Logistics

Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters. Full article

Background: In northern countries, spring requires the removal of large volumes of abrasive materials used in winter road maintenance. This sweeping process, crucial for safety and environmental protection, has traditionally relied on conventional mechanical brooms. Recent technological innovations, however, have introduced more efficient and environmentally friendly sweeping solutions; Methods: This study provides a comprehensive comparative analysis of the environmental and operational performance of these innovative sweeping systems versus conventional methods. Using simulation models based on real-world data and integrating fuel consumption models, the analysis replicates sweeping behaviors to assess both operational and environmental performance. A sensitivity analysis was conducted using these models, focusing on key parameters such as the collection rate, the number of trucks, the payload capacity, and the truck unloading duration; Results: The results show that the innovative sweeping system achieves an average 45% reduction in GHG emissions per kilometer compared to the conventional system, consistently demonstrating superior environmental efficiency across all resources configurations; Conclusions: These insights offer valuable guidance for service providers by identifying effective resource configurations that align with both environmental and operational objectives. The approach adopted in this study demonstrates the potential to develop decision-making support tools that balance operational and environmental pillars of sustainability, encouraging policy decision-makers to adopt greener procurement policies. Future research should explore the integration of advanced technologies such as IoT, AI-driven analytics, and digital twin systems, along with life cycle assessments, to further support sustainable logistics in road maintenance. Full article

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters. Full article

Background: One of the ongoing challenges in freight transport operations is to balance efficiency, effectiveness, and sustainability through the integration of sustainable practices to minimize the environmental impact. When it comes to truck payload and sustainability, the emphasis is on optimizing space, and minimizing empty miles and the wastage of resources. Ensuring that truck loads meet their targets has many challenges, and our empirical research examines the factors influencing the payloads of recycled fibre across the network in the UK paper industry. Methods: A mixed method approach includes interviews, business process analysis, the identification of opportunity areas, a site visit, simulation, and viability analysis to assess factors as part of the sustainable solution. Results: The research identified aspects related to processes, data availability and fragmentation, consistent procedures, practices, and operational considerations. Refining cage-loading procedures, enhancing baling processes and the visibility of upstream processes, and establishing robust information-sharing mechanisms improve efficiency and support sustainability. Conclusions: The empirical research extends the knowledge related to freight efficiency movements on the road and focuses on practical actions in utilizing recycled fibre’s carrying capacity. Full article

Background: Urban mobility management faces growing challenges that require the analysis and optimization of sustainable solutions. Digital twins (DTs) have emerged as innovative tools for this assessment, but their implementation requires standardized procedures and languages; Methods: As part of a broader methodology for continuous DT validation, this study focuses on the conceptual validation phase, presenting a conceptualization approach through formalization using Specification and Description Language (SDL), agnostic to simulation tools. The conceptual validation was achieved through stakeholder engagement in the Bolzano context, producing 41 SDL diagrams that define both elements common to different urban realities and specific local data collection procedures; Results: The feasibility of implementing this stakeholder-validated conceptualization was demonstrated using Simulation of Urban MObility (SUMO) for traffic simulation and optimization criteria calculation, and its framework SUMO Activity GenerAtion (SAGA) for generating an Activity-Based Modeling (ABM) mobility demand that can be improved through real sensor data; Conclusions: The SDL approach, through its graphical representation (SDL/GR), enables conceptual validation by enhancing stakeholder communication while defining a framework that, while adapting to the monitoring specificities of different urban realities, maintains a common and rigorous structure, independent of the chosen implementation tools and programming languages. Full article

Background: This study introduces a novel supply chain management (SCM) model tailored for the hospital industry in Thailand. The model emphasises the integration of third-party logistics (3PL) providers to streamline supply-processing-distribution (SPD) functions. By outsourcing non-core activities like SPD to 3PL providers, hospitals can enhance their operational efficiency, allowing healthcare professionals to focus on core tasks and ultimately improving service delivery. Methods: This research employed a dual methodology, combining an analytic hierarchy process (AHP) with a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). These approaches evaluated various SCM models based on multiple hospital logistics performance attributes. Results: The AHP results highlighted on-time delivery, patient safety, utilisation rate, and emergency procurement as critical criteria for selecting the optimal model. Fuzzy TOPSIS analysis identified the SCIII: W-G-H model as the most suitable for implementation in Thai hospitals. This model incorporates a centralised warehouse for negotiation leverage, a Group Purchasing Organisation (GPO) for cost efficiency, and regional SPD hubs for effective inventory management and rapid responses to demand fluctuations or emergencies. Conclusions: Adopting this SCM model is expected to significantly enhance supply chain performance, reduce operational costs, and improve the quality and safety of patient care in Thai hospitals. Full article

