Browsing by Author "Ilgin, MA"

Now showing 1 - 16 of 16
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    Physical Programming: A Review of the State of the Art
    Ilgin, MA; Gupta, SM
    Most traditional multi-criteria optimization techniques require that the decision maker construct an aggregate objective function using the weights determined as a result of a trial and error process. Physical programming (PP) eliminates this tedious weight assignment process by providing decision makers with a flexible and more natural problem formulation. In PP, the decision maker specifies ranges of different degrees of desirability instead of defining weights. In this paper, we present a comprehensive review of PP studies by classifying them into four major categories (viz., methodological papers, industrial engineering applications, mechanical engineering applications and other applications). Insights from the review and future research directions conclude the paper.
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    Advances in partial disassembly line balancing: A state-of-the-art review
    Güler, E; Kalayci, CB; Ilgin, MA; Özceylan, E; Güngör, A
    As sustainable manufacturing practices increasingly prioritize the efficient and environmentally conscious disassembly of products, understanding the nuances of partial disassembly line balancing (PDLB) becomes essential. In our study, we conducted an in-depth analysis of 53 PDLB studies, published from 2008 to 2024, to understand the current trends and gaps in this domain. Our research methodology involved a systematic collection of material, descriptive analysis, careful category selection, and rigorous material evaluation. This approach enabled us to construct a comprehensive taxonomy tailored to PDLB, distinguishing it from classical disassembly line balancing (DLB) literature. Our findings reveal a noticeable surge in PDLB research in the last three years, reflecting its growing relevance in sustainable manufacturing. The study highlights that deterministic disassembly conditions, economic performance indicators, and iterative heuristic approaches are the focal points in recent PDLB research. This insight is critical for guiding future investigations in the field. Our taxonomy not only categorizes existing research in PDLB but also sheds light on under-explored areas, offering a clear direction for upcoming studies. This paper contributes to the literature by providing a detailed review of PDLBspecific elements such as problem characteristics, disassembly levels, objectives, constraints, and solution methods, thus paving the way for further advancements in sustainable manufacturing practices.
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    Use of MCDM techniques in environmentally conscious manufacturing and product recovery: State of the art
    Ilgin, MA; Gupta, SM; Battaïa, O
    Increasing environmental awareness of customers and stricter environmental regulations by local governments force manufacturers to invest in environmentally conscious manufacturing which involves the application of green principles to all phases of a product's life cycle from conceptual design to final delivery to consumers, and ultimately to the end of life (EOL) disposal. They also setup facilities for product recovery which is the recovery of materials and components from returned or EOL products via disassembly, recycling and remanufacturing. To address these new issues efficiently, multi criteria decision making (MCDM) techniques are used in order to evaluate the economic and environmental indicators. This paper presents over 190 MCDM studies in environmentally conscious manufacturing and product recovery (ECMPRO) by classifying them into three major categories. Insights from the review and future research directions conclude the paper. (C) 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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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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    A DEMATEL-Based Disassembly Line Balancing Heuristic
    Ilgin, MA
    Circular economy has emerged as a response to increasing environmental problems. As opposed to linear economy, circular economy aims at the preservation of energy, material, and labor contents of used products. A critical process in circular economy is product recovery which involves the recovery of materials or components from returned products through various recovery options including recycling, refurbishing, and remanufacturing. All recovery options require some level of disassembly and disassembly operations that are generally carried out in a disassembly line. Like assembly lines, disassembly lines must be balanced in order to ensure the effective operation of the line. Mathematical programming techniques, metaheuristics, and various heuristic procedures were employed in order to solve different types of disassembly line balancing problem (DLBP). However, the use of multi-attribute decision making techniques is limited to few studies. in this study, we propose a DEMATEL-based disassembly line balancing approach which does not require extensive knowledge in operations research and computer programming. A solution can be obtained by carrying out basic matrix operations and following the steps of the approach. Two numerical examples are also provided in order to present the applicability of the proposed approach. The results indicate that the proposed approach presents a satisfactory performance compared to the previously proposed approaches.
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    A part grouping-based approach for disassembly sequencing
    Güçdemir, H; Ilgin, MA
    Disassembly sequencing involves the determination of the best sequence of operations while dismantling a product and it has utmost importance in effective planning of disassembly systems. On the other hand, sequencing of disassembly operations becomes more complicated when the product involves high number of parts. In this concern, we propose a novel part grouping-based disassembly sequencing approach that integrates data clustering and sequencing rules. First, parts of an end-of-life product are grouped based on various characteristics by using k-means algorithm. Then, the disassembly sequence is obtained by using a simulation-based heuristic procedure which considers part groups, precedence relationships and direction changes. The applicability of the proposed approach is tested by applying it to an exemplary product structure and it is concluded that grouping of parts prior to sequencing provides satisfactory results in various performance measures.
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    Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions
    Edis, EB; Ilgin, MA; Edis, RS
    Due to the acceleration of technological developments and shortening of product life cycles, product recovery has gained great importance in recent years. Disassembly line balancing (DLB) problem is one of the most important problems encountered during disassembly operations in product recovery. In this study, a single model and complete DLB problem with balancing issues, hazardousness of parts, demand quantities and direction changes is considered. Majority of DLB studies in the literature solve this problem using heuristics or metaheuristics which do not guarantee the optimality. Although a few studies present mathematical formulations for some variants of this problem, they prefer to solve the problem by using heuristics or metaheuristics due to the non-linear structure and combinatorial nature of the problem. In this study, a generic mixed integer linear programming (MILP) model is developed for the investigated problem and its performance is tested through a series of benchmark instances. The computational results demonstrate that the proposed MILP model is able to solve test instances with up to 30 tasks. Hence, it can effectively be utilized to evaluate the optimality performance of DLB approaches. Moreover, several extensions on the MILP model regarding to line balancing, hazardousness and demand of parts and direction changes are proposed and their effects are analyzed through computational studies. (c) 2019 Elsevier Ltd. All rights reserved.
