Emrah Demir
@cardiff.ac.uk
Cardiff Business School
Cardiff University
RESEARCH, TEACHING, or OTHER INTERESTS
Management Science and Operations Research, Transportation, Management Information Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sajjad Hedayati, Mostafa Setak, Emrah Demir, and Tom Van Woensel
Oxford University Press (OUP)
Abstract Accepted by: M. Zied Babai The clustered and generalized vehicle routing problem (CGVRP) extends the well-known vehicle routing problem by grouping the demand points into multiple distinct zones, and within each zone, further separation is made by forming clusters. The objective of the CGVRP is to determine the optimal routes for a fleet of vehicles dispatched from a depot, visiting all zones within each cluster. This requires making two simultaneous optimization decisions. Firstly, each zone must be visited by exactly one node, and secondly, all zones within a cluster must be visited by the same vehicle. In this paper, we introduce two mixed-integer linear programming formulations for the CGVRP, aimed at solving a joint order batching and picker routing problem with alternative locations in a warehouse environment featuring mixed-shelves configuration. Both formulations are tested on three scenarios of randomly generated small- and medium-sized instances. Additionally, we propose a general rule approach for calculating a cost matrix in a rectangular environment. The results demonstrate the effectiveness of the proposed mathematical formulations in efficiently solving problems with up to 180 nodes.
David Lai, Yasel Costa, Emrah Demir, Alexandre M. Florio, and Tom Van Woensel
Elsevier BV
Xinyue Hao and Emrah Demir
Emerald
Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.
Ba Luat Le, Layla Martin, Emrah Demir, and Duc Minh Vu
Springer Nature Singapore
Xinyue Hao and Emrah Demir
Informa UK Limited
Zhuowu Zhang, Emrah Demir, Robert Mason, and Carla Di Cairano-Gilfedder
Springer Science and Business Media LLC
AbstractDespite the significant impact of driver behavior on fuel consumption and carbon dioxide equivalent (CO2e) emissions, this phenomenon is often overlooked in road freight transportation research. We review the relevant literature and seek to provide a deeper understanding of the relationship between freight drivers’ behavior and fuel consumption. This study utilizes a real-life dataset of over 4000 driving records from the freight logistics sector to examine the effects of specific behaviors on fuel consumption. Analyzed behaviors include harsh acceleration/deceleration/cornering, over-revving, excessive revolutions per minute (RPM), and non-adherence to legal speed limits ranging from 20 to 70 miles per hour (mph). Our findings confirm existing literature by demonstrating the significant impact of certain driving characteristics, particularly harsh acceleration/cornering, on fuel consumption. Moreover, our research contributes new insights into the field, notably highlighting the substantial influence of non-adherence to the legal speed limits of 20 and 30 mph on fuel consumption, an aspect not extensively studied in previous research. We subsequently introduce an advanced fuel consumption model that takes into account these identified driver behaviors. This model not only advances academic understanding of fuel consumption determinants in road freight transportation, but also equips practitioners with practical insights to optimize fuel efficiency and reduce environmental impacts.
D. G. Mogale, Xun Wang, Emrah Demir, and Vasco Sanchez Rodrigues
Springer Science and Business Media LLC
AbstractSupply Chains (SCs) are becoming more vulnerable to disruption risks because of globalisation, competitiveness, and uncertainties. This study is motivated by an online grocery retailer in the UK that experienced multiple disruption risks, such as demand and supply shocks, facility closures, and disruption propagation simultaneously in 2020. The main purpose of this study is to model and perform quantitative analyses of a range of SC disruption risks affecting the UK online retailer. We have attempted to study how UK retailers responded to the first and second waves of the pandemic and the effect on multiple products. Six scenarios are developed based on SC disruption risks and their impacts on SC performance are analysed. The quantitative analysis of two strategies used by grocery retailers during the pandemic, namely vulnerable priority delivery slots and rationing of products, illustrates that rationing of products had a greater SC impact than the use of priority delivery slots. The effects of two resilience strategies, backup supplier and ramping up distribution centre capacity, are also quantified and discussed. Novel managerial insights and theoretical implications are discussed to make online grocery SC more resilient and robust during future disruptions.
David Lai, Yijun Li, Emrah Demir, Nico Dellaert, and Tom Van Woensel
Elsevier BV
M. Amine Masmoudi, Leandro C. Coelho, and Emrah Demir
Elsevier BV
Emrah Demir, Aris Syntetos, and Tom van Woensel
Oxford University Press (OUP)
Abstract Aspiring green agendas in conjunction with tremendous economic pressures are resulting in an increased attention to the environment and technological innovations for improving existing logistics systems. Last mile logistics, in particular, are becoming much more than a consumer convenience necessity and a transportation optimization exercise. Rather, this area presents a true opportunity to foster both financial and environmental sustainability. This paper investigates recent technological advancements and pending needs related to business and social innovations, emphasizing green logistics and city logistics concepts. We discuss various pertinent aspects, including drones, delivery robots, truck platooning, collection and pickup points, collaborative logistics, integrated transportation, decarbonization and advanced transport analytics. From a mathematical perspective, we focus on the basic features of the vehicle routing problem and some of its variants. We provide recommendations around strategies that may facilitate the adoption of new effective technologies and innovations.
Davood Mohammadi, Mohamed Abd Elaziz, Reza Moghdani, Emrah Demir, and Seyedali Mirjalili
Springer Science and Business Media LLC
Cheng Chen and Emrah Demir
Springer International Publishing
Cheng Chen, Emrah Demir, and Yuan Huang
Elsevier BV
Emna Marrekchi, Walid Besbes, Diala Dhouib, and Emrah Demir
Springer Science and Business Media LLC
Hamid Tikani, Mostafa Setak, and Emrah Demir
Elsevier BV
Emrah Demir, Daniel Eyers, and Yuan Huang
Elsevier BV
Cheng Chen, Emrah Demir, Yuan Huang, and Rongzu Qiu
Elsevier BV
Hamid Tikani, Mostafa Setak, and Emrah Demir
Elsevier BV
Vasco Sanchez Rodrigues, Emrah Demir, Xun Wang, and Joseph Sarkis
Elsevier BV
Martin Hrušovský, Emrah Demir, Werner Jammernegg, and Tom Van Woensel
Elsevier BV
Reza Moghdani, Khodakaram Salimifard, Emrah Demir, and Abdelkader Benyettou
Elsevier BV
Reza Moghdani, Khodakaram Salimifard, Emrah Demir, and Abdelkader Benyettou
Elsevier BV
Reza Eshtehadi, Emrah Demir, and Yuan Huang
Elsevier BV
Mohamed Amine Masmoudi, Manar Hosny, Emrah Demir, and Erwin Pesch
Springer Science and Business Media LLC
Mohamed Amine Masmoudi, Manar Hosny, and Emrah Demir
Wiley
This chapter presents a new extension of the Dial‐a‐Ride Problem (DARP), in which a fleet of Alternative Fuel Vehicles (AFVs) is considered. Due to the limited driving range, the AFVs may visit some Alternative Fuel Stations to be refueled with a partial refueling quantity during its journey to serve all users' demands. The proposed variant is called the Green DARP (G‐DARP). The chapter introduces a linear mixed‐integer mathematical formulation of the G‐DARP, and proposes an efficient Adaptive Large Neighborhood Search heuristic algorithm to solve the G‐DARP. The algorithm is supported by efficient local search operators to enhance the search and improve the quality of solutions, as well as a flexible acceptance function to more explore the search space. The chapter also presents numerical experiments to demonstrate that the solution approach provides high‐quality solutions for newly generated instances.