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Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Food sector supply chains have significant negative environmental impacts, including the expansion of global food commodity production, which is driving tropical deforestation – a major climate and biodiversity problem. Innovative supply chain monitoring services promise to address such impacts. Legislation also designates “forest-risk commodities”, demanding supply chain due diligence of their provenance. But such data alone does not produce change. This study investigates how theory in performance measurement and management (PMM) can combine with sustainable supply chain management (SSCM) and decision theory (DT) via case study research that addresses paradoxes of simplicity and complexity.

Given existing relevant theory but the nascent nature of the topic, theory elaboration via abductive case study research is conducted. Data collection involves interviews and participatory design workshops with supply chain actors across two supply chains (coffee and soy), exploring the potential opportunities and challenges of new deforestation monitoring services for food supply chains.

Two archetypal food supply chain structures (short food supply chains with high transparency and direct links between farmer and consumer and complex food supply chains with highly disaggregated and opaque links) provide a dichotomy akin to the known/unknown, structured/unstructured contexts in DT, enabling novel theoretical elaboration of the performance alignment matrix model in PMM, resulting in implications for practice and a future research agenda.

The novel conceptual synthesis of PMM, SSCM and DT highlights the importance of context specificity in developing PMM tools for SSCM and the challenge of achieving the general solutions needed to ensure that PMM, paradoxically, is both flexible to client needs and capable of replicable application to deliver economies of scale. To advance understanding of these paradoxes to develop network-level PMM systems to address deforestation impacts of food supply chains and respond to legislation, a future research agenda is presented.

This study aims to quantify and compare the environmental impacts of Marble-stone and Kota-stone flooring options widely used for buildings in India. The study discusses the possibility of carbon sequestration through Bamboo cultivation in India.

The study has followed a standard life cycle assessment (LCA) framework based on ISO 14040 guidelines. Three distinct phases have been compared on midpoint and endpoint assessment categories – raw material, polishing and disposal. Primary data has been collected from the construction site in India, and secondary data has been collected from the Ecoinvent 3.0 database. Previous studies have been referred to discuss and calculate the area of bamboo cultivation required to sequestrate the generated carbon from the flooring.

The study has found that endpoint category damage to resources, and midpoint categories of climate change, metal depletion and agricultural land use are highly impacted in building floorings. The study has also found that the Marble-stone floor generates higher environmental impacts than the Kota-stone floor in most of the midpoint and endpoint impact categories. This difference is significant in the raw material phase due to the different compositions of stones. The study also found that Bamboo has excellent potential to act as a carbon sink and mitigate the generated carbon.

This study excludes human labour, cutting and distribution of floor tiles made of Marble-stone and Kota-stone. The researcher can use the study to evaluate, compare and benchmark the various building flooring options from the environmental perspective. The study aids to the body of knowledge available on the various building flooring options by presenting the LCA or the environmental impacts generated by two flooring options. It is expected that the architects and builders can use these results to develop carbon-neutral buildings. This study provides a methodology for governments, constructors, builders and individuals to evaluate, compare and benchmark the various construction materials from the environmental perspective by computing the environmental impacts throughout the life cycle of the materials.

This study compares two widely used building flooring options using the LCA methodology and evaluates the potential of bamboo cultivation near the buildings for carbon sinks. The study is unique because it shows the environmental impacts of two flooring options and the carbon sequestration method to mitigate/absorb the generated environmental impacts in or around the building itself through bamboo cultivation. This study may set the foundation for carbon-neutral buildings.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

By reconsidering the concept of the historic environment, the aim of this study is to better understand how heritage is expressed by examining the networks within which the cultural performances of the historic environment take place. The goal is to move beyond a purely material expression and seek the expansion of the cultural dimension of the historic environment.

Conceptually, the historic environment is considered a valuable resource for heritage expression and exploration. The databases and records that house historic environment data are venerated and frequented entities for archeologists, but arguably less so for non-specialist users. In inventorying the historic environment, databases fulfill a major role in the planning process and asset management that is often considered to be more than just perfunctory. This paper approaches historic environment records (HERs) from an actor network perspective, particularizing the social foundation and relationships within the networks governing the historic environment and the environment's associated records.

The paper concludes that the performance of HERs from an actor-network perspective is a hegemonic process that is biased toward the supply and input to and from professional users. Furthermore, the paper provides a schematic for how many of the flaws in heritage transmission have come about.

The relevance here is largely belied by the fact that HERs as both public digital resources and as heritage networks were awaiting to be addressed in depth from a theoretical point of view.

Forests are negatively affected from rapid world population increase and industrialization that create intense pressures on natural resources and the possibility of an achieving circular economy. Forests can be considered as essential resources for providing sustainable society and meeting the requirements of future generations and circular economy. Therefore sustainable production tools as part of circular economy can be handled as one of the basic indicators for achieving circular economy. Accordingly the main purpose of this study is developing a novel rough – fuzzy multi-criteria decision-making model (MCDM) for evaluation sustainable production for forestry firms in Eastern Black Sea Region.