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    An integrated methodology for the used product selection problem faced by third-party reverse logistics providers
    Ilgin, MA
    The rise of electronic commerce and the stricter government regulations on the recovery of end-of-life products increased the number of products returned by customers. A company may deal with increasing product returns by developing its own reverse logistic system. However, high fixed costs associated with dedicated reverse logistics equipment and infrastructure force many companies to outsource their reverse logistics operations to third-party reverse logistics providers. One of the important problems faced by third-party reverse logistics providers is the selection of the types of used products to be collected. In this study, a four-phase used product selection methodology is proposed. First, the quantitative and qualitative selection criteria are determined. Then the weights of the criteria are calculated using fuzzy analytic hierarchy process. Next, simulation models are employed to determine the values of quantitative criteria. Finally, technique for order preference by similarity to ideal solution ranks alternative used product types. A numerical example is also provided in order to present the applicability of the proposed methodology.\
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    A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing
    Tasoglu, G; Ilgin, MA
    Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.
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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.
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    Integrating Linear Physical Programming and Fuzzy Programming for the Management of Third Party Reverse Logistics Providers
    Ilgin, MA
    Shorter product lifecycles, more liberal return policies and the rise of internet marketing increased the amount of product returns in recent years. Companies must have a well-managed reverse logistics system to ensure the timely and cost-effective collection, processing and disposal of returned products. However, high fixed cost of reverse logistics infrastructure and high level of uncertainty associated with product returns force companies to outsource their reverse logistics operations to third party reverse logistics providers (3PRLPs). The success of outsourcing largely depends on the selection of suitable 3PRLP(s). Although there are many 3PRLP evaluation methodologies, the research on the determination of order quantities from 3PRLPs considering uncertainties associated with budget allocation and capacity is very limited. In addition, the previous studies do not allow decision makers to express their preferences for 3PRLP selection criteria in a physically meaningful way. This study fills these research gaps by proposing a novel 3PRLP evaluation methodology which integrates linear physical programming (LPP) and fuzzy programming (FP). First, an LPP model is constructed based on decision makers' preferences and alternative 3PRLPs are ranked according to their total LPP scores. Then, an FP model takes total LPP scores, budget allocation and capacity constraints as input and determines the number of returned products to be processed by each 3PRLP. A numerical example is also provided to illustrate the feasibility and practicality of the proposed method. The results from this example are analyzed by considering the effects of capacity and budget limitations on order quantities and several managerial insights are proposed.
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    Sewing Machine Selection Using Linear Physical Programming
    Ilgin, MA
    Sewing is a critical operation in garment production process. Therefore, alternative sewing machines must carefully be evaluated prior to procurement. Multiple criteria decision making (MCDM) techniques can effectively be used in sewing machine evaluation process since multiple evaluation criteria including speed and price must be considered. However, physically meaningless subjective weights are assigned to evaluation criteria in most MCDM techniques. Linear Physical Programming (LPP) is a MCDM methodology that eliminates this subjective weight assignment process by allowing decision makers to express their preferences in a physically meaningful way. In this study, a sewing machine selection problem faced by a textile company is solved using LPP.
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    A SPARE PARTS CRITICALITY EVALUATION METHOD BASED ON FUZZY AHP AND TAGUCHI LOSS FUNCTIONS
    Ilgin, MA
    Effective and efficient operation of a manufacturing system highly depends on the timely and correct implementation of maintenance operations. One of the most important factors affecting the successful implementation of maintenance operations is the determination of suitable inventory control policies for maintenance spare parts. Effective spare parts inventory management requires the criticality evaluation of spare parts. In this study, a novel spare parts criticality evaluation approach is proposed First, the evaluation criteria are determined based on literature review and expert opinion and Fuzzy Analytical Hierarrhy Process (AHP) is used to determine the criteria weights. Next, Taguchi loss functions and simulation modeling are employed for the calculation of loss values for the spare parts. Finally, a criticality ranking of the spare parts is obtained based on the weighted loss values which are calculated using criteria weights and loss values. The applicability of the proposed approach was tested by applying it to a spare part criticality evaluation problem faced by a manufacturing company.
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    Simultaneous Determination of Disassembly Sequence and Disassembly-to-Order Decisions Using Simulation Optimization
    Ilgin, MA; Tasoglu, GT
    Strict environmental regulations and increasing public awareness toward environmental issues force many companies to establish dedicated facilities for product recovery. All product recovery options require some level of disassembly. That is why, the costeffective management of product recovery operations highly depends on the effective planning of disassembly operations. There are two crucial issues common to most disassembly systems. The first issue is disassembly sequencing which involves the determination of an optimal or near optimal disassembly sequence. The second issue is disassembly-to-order (DTO) problem which involves the determination of the number of end of life (EOL) products to process to fulfill the demand for specified numbers of components and materials. Although disassembly sequencing decisions directly affects the various costs associated with a disassembly-to-order problem, these two issues are treated separately in the literature. In this study, a genetic algorithm (GA) based simulation optimization approach was proposed for the simultaneous determination of disassembly sequence and disassembly-to-order decisions. The applicability of the proposed approach was illustrated by providing a numerical example and the best values of GA parameters were identified by carrying out a Taguchi experimental design.
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    Mixed integer programming approaches to partial disassembly line balancing and sequencing problem
    Edis, EB; Edis, RS; Ilgin, MA
    Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e. g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems.