Purpose

While Industry 4.0 draws on a technology-centered approach, Industry 5.0 emphasizes resilience, sustainability, and human-centricity as key components of value chains supported by technological advancements (Golovianko et al. 2023). One guiding principle of Industry 5.0 is to capture the value of new technologies while prioritizing humans' well-being, where technology should serve humans, rather than the other way around (Kolade and Owoseni 2022). The purpose of this study is to examine the impact of individual human linguistic differences in goal-oriented visual search tasks which is typical for technology assisted production settings. This would support an enhanced understanding of human factor issues on operations performance in production and logistics settings, paving the way for human-centric workplace and process design with superior performance as a win-win-objective (Casla et al. 2019, Lu et al. 2022).

Methodology

We conduct an online experiment with two groups of participants. One group learned German as the first native written language and the second group learned Japanese. Both groups perform three goal-oriented visual search tasks with low, medium, and high visual clutter (stimuli). In our visual search tasks, participants are asked to select one correct shelf location among nine shelve locations. Repeating each trial twice, we arrive at 2 (observation groups) x 3 (stimuli) x 2 (repetitions) equaling 12 trials in total where we add 6 neutral trials and 6 empty trials where participants see a fixation cross. We collect information about the time required to select the correct field and the error rate when selecting one field. Time to select and the probability of error are our two dependent variables.

Findings

The linguistic relativity hypothesis proposes that the language we speak affect the way we think. We expect interesting findings when comparing participants involved in reading different script systems, such as alphabetic German versus logographic Japanese scripts. Representatives of alphabetic scripts are known for using analytical models of cognitive processes, concentrating on objects and their characteristics, while representatives of logographic scripts process information more holistically.

Originality

Previous research has reached wide consensus that a human-centric design of production operations systems is crucial, encompassing social, physical, and cognitive human factors (Loske et al., 2024; Klumpp, 2018). Relative to the literature on cognitive human factors and executed tasks, e.g., performance improvements through learning by doing, differences in cognitive processing stemming from a particular culture have received little attention in the production and operations management literature. Our study proposes that culture-specific mental functioning is important to consider in an Industry 5.0 environment in order to achieve process improvements and human well-being at the same time in production and logistics operations.

References

Casla et al. (2019).

Golovianko et al. (2023).

Klumpp (2018).

Kolade & Owoseni (2022).

Loske et al. (2024).

Lu et al., (2022).

Port decarbonization is an important part of worldwide efforts to combat climate change and move to a more sustainable economy. This study investigates how port decarbonization measures directly support the creation of green employment, therefore benefiting both environmental sustainability, economic growth and social welfare.

Ports emit considerable amounts of greenhouse gases mainly as they rely on fossil fuels for cargo handling and transportation. The move to decarbonization at ports entails implementing green technology such as electrification, renewable energy integration, and enhanced emission control methods (Botana Lagarón et al., 2022). Implementing these technologies cuts emissions and generates new employment possibilities in renewable energy generation, energy efficiency management, and environmental monitoring (Alamoush et al., 2023).

The requirement for specialized personnel to develop, install, and maintain new technologies and infrastructures contributes to generate green jobs. For example, the use of cold ironing at ports needs the installation and maintenance by electrical engineers and technicians (Bosich et al., 2023). Furthermore, ports that invest in green hydrogen production for fuel transition help to create jobs in the hydrogen sector (Pivetta et al., 2022). Additionally, incorporating renewable energy sources, such as solar and wind power, into port operations, creates jobs in the installation and maintenance of these systems. Ports that shift to electric cars and equipment need trained personnel in electric mobility technology (Issa Zadeh et al., 2023). Innovations such as digital twins and simulation technologies improve the efficiency of port operations while lowering their carbon impact, creating jobs in the technology industry (Wu et al., 2022).