For determining 18 criteria weights a novel Rough PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method is developed. Eight decision-makers (DMs) participated in the research, and to obtain group rough decision matrix, rough Dombi weighted geometric averaging (RNDWGA) operator has been applied. For evaluation forestry firms fuzzy MARCOS (Measurement of alternatives and ranking according to COmpromise solution) method was utilized.

After application developed model the fourth alternative was found as the best. Sensitivity analysis and comparison were made to present the applicability of this method.

Development of novel integrated Rough PIPRECIA-Fuzzy MARCOS model with emphasis on developing new Rough PIPRECIA method.

This study aims to find the impact of supply chain certification (SCCert) on supply chain efficiency (SCEffi) with the inverted U-shaped moderator role of supply chain complexity (SCComp).

In order to test the conceptual model and the hypothesized relationships between all the constructs, the 307 useable survey responses were collected using the purposive sampling technique on a seven-point Likert scale. The SPSS26.0 and AMOS24.0 were used to analyze data, and the hierarchical regression analysis was used to test the model.

This study reached a set of interesting results where it was confirmed that there is a significant relationship between SCCert and SCEffi. It further confirmed the inverted U-shaped moderating effect of SCComp between SCCert and SCEffi: on the left side of the threshold, the increase of SCComp will enhance the promotion effect of SCCert on SCEffi, while on the right side of the threshold, excessive SCComp will rather weaken the promotion effect of SCCert on SCEffi.

The findings provide implications for supply chain efficiency enablers to introduce/promote certification upgrading actions. The study provides a framework for solving the power and constraint problem of supply chain efficiency change.

Findings provide deeper and new insights into threshold feature of supply chain complexity, analyzing how supply chain certification activity realize supply chain efficiency reform through the moderating role of supply chain complexity.

It becomes a strategic option for enterprises to upgrade and improve supply chain efficiency (SCE) by promoting the digital transformation (DT). This study formulated a parallel mediation model to analyze the relationships among DT, supply chain transparency (SCT), supply chain agility (SCA) and SCE to reveal how DT affects SCE through the mediation of SCT and SCA.

Three paradigms, i.e. resource-based view (RBV), dynamic capability view (DCV) and structure-conduct-performance (SCP) were employed to address the parallel mediation effects. A total of 392 questionnaires (samples) from the port-hinterland supply chain in the DT pilot project of New Land-Sea Corridor in western China were collected, which was then applied to formulate a structural equation model (SEM) to verify the proposed hypotheses.

The results confirmed the existences of parallel mediating effects of SCT and SCA between DT and SCE. On one hand, the direct effect of DT on SCE is not significant when SCT and SCE plays jointly impacts on DT and SCE. On the other hand, SCT and SCA play a positive parallel full mediating effect of DT on SCE.

This study contributed to the literature on changing activities of SCE in DT processes. Specifically, it highlighted how DT leads to SCE via SCT and SCA activities. In addition, this study specified the conditions that the insignificant direct effect of DT has reflects on SCE, it is the time when SCT and SCE are jointly acting on DT and SCE.

By integrating insights from the RBV, DCV and SCP paradigms, this study clarified the mechanisms of DT on SCE, and provided insight on the role of SCT and SCA in the relationship between DT and SCE. The novelty of this study and the results extend the existing literature and provide implications for future research.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The shipbreaking sector in Bangladesh has spurred extensive academic and policy debates on relations between shipbreaking industries, environmental degradation and the health security of their workers. As shipbreaking is an economically significant industry in Bangladesh, it needs to implement both domestic and global mechanisms for environmental conservation and the protection of the labourers’ health from environmental risks. The purpose of this paper is to primarily explore the environmental and health security issues in shipbreaking activities in Bangladesh. It also identifies the challenges in implementing the rules and regulations for protecting the health of the workers at shipbreaking yards in Bangladesh and preserving the marine environment.

This is a qualitative paper based on secondary materials, including journal articles, books and national and international reports. It critically reviews the existing literature, rules, regulations and policing on shipbreaking with a particular focus on the environment and health security of the workers.

This paper finds that the implementation of the rules and regulations in shipbreaking in Bangladesh is complicated because of weak implementation mechanisms, political and economic interests of the yard owners, lack of coordination among different agencies, lack of adequate training and awareness among the workers and workers’ poor economic condition, which contribute to the degradation of marine and local environments and trigger health hazards among the workers. Therefore, degrading the environment and undermining occupational health and safety regulations have become regular; thus, accidental death and injury to the workers are common in this sector.

This paper is an important study on the issues of workers' health and safety and environmental hazards in the shipyard. It reports how the health security of the workers in shipbreaking yards in Bangladesh is vulnerable, and environmental rules are challenged. Finally, this paper frames some policy implications to safeguard the workers’ health rights and the marine environment.