained stochastic programming approach for blending problem in brass casting industry to handle the statistical variations in raw material compositions. The proposed approach is a non-linear mathematical model that is solved global optimally by using GAMS/BARON solver. An application has been performed in MKEK brass factory in KA +/- rA +/- kkale, Turkey and the solution of the application has been compared with alternative solution approaches based on cost and specification violation risk conditions. This comparison demonstrates that the proposed model is the most effective solution approach for managing stochastic uncertainties in blending problems and successfully can be used other industries such as alloy steel or secondary aluminum production.