Case studies show that ports that implement sustainable practices have effectively decreased their carbon footprint while fostering economic growth and job creation (Botana Lagarón et al., 2022). Decarbonizing ports requires extensive training and upskilling of the existing workforce to handle new technology and procedures, resulting in long-term job possibilities and career advancement. Green jobs not only contribute to reaching environmental goals but also to improve the quality of life for individuals and their communities (Alamoush et al., 2020). Ultimately, decarbonizing ports is critical for meeting global climate targets and creating green jobs. The incorporation of modern green technology into port operations not only decreases environmental effect but also generates major job possibilities, therefore contributing to economic and environmental sustainability.

This study seeks to understand the economic and societal implications of port decarbonization, with a focus on green job creation. By examining economic benefits such as employment opportunities in renewable energy and environmental management, we investigate how decarbonization activities boost local economies and create long-term jobs. The societal advantages, such as improved public health, higher community involvement, and increased social welfare, highlight the overall benefits of switching to green technology. We provide valuable recommendations for policies to boost green job creation, as well as targeted investments and regulatory frameworks to help with the transition to a low-carbon economy. Finally, this study demonstrates that port decarbonization not only satisfies environmental goals, but also promotes economic growth and social well-being, presenting a strong argument for broader community support for green projects.

Ethiopia's apparel sector has seen a surge in export-led growth and ranks among countries like China and Bangladesh in industrial output and sourcing destinations. Industrial zones are being constructed to attract large-scale suppliers and buyers, accelerate export-oriented industrialisation, and foster linkages between domestic assemblers and foreign suppliers. The participation of domestic suppliers in the global supply chain system can ensure the sustainability of economic development and supply chain integration through key strategic partnership development. Regardless of industrial policy, government commitment, and remarkable economic growth, the country has achieved little economic, technological, and knowledge transformation due to the weak participation of domestic suppliers. This led us to question the reasons behind weak participation and challenges for domestic suppliers to enter and remain competitive in the global apparel supply chain (SC) networks. Moreover, the author is interested in understanding how international suppliers’ presence and linkages impact the performance and participation of apparel producers. Research on the context of developing nation suppliers' capability to penetrate the global SC challenges and business orientation towards creating a brand image is limited. This study investigates the reasons behind weak participation and challenges for domestic suppliers in global apparel SC networks to help the emerging economy design resilient policies. This study used random sampling techniques to investigate the participation of domestic suppliers in global SC networks. Dillman's (2000) research procedures were used to collect data from primary and secondary sources. The research is conceptualised using the absorptive capacity theory, as it fits the understanding of how weak domestic supplier participation inhibits supply chain integration and efficiency. The study was analysed using R software. Despite an integrated value chain approach and sustainable industrialization, no single operational domestic firm exists in the 26 industrial parks. The analysis showed that 77.78% of firms have domestic market motives. And 68% of them believe that their products are competitive in domestic markets in terms of price, supply, and quality. The analysis further revealed that 23% of domestic apparel and textile suppliers have market and profit orientations, while 8% are interested in forming branded firms. The weak participation of domestic suppliers in the standard industry parks was negatively correlated with the significance of participating in the global supply chain, r(27) = -0.526**, p<0.001. The significant participation of domestic suppliers in the global supply chain system significantly predicted the weak participation of domestic suppliers in the standard industry parks (b = -.618, t(25) = -3.091, p<.001). This work enhances SC integration and sustainability by providing insights for suppliers and academic concepts, highlighting the role of domestic suppliers in global supply chains, economic contribution, and competitiveness. Furthermore, this study shows the importance of domestic supplier participation in developing robustness against SC disruptions. Domestic suppliers are important sources of sustained economic development. Encouraging private investment is recommended for long-term prosperity. Policies should enhance productivity and competitiveness by linking foreign enterprises with domestic firms, promoting technological transfer, spillover effects, and managerial skill transfer, and requiring continuous monitoring and evaluation.

Introduction

The benefits of RoRo shipping in terms of quicker turnaround times compared to container transport are apparent, primarily attributed to higher departure frequencies and reduced dwell times in terminals (Woxenius and Bergqvist 2011). Addressing bottlenecks is crucial for achieving these efficiencies. Despite limited coverage in the literature, simulation studies of RoRo terminal operations are lacking (Abourraja et al. 2023). However, the disclosure of the modelling of terminal processes as a basis for simulation studies has so far only been carried out to a very limited extent. This study aims to comprehensively delineate current terminal processes in the Baltic Sea region, facilitating simulation studies and digital integration initiatives (Santos and Santos 2024).