Background: The sustainable development of mountain areas, which have fragile ecosystems, has increasingly attracted the attention of researchers and practitioners. Logistics systems are crucial in supporting these regions and addressing mountainous terrain’s unique challenges. While many studies have examined aspects of mountain logistics, a comprehensive and systematic review of the field is still lacking. Design/Methodology/Approach: This paper aims to fill the gap by systematically reviewing the existing literature on mountain logistics using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Results/Conclusions: We identify four main research foci: design of logistics infrastructure or vector, optimization of logistics systems, safety in logistics systems, and impact of logistics systems on mountain communities. In addition to categorizing these themes, we conduct a detailed descriptive analysis of published studies in this domain. Our findings highlight significant research gaps, particularly in integrating digital technologies, sustainable mass transportation solutions, and logistics systems’ socioeconomic and environmental impacts. We propose targeted directions for future research to advance sustainable logistics practices in mountain regions. Full article

Background: Automated vehicles are increasingly entering logistics operations, driven by factors like controllability, standardization, and reduced risk. However, successful automation requires understanding the diverse perspectives of logistics stakeholders. Method: This paper investigates these perspectives through 28 interviews with representatives from five key stakeholder groups within the European Project AWARD’s four pilot sites. Results: Key findings highlight positive expectations for efficiency, safety, and reliability, but also identify critical prerequisites still to be met: further technological advancements, shifts in logistics roles and working conditions, regulatory improvements, and careful narrative building around technology. Conclusions: A deeper analysis of individual stakeholder groups and pilot site representatives reveals nuanced needs and concerns, emphasizing the importance of considering different perspectives and need for further research involving a wider range of stakeholder groups to fully capture the complexities of this emerging field. Full article

Background: The rise in natural and man-made disasters has increased the need for effective search-and-rescue tools, particularly in resource-limited areas. Unmanned Aerial Vehicles (UAVs) are increasingly used for this purpose due to their flexibility and lower operational costs. However, finding the most efficient paths for these UAVs remains a challenge, as it is essential to maximize victim location and minimize mission time. Methods: This study presents an autonomous UAV-based approach for identifying victims, prioritizing high-risk areas and those needing urgent medical attention. Unlike other methods focused solely on minimizing mission time, this approach emphasizes high-risk zones and potential secondary disaster areas. Using a partially observable Markov decision process, it simulates victim detection through an image classification algorithm, enabling efficient and independent operation. Results: Experiments with real data indicate that this approach reduces risk by 66% during the mission’s first half while autonomously identifying victims without human intervention. Conclusions: This study demonstrates the capability of autonomous UAV systems to improve search-and-rescue efforts in disaster-prone, resource-constrained regions by effectively prioritizing high-risk areas, thereby reducing mission risk and improving response efficiency. Full article

Background: Supply chain performance (SCP) is impacted by complexity brought about by static and dynamic drivers. This study aims to investigate the effects of supply chain complexity (SCC) on SCP and ascertain whether additive manufacturing best practices have moderating effects on this relationship. Methods: Using data from 29 Ethiopian footwear industries and 205 respondents, the relationship established in the theoretical framework was validated using structural equation modelling (SEM). Results: The study’s findings provided several important insights. First, upstream supply chain complexity (USSCC), midstream supply chain complexity (MSSCC), and downstream supply chain complexity (DSSCC) negatively affect SCP. Second, additive manufacturing best practices have significant moderation effects between supply chain complexity and supply chain performance. Third, the negative impacts of USSCC and MSSCC on SCP are reduced at a higher level of additive manufacturing adaptation. The findings of this study also revealed that the effects of DSSCC on SCP have no difference at both low and high levels of additive manufacturing best practices. Conclusions: This work offers the first empirical investigation to which the detrimental effects of SCC on SCP are mitigated or improved through the moderating role of additive manufacturing best practice. Full article

