TEKSTIL VE KONFEKSIYON

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

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY

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

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME

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

JOURNAL OF MANUFACTURING SYSTEMS

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

INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING

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

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME

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

STUDIES IN INFORMATICS AND CONTROL

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