Methodology

The research framework of Design Science Research (DSR) (Peffers et al. 2008) guided this study. A literature review was conducted to identify prevalent approaches to modeling operational processes within RoRo terminals and ports. Additionally, interviews were conducted with various stakeholders in the RoRo sector to understand current terminal processes. Twenty-three interviews were conducted initially, involving players in the sector. BPMN was employed for modeling, followed by validation of BPMN models in five feedback sessions involving sector participants.

Findings:

The findings reveal a macro-level model illustrating the interconnections among distinct functional areas within RoRo terminals, categorized by accompanied and unaccompanied transport in import and export contexts. This model forms the foundation for four additional models delineating import and export processes, further subdivided based on accompanied and unaccompanied transport. It offers insights into the operational engagements of individual stakeholders and their mutual interactions.

Discussion:

The delineation of individual process steps across all terminal stakeholders can serve as a foundation for operational simulation studies and facilitate the integration of digital methodologies to enhance terminal performance. Previously, there was no comparable, nuanced process delineation within the RoRo/RoPax sector. A deductive approach was employed to outline an archetypical terminal operation process, adaptable to specific terminals, thus providing a framework for simulation studies and digital solution integration. Additionally, depicting these processes fosters comprehension of the diverse stakeholders in the RoRo sector.

Conclusion

The study investigates current operational protocols at RoRo terminals in the Baltic Sea region, laying the groundwork for conducting simulation studies and integrating digital methodologies to improve operational efficiency. The results produce a detailed macro-level model illustrating interdependencies among functional domains, allowing for customization to meet individual terminal needs. This model can be utilized in future research to identify key performance indicators (KPIs) and subsequently integrated into simulation models. Additionally, it can be expanded to encompass aspects of passenger transport that have not yet been examined..

Research at the nexus of manufacturing operations and sustainability is becoming increasingly popular because of growing concerns about climate change, ground and water pollution, local and regional impacts on air quality, and the effects of increased industrial activity on community health and safety (Drake & Spinler, 2013; Corbett & Kleindorfer, 2001). Empirical academic research on sustainable operations management (SOM) was collected and analyzed in this article. Using a systematic review of the literature, the study investigated 102 articles and books on SOM that were published between 1994 and 2023. Elkington (1994) and Gupta and Sharma (1996) are two significant articles from the mid-1990s that the study included. SOM received more attention as a result of these important contributions to the integration of sustainability aspects in OM. (1) The definitions of SOM, (2) the practices of SOM, and (3) the empirical results of SOM practices on sustainable performance were the primary concerns that have guided the analysis of this study. The 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) approach was used for reports. To locate research that is eligible, four methods were used. First, to ensure that a wide range of scientific output is covered, the study initially performed an electronic search in two databases, Scopus and SCimago. Second, In order to determine how sustainability is integrated into operations, the study examined SOM journal articles that were published in five of the leading journals for operations management, including the International Journal of Operations and Production Management (five articles) and the Journal of Cleaner Production (nine articles). Third, to locate pertinent material, the study searched Google books and related web databases. Finally, the study looked through internet databases to find relevant conference proceedings. The features of the eligible studies found and the analysis to the above research objectives have been then presented systematically. The systematic literature review revealed that several researchers have empirically examined the relationship between some aspects of SOM practices and one or more of the dimensions of manufacturing firms' sustainable performance; however, no clear findings have been obtained thus far. Considering this discovery, the study suggested that future research bridges this knowledge gap by conducting a comprehensive analysis of the connection between SOM practices and sustainable performance. Moreover, two significant findings from the systematic literature review have been emphasized. On the one hand, manufacturing companies should take part in triple bottom line (economic, environmental, and social) issues to attain sustainability in their operations. SOM, on the other hand, broadens the firm's scope. It includes not just implementing internal SOM practices (such as sustainable product design and manufacturing processes) that improve the firm's sustainability performance but also extending sustainable practices to the supply chain that supports the firm's performance in sustainability. Besides, the study offered a widely comprehensive definition of SOM. Additionally, analysis and conclusions about how SOM practices explain business success were made from the systematic literature review. This report also identified methodological and theoretical limitations and provides several recommendations for further research.