Background: Amid growing global uncertainty and increasingly complex disruptions, the ability of supply chains to rapidly adapt and recover is critical. The incorporation of artificial intelligence (AI) into supply chain management represents a transformative strategy for enhancing resilience. By harnessing advanced AI technologies, such as machine learning, predictive analytics, and real-time data processing, organizations can more effectively anticipate, respond to, and recover from disruptions.AI improves demand forecasting accuracy, optimizes inventory management, and increases real-time visibility across the supply chain, reducing the risks of stockouts and surplus inventory. Furthermore, I-driven automation and robotics enhance operational efficiency by minimizing human error and streamlining processes. Methodology/Approach: This paper proposes a conceptual framework for strengthening supply chain resilience through AI integration. The framework leverages AI technologies to improve key aspects of supply chain resilience, including risk management, operational efficiency, and real-time visibility. Result/Conclusions: Additionally, it underscores the importance of collaborative relationships with supply chain partners, enabled by AI-powered data-sharing and communication tools that foster trust and coordination within the network. Originality/Value: This comprehensive framework offers a strategic approach to integrating AI into supply chain management, highlighting its potential to significantly enhance resilience, operational efficiency, and sustainability, thereby empowering organizations to navigate the complexities of modern supply chains more effectively. Full article

Background: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited. This article aims to investigate the potential effects of AR technology on organizational performance through the mediation role of SCM and logistics value chain functions to address the existing knowledge gap. Methods: This research employed a cross-sectional design and an explanatory survey as a deductive approach for hypothesis development. The primary data collection method involved the self-administration of a questionnaire to furniture suppliers located in the Gulf Cooperation Council (GCC), including six countries. Of the 656 questionnaires submitted to suppliers, 483 were considered usable, yielding a response rate of 73.6%. The research utilized partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques to evaluate the gathered data. Results: The current paper’s statistical evidence demonstrates that AR implementation has a positive impact on the supply and logistics value chain activities and organizational performance of furniture suppliers in the GCC region. Moreover, it illustrates that the design and planning variable of supply chain value dominates as the primary predictor of organization performance. The results indicated that the ANN strategy provided a more comprehensive explanation of internally generated constructs compared to the PLS-SEM technique. Conclusions: This study demonstrates its usefulness by advising furniture industry decision-makers on what to avoid and what aspects to consider when creating plans and regulations. The report also suggests operations managers apply machine learning (ANN) for prediction and decision-making in supply and operations value chains. This essay looks at how the AR and resource-based supply value chain view may affect company performance across countries, firm sizes, and ages. Full article

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains. Full article

Background: Circular supply chains (CSCs) aim to minimize waste, extend product lifecycles, and optimize resource efficiency, aligning with the growing demand for sustainable practices. Machine learning (ML) can potentially enhance CSCs by improving resource management, optimizing processes, and addressing complexities inherent in CSCs. ML can be a powerful tool to support CSC operations by offering data-driven insights and enhancing decision-making capabilities. Methods: This paper conducts a systematic literature review, analyzing 66 relevant studies to examine the role of ML across various stages of CSCs, from supply and manufacturing to waste management. Results: The findings reveal that ML contributes significantly to CSC performance, improving supplier selection, operational optimization, and waste reduction. ML-driven approaches in manufacturing, consumer behavior forecasting, logistics, and waste management enable companies to optimize resources and minimize waste. Integrating ML with emerging technologies such as IoT, blockchain, and computer vision further enhances CSC operations, fostering transparency and automation. Conclusions: ML applications in CSCs align with broader sustainability goals, contributing to environmental, social, and economic sustainability. The review identifies opportunities for future research, such as the development of real-world case studies further to enhance the effects of ML on CSC efficiency. Full article

Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m³. CO₂ emissions were measured at 487.7 kg/m³. The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production. Full article

