Browsing by Author "Kucukvar, Murat"

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Citation - WoS: 5
Citation - Scopus: 7
A Memetic Algorithm for the Bi-Objective Quadratic Assignment Problem
(ELSEVIER SCIENCE BV, 2019) Cemre Cubukcuoglu; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Liang Gao; Murat Kucukvar; Tasgetiren, M. Fatih; Kucukvar, Murat; Sariyildiz, I. Sevil; Fatih Tasgetiren, M.; Cubukcuoglu, Cemre; Sevil Sariyildiz, I.; Gao, Liang; CH Dagli; GA Suer
Recently multi-objective evolutionary algorithms (MOEAs) have been extensively used to solve multi-objective optimization problems (MOPs) since they have the ability to approximate a set of non-dominated solutions in reasonable CPU times. In this paper we consider the bi-objective quadratic assignment problem (bQAP) which is a variant of the classical QAP which has been extensively investigated to solve several real-life problems. The bQAP can be defined as having many input flows with the same distances between the facilities causing multiple cost functions that must be optimized simultaneously. In this study we propose a memetic algorithm with effective local search and mutation operators to solve the bQAP. Local search is based on swap neighborhood structure whereas the mutation operator is based on ruin and recreate procedure. The experimental results show that our bi-objective memetic algorithm (BOMA) substantially outperforms all the island-based variants of the PASMOQAP algorithm proposed very recently in the literature. (C) 2019 The Authors. Published by Elsevier Ltd.
Carbon Footprint of Food Production: A Systematic Review and Meta-Analysis
(Nature Portfolio, 2025-10-13) Onat, Nuri C.; Kucukvar, Murat; Kazançoğlu, Yiğit; Jabbar, Rateb; Al-Quradaghi, Shimaa; Al-Thani, Soud; Mandouri, Jafar
In the face of the urgent climate crisis, food production is a significant contributor to greenhouse gas emissions (GHG). We analyzed 118 life-cycle assessment (LCA) studies on GHG emissions of food production, considering LCA methods, life cycle phase, waste inclusion, and regional factors, including country, continent, and development status. Additionally, machine learning analysis identifies influential factors of GHG emissions of food production across seven categories: red meats, seafood, white meat, fruits & vegetables, animal products, other plant-based, and others (oils). Based on the gradient boosting algorithm, the LCA method choice ranks among the top determinants for GHG emissions in animal products, red meat, seafood, other plant-based products, and others food categories. Only 22% of studies include waste, revealing up to 39% higher emissions in some categories compared to those excluding waste. Our meta-analysis presents min-max-average GHG emission results for each food category, within countries, different scope settings, waste considerations, and LCA methods.
Citation - WoS: 3
Citation - Scopus: 3
Carbon Footprint of Food Production: A Systematic Review and Meta-Analysis
(Nature Portfolio, 2025-10-13) Onat, Nuri C.; Kucukvar, Murat; Kazançoğlu, Yiğit; Jabbar, Rateb; Al-Quradaghi, Shimaa; Al-Thani, Soud; Mandouri, Jafar
In the face of the urgent climate crisis, food production is a significant contributor to greenhouse gas emissions (GHG). We analyzed 118 life-cycle assessment (LCA) studies on GHG emissions of food production, considering LCA methods, life cycle phase, waste inclusion, and regional factors, including country, continent, and development status. Additionally, machine learning analysis identifies influential factors of GHG emissions of food production across seven categories: red meats, seafood, white meat, fruits & vegetables, animal products, other plant-based, and others (oils). Based on the gradient boosting algorithm, the LCA method choice ranks among the top determinants for GHG emissions in animal products, red meat, seafood, other plant-based products, and others food categories. Only 22% of studies include waste, revealing up to 39% higher emissions in some categories compared to those excluding waste. Our meta-analysis presents min-max-average GHG emission results for each food category, within countries, different scope settings, waste considerations, and LCA methods.
Citation - WoS: 23
Citation - Scopus: 29
Investigating the role of stakeholder engagement for more resilient vaccine supply chains during COVID-19
(Springer, 2022-04-26) Yigit Kazancoglu; Muruvvet Deniz Sezer; Melisa Ozbiltekin-Pala; Murat Küçükvar; Ozbiltekin-Pala, Melisa; Sezer, Muruvvet Deniz; Kucukvar, Murat; Kazancoglu, Yigit
The complexity of the supply chains and the uncertainties in the processes cause business to become more vulnerable in the face of disruptions. Pandemic situations such as COVID-19 cause sudden disruptions in supply chains causing processes to be disrupted. Especially in multi-stakeholder supply chains the importance of stakeholder communication motivation and regulations i.e. comes to the forefront in order to ensure the resilience of supply chains. As learned with the COVID-19 pandemic vaccine supply chains are also one of the multi-stakeholder supply chains and are extremely vulnerable to disruptions. In COVID-19 times the importance of vaccine supply chain management and the resilience in vaccine supply chains increased. To have more resilient vaccine supply chains stakeholder engagement is an essential issue. Therefore the Graph Theory Matrix Approach has been used to determine factors of stakeholder engagement in multi-stakeholder vaccine supply chains and to specify the relationships between the factors of project and stakeholder engagement in vaccine supply chains to increase resilience in disruption times. The aim of the study is to identify the factors of project and stakeholder engagement that are necessary to ensure the resilience of multi-stakeholder vaccine supply chains and not be affected by disruptions such as COVID-19 as it is today. As a result of the study innovativeness of stakeholders is the most important factor of stakeholder engagement in vaccine supply chains. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 5
Transforming Challenges into Opportunities for Qatar’s Food Industry: Self-Sufficiency Sustainability and Global Food Trade Diversification
(MDPI, 2023-03-25) Noora Al-Abdelmalek; Murat Küçükvar; Nuri Cihat Cihat Onat; Enas Fares; Hiba Anis Ayad; Muhammet Enis Bulak; Banu Yetkin Yetkin Ekren; Yigit Kazancoglu; Kadir Ertogral; Kucukvar, Murat; Onat, Nuri C.; Al-Abdelmalek, Noora; Fares, Enas; Bulak, Muhammet Enis; Ertogral, Kadir; Ayad, Hiba
Food trade restrictions pose a serious risk for countries that are heavily reliant on food imports potentially leading to food crises inequality and geopolitical conflicts on a global scale. However such restrictions may also have transformative effects in promoting food supply chain resilience security and self-sufficiency. In this study a novel econometric analysis is presented utilizing a data-driven analytical model to investigate the impact of a food embargo on the industry using Qatar as a case study. A structured and automated food trade database is created using Microsoft Management Server Studio and data visualization software is integrated for automated data discovery. By using a global trade-based sustainability assessment model which combines the multi-region input-output (MRIO) analysis with transportation mode-based (sea road and air) emissions the carbon footprint of the dairy food production sector could be estimated. The study shows that the trade embargo on Qatar’s food industry can lead to significant reductions in the annual import of food products promoting self-sufficiency and reducing the net carbon emissions of the dairy food sector by nearly 40%. This reduction is not only achieved through food supply chain changes such as transportation modes but also by restrictions pushing the country to increase domestic production. Overall the study demonstrates that a trade embargo with the support of a well-designed national food security strategy trade/import diversification and the use of different modes of transportation for food products can improve the resilience of global supply chains self-sufficiency and environmental sustainability. © 2023 Elsevier B.V. All rights reserved.