In response to the escalating challenges of last-mile delivery (LMD) due to various factors such as urbanization and the rise of e-commerce, this study explores the innovative integration of autonomous delivery robots within a Van-and-Robot (VnR) system, exemplified by the BeIntelli project.

In recent years, the integration of fully or semi-autonomous vehicles has attracted considerable attention. Technological innovations are propelling the logistics industry toward the adoption of autonomous delivery robots for LMD. This approach aims to enhance operational efficiency through optimized routing and extended operational hours, reduce costs primarily by lowering labor expenses, and promote sustainability through the use of electric vehicles. However, the integration of autonomous delivery robots into urban logistics faces multiple challenges, including limitations in their range or capacity. The conceptual framework that combines vans with robots offers a promising solution to extend the operational capabilities of robots and utilize vans as mobile depots, directly addressing the challenges of LMD. Despite the growing interest from both industry and academia there is currently a noticeable gap in the conceptualization and practical implementation of this concept, even though the necessary technologies are increasingly accessible. This study aims to broaden its research focus to include the technical design and development of such a VnR system, specifically within the context of the BeIntelli research project.

Employing a qualitative morphological analysis, including a morphological box and cross-consistency matrix, the research delineates feasible VnR configurations. This methodology, enriched by patent review, identifies essential categories and variants for the VnR system. For this concept, the integration into the logistics process takes precedence over specific technical construction details. Collaboration with experts, including developers of the delivery robot, the autonomous driving stack for the transporter, and integrators of the vehicle's material flow system, is crucial in constructing the cross-consistency matrix to determine viable combinations. A specific, project-constrained variant is developed and executed as a proof-of-concept within the BeIntelli project, showcasing the practical application of the theoretical framework.

The study aims to contribute to urban logistics innovation, emphasizing the importance of structured analytical methodologies and collaborative development for the advancement of autonomous delivery solutions. The implementation of constructive projects could be enhanced by a conceptual framework, facilitating detailed and practical investigations into the challenges described. Additionally, the utility is derived from the definition and identification of subcomponents and process steps within the concept, enhancing understanding of its constituents and, consequently, informing the design of the concept. Through the BeIntelli project, this research not only presents a viable implementation of the VnR system but also sets a precedent for future exploration in enhancing urban delivery systems.

There is plenty of research on the vehicle routing problem (VRP) and its application in last mile logistics. For home deliveries with an in-person handover offering time windows (TW) increases customer satisfaction and first attempt delivery rate. In general, it can be stated that the smaller the time windows the higher the successful first attempt delivery rate. To improve the predicted delivery time and keep the time windows offered traffic and congestions risks can be integrated in the VRP. In the logistics context most analyses focus on the B2C and B2B market with small package deliveries. At the customer location a fixed or stochastically chosen stop time is used.

In the field of service management the technician routing and scheduling problem (TRSP) considers varying services times at the destination. Also here, time windows have to be considered.

As the e-commerce market is expanding in terms of product portfolio heavier and bulky items like furniture and household appliances are added to the online stores. At the same time the vendors add services like final assembly or installation at the customers site to increase customer satisfaction. Due to the size and weight of the items to be delivered and the additional services offered the stop (and service) time at the customer site is longer and therefore relevant for an adapted VRP-TW.

The scope of this research is to analyze the processes at the customer location in more detail with a special focus on the process steps beginning with the search for a parking spot to unloading and the delivery of multiple items into the customers apartment or house. The objective is to identify patterns and indicators that allow for a more accurate prediction of the total stop time. One of the hypotheses is that deliveries in the city center take longer due to limited parking spaces which result in longer walking distances combined with multi-story buildings with narrow staircases compared to deliveries in suburban areas where the delivery van can park in the driveway.