Background: Warehousing operations, crucial to logistics and supply chain management, often seek innovative technologies to boost efficiency and reduce costs. For instance, AR devices have shown the potential to significantly reduce operational costs by up to 20% in similar industries. Therefore, this paper delves into the pivotal role of smart glasses in revolutionising warehouse effectiveness and efficiency, recognising their transformative potential. However, challenges such as employee resistance and health concerns highlight the need for a balanced trade-off between operational effectiveness and human acceptance. Methods: This study uses scenario and regression analyses to examine data from a German logistics service provider (LSP). Additionally, structured interviews with employees from various LSPs provide valuable insights into human acceptance. Results: The findings reveal that smart glasses convert dead time into value-added time, significantly enhancing the efficiency of order picking processes. Despite the economic benefits, including higher profits and competitive advantages, the lack of employee acceptance due to health concerns still needs to be addressed. Conclusions: After weighing the financial advantages against health impairments, the study recommends implementing smart glass technology in picking processes, given the current state of technical development. This study’s practical implications include guiding LSPs in technology adoption strategies, while theoretically, it adds to the body of knowledge on the human-technology interface in logistics. Full article

Background: Over the last years, the assessment and selection of suppliers, based on the environmental performance of their products/services and their operations, has reached paramount importance and attracted the interest of many researchers and practitioners. Based on the prevailing perspective of supplier selection as a purely decision-making problem, this interest has been channeled towards the development of decision-support methods and tools. Other broader issues, such as whether there are converging or diverging green supplier evaluation and selection organizational processes across industries has not been addressed. Methods: Here, for the first time, we address this question by adopting a systems perspective and by considering green supplier evaluation and selection as an organizational sub-process of the broader sourcing process. We use activity theory to represent green supplier evaluation and selection as two interconnected activities, each comprising a set of organizational practices. Based on this representation, we developed a research instrument to carry out empirical research in a sample of 80 companies from five industries (pharmaceuticals, food processing, aquaculture, construction materials, waste management and recycling) in Greece. Results: The results of the survey suggest that green supplier evaluation and selection practices do not fully converge, but there are differences across industries. Conclusions: The cultural and historical context of industries influences the adoption of specific environmental supplier evaluation and selection practices. Full article

Background: The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. Methods: This paper provides a narrative review of current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Drawing from recent studies and case reports, the review highlights the role of technologies such as artificial intelligence (AI)-driven navigation systems, real-time monitoring, and secure payload management in mitigating logistical barriers like traffic congestion and geographical isolation. Results: Based on findings from various case studies, the review demonstrates how drones can significantly reduce transportation time and costs, while improving accessibility to healthcare services in underserved areas. Conclusions: This paper concludes that, while challenges such as regulatory hurdles and privacy concerns remain, ongoing technological advancements and the development of supportive regulatory frameworks have the potential to revolutionize medical logistics, ultimately improving patient outcomes and healthcare delivery. Full article

Background: The supplier selection process (SSP) has grown as a crucial mechanism in organizations’ supply chain management (SCM) strategies and as a foundation for continuously gaining a competitive advantage. The concept of the circular economy has garnered significant interest due to its ability to address both environmental and social criteria. It is highly important to carefully choose suppliers across all industries that take into account circular and sustainability issues, as well as traditional criteria. There is very limited research involving the supplier selection process in the Indian HVAC manufacturing sector. Design/Methodology/Approach: Thus, this study aimed to determine the critical factors for sustainable supplier selection for HVAC manufacturing firms using a mixed research method with three stages: a secondary study, the Delphi method, and the fuzzy analytical hierarchy process (FAHP). Thirty-two critical sub-factors were identified and grouped into eight major factors: delivery, economic, environmental, social, management and organization, quality, services, and supplier relationship. Results/Conclusions: For HVAC manufacturing firms, the major factors of delivery, quality, and economics were found to be top-ranked among the factors, followed by environmental factors. Studies in developing countries using sustainable factors are still nascent, especially in India. Originality/Value: This study’s novelty lies with the proposed eight major factors, comprising all facets of organizations, including sustainability factors. Supplier selection in HVAC manufacturing firms is exhaustively dealt with in this study, filling a gap in the existing literature. This is important because HVAC products are high-energy-consuming, high-energy-releasing, and costly. Full article