Browsing by Author "Araz, C"

Now showing 1 - 6 of 6
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    AN INTEGRATED FUZZY APPROACH FOR DETERMINING ENGINEERING CHARACTERISTICS IN CONCRETE INDUSTRY
    Ertay, T; Akyol, DE; Araz, C
    This paper deals with the modeling of conceptual knowledge to capture the major customer requirements effectively and to transform these requirements systematically into the relevant design requirements. Quality Function Deployment (QFD) is a well-known planning and problem-solving tool for translating customer needs (CNs) into the engineering characteristics (ECs) and can be employed for this modeling. In this study, an integrated methodology is presented to rank ECs for implementing QFD in a fuzzy environment. The proposed methodology uses fuzzy weighted average method as a fuzzy group decision making approach to fuse multiple preference rankings for determining the weights of the customer needs. It adopts a fuzzy Analytic Network Process (ANP) approach which enables the consideration of inner dependencies in a cluster as well as the interdependencies between the clusters to determine the importance of ECs. The proposed approach is illustrated through a case study in ready-mixed concrete industry.
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    A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT
    Demir, L; Akpinar, ME; Araz, C; Ilgin, MA
    Depleting natural resources and limited amount of landfill areas have forced many governments to impose stricter measures on environmental performance. In order to comply with those measures and to have a better environmental image, companies are investing heavily in environmental, social and economic responsibility issues. Moreover, they continuously track the environmental performance of their suppliers. Many green supplier evaluation and ranking methodologies were proposed in the literature in order to assist companies in the environmental performance evaluation of suppliers. However, the number of studies on the sorting of suppliers based on environmental criteria is very limited. In this study, we fill this research gap by proposing a novel VIKOR-based green supplier sorting methodology called VIKORSORT. This methodology evaluates the environmental performance of suppliers and sorts them into the predefined ordered classes. The proposed methodology can easily be embedded into an expert system which can suggest a suitable green supplier development program for each class. (C) 2018 Elsevier Ltd. All rights reserved.
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    Fuzzy demand-driven material requirements planning: a comprehensive analysis of fuzzy logic implementation in DDMRP
    Araz, OU; Ilgin, MA; Eski, O; Araz, C
    Demand-Driven Material Requirements Planning (DDMRP) is a new method of inventory control designed to address the challenges of today's complex and volatile supply chain environment. DDMRP replaces traditional Material Requirements Planning (MRP) with a more agile and responsive approach that is based on a set of colour-coded buffers. DDMRP ensures that the right inventory is in the right place at the right time, enabling companies to respond quickly and effectively to changing customer demand. DDMRP requires the setting of various parameters, such as variability factor (VF) and lead time factor (LTF), which can have a significant impact on systems' performance. Pre-assumed fixed values of DDMRP parameters are used in most of the existing studies. This may lead to either high stockout levels or excess inventory especially in environments involving high level of variability. In this study, we proposed a fuzzy logic-based approach which dynamically adjusts the values of VF and LTF parameters considering demand and lead time variability. The effectiveness of the proposed approach was investigated by comparing its performance with DDMRP based on several numerical experiments. The results showed that the proposed Fuzzy Demand-Driven Material Requirements Planning (FDDMRP) outperforms DDMRP in terms of backorder rate and total cost.
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    Determining the number of kanbans for dynamic production systems: An integrated methodology
    Uzun Araz, Ö; Araz, C; Eski, Ö
    Just-in-time (JIT) is a management philosophy that reduces the inventory levels and eliminates manufacturing wastes by producing only the right quantity at the right time. A kanban system is one of the key elements of JIT philosophy. Kanbans are used to authorize production and to control movement of materials in JIT systems. In Kanban systems, the efficiency of the manufacturing system depends on several factors such as number of kanbans, container size etc. Hence, determining the number of kanbans is a critical decision in Kanban systems. The aim of this study is to develop a methodology that can be used in order to determine the number of kanbans in a dynamic production environment. In this methodology, the changes in system state is monitored in real time manner, and the number of the kanbans are dynamically re-arranged. The proposed methodology integrates simulation, neural networks and Mamdani type fuzzy inference system. The methodology is modelled in simulation environment and applied on a hypothetic production system. We also performed several comparisons for different control policies to show the effectiveness of the proposed methodology.
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    A REACTIVE SCHEDULING APPROACH BASED ON FUZZY INFERENCE FOR HYBRID FLOWSHOP SYSTEMS
    Araz, OU; Eski, O; Araz, C
    Hybrid flowshops consist of multiple production stages each of which has multiple parallel machines. Scheduling of hybrid flowshops is a NP-hard even in its simplest form. The presence of uncertainty in real-world problems forces the decision makers to reconsider their scheduling decisions in reactive manner. In this study, we proposed a proactive-reactive scheduling approach which allows to be changed dispatching rule set applied in time. The methodology consists of three parts: Shop Floor Management system with a triggering mechanism based on fuzzy inference system, performance prediction of the alternative dispatching rule sets based on Taguchi design, simulation, artificial neural networks, and a multi-criteria decision making methodology for determining new scheduling dispatching rule set. The proposed approach is applied on a real world problem from literature and the results are compared with static approach.
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    Disassembly line balancing using linear physical programming
    Ilgin, MA; Akçay, H; Araz, C
    Disassembly is the separation of a product into its constituent parts in a systematic way. It has gained importance recently due to its vital importance in product recovery. Cost-effective implementation of disassembly operation has a direct impact on the profitability of product recovery activities (recycling, remanufacturing etc.). Although it is possible to carry out disassembly operations in a disassembly station or in a disassembly cell, the highest productivity is achieved in a disassembly line. The output of a disassembly line can be maximised only if the line is balanced. A linear physical programming-based disassembly line balancing method is proposed in this study. This method was used to balance a mixed-model disassembly line and the effectiveness of the method was illustrated by analysing the results.