To identify these patterns and indicators delivery service teams of a logistics service provider have been equipped with motion tracking devices. Based on the Motion Mining technology by MotionMiners which have been successfully applied in the context of manual warehouse picking process analysis the motions are classified into basic activities (e.g. walking, carrying, unloading, documentation) from which the duration of the individual process steps can be derived. Linking the empirical data with the logistics aspects of the delivery task (e.g. number of pieces, volume, weight, address, ZIP code) allows for a stop time prediction of similar delivery tasks in the future.

Overall, the results enable a more precise transport planning which could on the one hand optimize the utilization and workload of the delivery crews and on the other hand increase customer satisfaction as more accurate delivery time windows can be offered.

Dairy services are considered as sources of income, employment, nutrition and health for the smallholder rural and urban farmers. The main objective of this study is to assess the inter linkage of dairy farm and small-scale dairy processing in Mekelle, Tigray. To achieve the stated objective, a descriptive research approach was employed where data was collected from 45 dairy farmers and 40 small-scale processors and analyzed through calculating the mean values and percentages. Findings show that dairy business in the study area is characterized by shortage of feed and water for the farm. The dairy farm is dominated by breeds of hybrid type, followed by the so called ‘begait’. Though the farms have access to medication and vaccination to the cattle, they fell short of hygiene practices, reliable shade for the cattle and separate space for the claves. The value chain at milk production stage is characterized by low production rate, selling raw milk without adding value and a very meager traditional processing practice. Furthermore, small-scale milk processors are characterized by collecting milk from farmers, producing cheese, butter, ghee and sour milk. They do not engage in modern milk processing like pasteurized milk, yoghurt and table butter. Most small-scale milk processors are engaged in traditional production system. Additionally, milk consumption and marketing part of the chain are dominated by the informal market (channel) where market problem, lack of skill and technology, shortage of loan and weak policy support are being faced as the main challenges. Based on the findings, recommendations and future research areas are forwarded.

The investigation is to examine intra-organizational supply chain relationships of firms which will help to look and give an emphasis for internal processes and operations strength and achievements to make an influence even for external relationship management and outstanding performances of organizations. The purpose of the study is to scrutinize the internal supply chain relationships with in manufacturing companies located in Tigray. The qualitative and quantitative data analysis methods were employed during the study by applying the primary data sources (questionnaires & interviews) and secondary data sources (organizational reports and documents) with the purposive sampling method. Thus, a descriptive research design was also applied in the research project in line with the cross-sectional research design which portrays simply the magnitude of the issues and problems by collecting the required and necessary data once from the sample respondents. This is because, the study variables don’t have any cause and effect relationship in the research project that requires other type of research design than a descriptive research design, it already needs to be assessed and analyzed with the detail description of the results after quantifying the outcomes and degree of the issues and problems based on the data gathered from respondents. The collected data was also analyzed by using the statistical package for social sciences (SPSS Version 20). The intra-organizational relationships of the companies are moderately accomplished which requires an improvement for enhancing the performances of each units or departments within the firms so as to upgrade and ensuring progress of the companies’ effectiveness and efficiency. Moreover, the manufacturing companies have low industrial discipline and working culture, weak supervision of man power, delayed delivery in the process with in the companies, unsatisfactory quality of products, underutilization of capacity, low productivity and profitability which in turn results to minimize the performance of intra-organizational supply chain relationships and to reduce the companies’ organizational efficiency, effectiveness and sustainability. Hence, the companies should have to give an emphasis to build and manage effectively the intraorganizational supply chain relationships because nothing can be done without creating a successful and progressive relationships with an internal units or functional areas and individuals for the production and provision of the required and qualified products which permits to meet the intended customers’ desires. The study contributes to improve the practical applications and to give an emphasis on the policy measurements and implications of the manufacturing companies with regard to intra-organizational supply chain relationships.

