Browsing by Author "Ekren, Banu Yetkin"

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    Assessing supply chain innovations for building resilient food supply chains: an emerging economy perspective
    (MDPI, 2023-03-09) Joshi, Sudhanshu; Sharma, Manu; Ekren, Banu Yetkin; Kazancoglu, Yigit; Luthra, Sunil; Prasad, Mukesh
    Food waste reduction and security are the main concerns of agri-food supply chains, as more than thirty-three percent of global food production is wasted or lost due to mismanagement. The ongoing challenges, including resource scarcity, climate change, waste generation, etc., need immediate actions from stakeholders to develop resilient food supply chains. Previous studies explored food supply chains and their challenges, barriers, enablers, etc. Still, there needs to be more literature on the innovations in supply chains that can build resilient food chains to last long and compete in the post-pandemic scenario. Thus, studies are also required to explore supply chain innovations for the food sector. The current research employed a stepwise weight assessment ratio analysis (SWARA) to assess the supply chain innovations that can develop resilient food supply chains. This study is a pioneer in using the SWARA application to evaluate supply chain innovation and identify the most preferred alternatives. The results from the SWARA show that ‘Business strategy innovations’ are the most significant innovations that can bring resiliency to the food supply chains, followed by ‘Technological innovations.’ The study provides insights for decision makers to understand the significant supply chain innovations to attain resilience in food chains and help the industry to survive and sustain in the long run.
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    Sustainable e-grocery home delivery: an optimization model considering on-demand vehicles
    (Elsevier, 2025-03) Tudisco, Vittoria; Perotti, Sara; Ekren, Banu Yetkin; Aktas, Emel
    The e-grocery sector has experienced a significant boost since the COVID-19 pandemic, dramatically changing consumer buying behaviours. As demand for faster and more efficient delivery options grows, e-grocery retailers face increasing pressure to optimize home delivery operations. Collaborations with third-party logistics providers (3PLs), although still overlooked, have emerged as promising, offering operational flexibility and environmental benefits. This work introduces an optimization model that supports the design of an on-demand delivery fleet conjunctly with delivery routings and schedules, while considering both cost and environmental impact. To this aim, a vehicle routing problem with time windows (VRPTW) is extended to incorporate on-demand fleet design and three different objective functions embodying a cost-efficient, an environmentally-effective and a cost-environmental balanced perspective respectively. Numerical experiments based on an Italian case study show that prioritizing environmental objectives reduces emissions by over 90%, with marginal increases in annual costs. Besides, on-demand vehicles enable flexibility that facilitates the adoption of sustainable delivery options without requiring challenging investments such as delivery fleet. Several contributions are provided: insights into using on-demand vehicles are proposed; a mathematical model jointly optimizing fleet design and delivery routing and scheduling, while considering both costs and environmental objectives, is developed and its practical application is demonstrated using real-world data. The findings highlight the significant impact of environmental considerations on fleet composition and operational efficiency, offering actionable strategies for e-retailers to reduce emissions while maintaining service quality.
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    Transaction processing policies in a flexible shuttle-based storage and re-trieval system by real-time data tracking under agent-based modelling
    (Ram Arti Publishers, 2023-12-31) Ekren, Banu Yetkin
    This study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttle-based storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles be-tween tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data track-ing-based scheduling policies in automated warehousing systems.