Manufacturing industries have a great role in the development of the world’s economies. However, the relationship with their suppliers and customer are faced with myriad of challenges which, along with the increased competition on the global market and extended supply chains, impact their overall performance. This study was conducted to scrutinize and analyze the challenges of manufacturing firms’ relationship with suppliers and customers in Tigray region of Ethiopia. Data was collected from primary and secondary data sources through questionnaires, interviews, and organizational survey and review of reports. Both qualitative and quantitative approaches were employed to analyze, make discussion, and interpret the data. The results show that low accomplishments have been reported by the manufacturing firms in managing their relationship with suppliers and customers such as low commitment, building unreliable suppliers, low feedback and low quality of information sharing, and adoption of unautomated information and communication technology. Furthermore, the companies didn’t give much attention and have feeble execution in following up customer requests and interests in time, providing consistent services, looking into the future and long-term business opportunity, responsiveness and cooperation which in turn lead to reduced satisfaction of customers, lower demand of the products and decreasing the sales volume, falling profitability and productivity of the companies. Hence, it is recommended that the companies make an improvement and progresses on the accomplishment and adoption of good practices of supplier and customer relationship, which may help them become more effective and efficient. Making frequent communication, maintaining high degrees of trust and commitment, dependency and cooperation, and developing supplier base by providing training may help the companies have better, successful and longer relationships.

Supply Chain Management (SCM) provides an integrated, cross-organizational perspective on all business processes, encompassing key areas such as procurement, production, distribution, marketing, and controlling. These areas are crucial to the value chain, directly influencing efficiency and competitiveness. Optimizing them is essential for long-term business success. In this context, machine learning (ML) and deep learning (DL)-based artificial intelligence (AI) models offer advanced tools for enhancing these processes through data analysis and processing. Although advanced AI technologies and algorithms have become theoretically accessible to enterprises of all sizes through open-source software, the practical implementation of these sophisticated models remains a significant challenge.

There is a significant research gap in the area of practical applications of AI models in SCM that complicates a clear understanding of specific use cases. This paper provides an overview of current and potential AI techniques, focusing on experimental studies, case studies, and practical problem-solving approaches that can enhance SCM.

A Multivocal Literature Review was conducted, incorporating formal literature from databases such as ScienceDirect and Scopus, as well as grey literature from sources like Google. The search process followed an adapted approach based on Denyer and Tranfield (2009) and integrated elements of the PRISMA framework (Page et al., 2021) to ensure quality and transparency. Identification of grey literature followed the method pruposed by Garousi et al. (2018). The Google search was restricted to PDF files, and the top 200 search results were reviewed. In total, 1,454 reports were examined, with 121 included in the qualitative analysis.

The results reveal that the most frequently employed AI technique is from the field of deep learning, specifically artificial neural networks in various forms (FFNN, CNN, RNN), which were utilized in 71% of the reviewed literature and applied across all investigated SCM domains. These deep learning approaches significantly contribute to quality management, pattern recognition, reliability analysis, demand forecasting, fault diagnosis, and improvements in risk management and resilience.

This study provides important insights into the challenges and motivation in AI adoption. A central challenge, particularly within SCM environments, is the necessary collaboration among multiple stakeholders and the management of heterogeneous data sources. Additional challenges include trust in AI outcomes, concerns about data security, issues in change management, and the lack of suitable frameworks for AI adoption and governance. The motivations for AI implementation are diverse, with performance optimization being a primary focus. Additionally, ecological motivations, such as promoting sustainability in production, and enhancing resilience are also noted.

This paper provides a novel perspective on SCM and emphasizes the critical role of AI.

Global manufacturing companies need to incorporate environmental, social, and economic criteria to enhance their competitiveness and sustainability. These metrics are becoming increasingly important. The movement of commodities, the provision of services, and logistical processes are all areas in which supply chain operations have a substantial impact on the environment. Important strategic decisions, particularly in carrier and mode of transportation selection, significantly impact the efficiency of logistics. Beneficial Cargo Owners (BCOs), or the companies whose goods are being delivered, must carefully examine several variables when choosing a carrier, including cargo security, equipment availability, transit times, and freight costs. These decisions are crucial because they have an immediate impact on customer satisfaction and operational effectiveness. To track cargo and manage inventory, BCOs mostly rely on real-time data. This allows them to guarantee accurate and timely deliveries, which improves the overall customer experience and satisfaction levels. In response to global imperatives and stakeholder expectations, manufacturing giants are increasingly acting upon sustainability mandates reflected in their reports. These actions include managing shipment volumes, adopting alternative energy sources, and actively pursuing strategies to mitigate emissions across their supply chains. The quest for more effective strategies at the strategic level is driving BCOs to embrace innovative approaches such as the implementation of multimodal freight transportation models. Through technology integration, service quality, and environmental impact factors, carrier selection criteria are optimized in this manner. The goal of the approach is to help BCOs achieve more sustainability and efficiency in their supply chain operations by emphasizing the relationship between technology, service quality, and environmental responsibility. They can minimize their environmental impact while optimizing operations through strategic decisions in carrier selection and logistics. Companies that adopt such models are positioned strategically to satisfy changing customer expectations for environmentally friendly operations and to comply with industry standards and regulatory regulations. Examining these important parameters in detail, this study suggests an all-encompassing, sustainable multimodal freight transportation optimization model. Two integrated decision support mechanisms are included in the model: a route generation mechanism that generates different routes based on the limitations of the BCO and a multi-criteria decision-making system that chooses the most appropriate carrier. At this stage, a mixed-integer programming model is created to produce multimodal transportation routes after a multi-criteria decision-making analysis is completed on the established carrier selection criteria. Based on the outcomes of the multi-criteria decision-making study, the model's parameters and decision variables are assigned. As a future direction of the study, an application phase using case study data will be carried out to validate the generic model that is intended to be fitted to operating BCOs in various sectors. As a result, this study positions itself to provide a way forward for improved operational effectiveness, lower expenses, and more customer satisfaction in the ever-changing world of international trade.

During outbound rail transportation from seaport terminals, containers are secured to the rail wagons using foldable pins to prevent slipping. In seaport terminals, depending on the container train loading scheme, the container pins must be flipped up or down before commencing container loading operations. The research project "Pin-Handling-mR" aims to automate this process using a mobile robot. The automation is expected to enhance the safety of terminal staff and reduce operational costs for terminal operators. A key challenge to enable this automation is the accurate recognition and pixel wise segmentation of container pins, that is essential for precise robotic manipulation. Deep learning frameworks can be leveraged to accurately detect, classify and segment container pins. However, such frameworks require high-quality annotated data for training, and to the best of author’s knowledge, no publicly sourced container-pin dataset exists to train and test a deep learning image segmentation network for real time container pin segmentation.

This study focuses on the evaluation of the state-of-the-art deep learning based models to detect and segment the pins with better accuracy. It proposes the creation of a Red-Green-Blue-Depth (RGB-D) container pin dataset, featuring two types of container pins, and the development of a deep learning-based object segmentation pipeline. The dataset is generated using images from two sources – first, a demonstrator table equipped with container pins and an RGB-D camera and second, images captured from the Container Terminal Tollerort (CTT) in Hamburg, Germany. Two state-of-the-art deep learning networks, namely Mask-RCNN and CMX, are trained by fusion of RGB and depth data from dataset and fine-tuned using a transfer learning approach. The performance of these networks is compared using evaluation metrics for both RGB and RGB-D data. Validation and final tests are performed through segmentation tests on images collected from CTT. The final trained networks achieve accurate classification and generate precise pixel-level category masks for both RGB and depth modalities.

The results demonstrate that the best-performing network has achieved an Average Precision (AP) of 92.5%, an Average Recall (AR) of 94%, and category-wise APs of 92.3%, 88.29%, and 97.1%. Such successful inference confirms the viability of deep learning-based frameworks for container-pin segmentation tasks.

Purpose: Container terminals face intense competition, with handling costs and processing times directly influencing their competitiveness. Ship-to-shore (STS) cranes play a pivotal role in container terminal operations, responsible for handling containers between quay and vessels. Therefore, the productivity of STS cranes is a critical performance metric. Idle time in STS crane operations results in productivity losses across the entire terminal system. Common port operating systems include STS cranes and terminal trucks (TT) working in tandem, with TT responsible for horizontal transport between the STS cranes and yard. The interaction between STS crane productivity and TT availability presents a significant operational challenge: insufficient TT leads to STS crane idle time and reduced productivity, whereas a surplus of TT results in TT idle time and inefficiencies. This research addresses this operational objective conflict through the application of logistics operating curves (LOC).

Methodology: The study begins by identifying relevant influencing, control, and target variables. The researchers then establish a discrete-event simulation (DES) using Tecnomatix Plant Simulation 2201 to generate output crucial for parametrizing the LOC. They propose a comprehensive framework that encompasses the relationships between inputs, outputs, the simulation model and the LOC. Additionally, they provide a schematic structure of the simulation, detailing the key functionalities implementing the handling processes in the model.

Findings: The expected results demonstrate that employing the DES model to accurately simulate the unloading process of a container ship using STS cranes and multiple TT is a viable method for constructing the LOC. The output data from the simulation allows the parametrization of the LOC for STS crane productivity relative to the number of TT. This operating curve highlights the trade-off and provides insights into optimizing the balance between STS crane and TT productivity.

Originality: This research contributes to literature on maritime logistics especially container terminal operations by addressing the operational conflict through the application of LOC. A method originally developed for production logistics, now adapted to maritime logistics. The proposed simulation model effectively captures the dynamics of STS crane operations. The framework and schematic structure detailed in this work provide a structured approach to developing and implementing the simulation model. The application of LOC can guide port operators in making data-driven decisions to enhance operational efficiency and competitiveness. Future research should refine these models and explore their applicability across diverse port environments to validate their effectiveness and generalizability.

The growing demand of global trade has evolved logistic operations at ports to adopt high-tech equipment’s with intelligent and automated moves. Although the application of automation technology has increased the productivity, but evidence has been found where in some cases automated machines had led to potential loss due to inefficient terminal design decisions ^[1].

The project’s goal is to investigate the operational aspects of single and double trolley ship to shore (STS) container cranes, and evaluate their performances based on a hypothetical automated terminal design concept. The discrete event simulation will be used to conduct experiments for different terminal design configurations and identify the performance of STS crane with parametric variations in operations. The impacts on the terminal key performance indicators (KPI) will also be identified and compared for both the crane types.

The project work involves both the qualitative and simulative studies. The qualitative study includes the desk research on available STS cranes in the market through manufacturer websites, publications, and reports. Recent container terminal news and survey reports will also be studied to identify a trend in the choice of STS crane by terminal operators. The simulative study will cover the aspects of simulation modelling and experimentation. In this, abstract simulation models for individual terminal components will be created. These models will include the state-of-the-art different means of automated horizontal transports and automated stacking cranes. The STS crane will be modelled with sub-models so that both the single trolley and double trolley cranes can be configured. The data for the simulation experimentation will be synthesised based on the expert reviews and raw data resources presented by publication partner (KALP GmbH).

Simulation experiments within present scope of study focuses on the evaluation of crane performance based on the position of lashing operations (water side and land side) for both single trolley and double trolley cranes. The study also involves the analysis of crane dynamics based on container position within vessel stowage plan during loading and unloading operation. Within the study both the coupled operations and decoupled operations will be studied individually, and in each case the transfer zone of container will be kept in the back-reach of the STS crane. Simulation experiments will be developed, and results will be documented in forms of graphs and performance charts representing efficiency and productivity.

The simulation results obtained from the hypothetical terminal design concepts will be explained and supporting statements will be drawn in the outlook of the study. Graphs and charts associated with crane performances will be elaborated and design recommendations will be drafted for attaining maximum productivity and optimum efficiency. Furthermore, general statements of recommendations for the reader will be drafted about the choice of decision over crane type selection and the parameters affecting such decision.

Source

[1]: N. Haworth, „Lessons in failure: Automation at the port of Auckland,“ ITFGLOBAL, 2023.