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The implementation of circular economy strategies is one of the central objectives of several governments seeking a transition toward a sustainable development. Circular economy in agriculture deals with the production of agricultural commodities making an efficient use of resources and avoiding unnecessary waste and carbon emission generation. Disruptions in the production and supply of critical agricultural products can have serious negative repercussions for firms and consumers of the food supply chain. In recent decades, disruptions generated by natural disasters such as hurricanes, thunderstorms and floods have greatly impacted social communities and industrial sectors. Supply chain risks approaches are seen to contribute key elements to address the impacts of natural disaster toward the implementation of circular economy in agriculture, helping to prevent collapses in the production and supply of food. The purpose of this paper is to study and identify flood risk drivers and their effects on the sustainability of an agriculture supply chain in connection with a circular economy strategy. By using an extended Step-wise Weight Assessment Ratio Analysis method combined with a multi-criteria decision analysis, the most essential flood drivers with a degree of importance are reported here. Then, the authors propose an Evaluation of Data based on average ASsessment method, to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. The application of this research lies within the framework of a real agricultural project founded by the European Commission Scientific Section, called RUC-APS.

The authors use management science-based tools to address circular economy in agriculture. The authors propose a multi-criteria-based methodology to assess the risks of flooding in crops areas. To validate the proposed methodology, a case example from Spain is discussed to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas.

The proposed multi-criteria methodology confirmed a successful application to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. Organizations and firms in the agricultural business can use the methodology to identify risks drivers and to detect the best projects to mitigate the highest impacts of flooding risks in crops areas.

The authors use supply chain risks approaches to address the impacts of natural disaster on the implementation of circular economy in agriculture. The authors propose a robust multi-criteria-based methodology to assess the risks of flooding in crops areas and we used to determine the best mitigating projects to face flooding risks on crop areas.

In this study, an integrated decision-making model consisting of decision-making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and a modified version of evaluation based on distance from average solution (EDAS) methods is proposed for supplier selection problem in a public procurement system considering sustainable development goals.

DEMATEL and BWM methods are used to determine weights of the criteria that are defined for the supplier selection problem. Weight aggregation method is applied to combine the weights obtained from these two methods. A modified version of EDAS method is then used in order to rank the alternative suppliers.

The proposed decision-making model is investigated for a supplier selection problem for a hospital in Spain. The validity of the results is checked using comparison with other decision-making methods and several performance analysis tests.

The proposed multi-criteria decision-making (MCDM) model contributes to the healthcare supply chain management (SCM) and aims to lead the policy makers in selecting the best supplier.

There is no such study that combines DEMATEL and BWM together for weight generation. The application of the modified EDAS method is also new. In real time situations, the decision experts may confront to the difficulty of using BWM while identifying the best and the worst criteria choices. The idea of using DEMATEL is to aid the experts to make them enable in distinguishing between the best/worst criteria and handle BWM easily.

Industrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.

In this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.

This research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.

The proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.

The proposed model is generic and can be applied for large-scale GSC environments with little modifications.

No prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

To meet the requirements of high-dimensional accuracy and surface finish of various advanced engineering materials for generating intricate part geometries, non-traditional machining (NTM) processes have now become quite popular in manufacturing industries. To explore the fullest machining capability of these NTM processes, it is often required to operate them while setting their different controllable parameters at optimal levels. This paper aims to present a novel approach for selection of the optimal parametric mixes for different NTM processes in order to assist the concerned process engineers.

In this paper, design of experiments (DoE) and technique for order preference by similarity to ideal solution (TOPSIS) are combined to develop the corresponding meta-models for identifying the optimal parametric combinations of two NTM processes, i.e. electrical discharge machining (EDM) and wire electrical discharge machining (WEDM) processes with respect to the computed TOPSIS scores.

For EDM operation on Inconel 718 alloy, lower settings of open circuit voltage and pulse-on time and higher settings of peak current, duty factor and flushing pressure will simultaneously optimize all the six responses. On the other hand, for the WEDM process, the best machining performance can be expected to occur at a parametric combination of zinc-coated wire, lower settings of pulse-on time, wire feed rate and sensitivity and intermediate setting of pulse-off time.

As the development of these meta-models is based on the analysis of the experimental data, they are expected to be more practical, being immune to the introduction of additional parameters in the analysis. It is also observed that the derived optimal parametric settings would provide better values of the considered responses as compared to those already determined by past researchers.

This DoE–TOPSIS method-based approach can be applied to varieties of NTM as well as conventional machining processes to determine the optimal parametric combinations for having their improved machining performance.

Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk.

At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics.

A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model.

The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors.

A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.

It's no longer a secret that a hassle-free life and better human development index are only possible in smart cities with appropriate and efficient deployment of artificial intelligence (AI)-based technologies where the best results of data analysis are being used. Technology is becoming more productive using circular economy while employing all the dimensions of AI where integration of results is being incorporated as an outcome of data analysis received from different segments i.e., Traffic Management, Public Safety, and Movement, Security and surveillance, Waste Management Systems, or the Energy Management, etc. This chapter specifically talks about areas where AI is facing challenges in the implementation and administration of smart cities while covering the intrinsic challenges faced in specialized domains such as Public Sanitation, Virtual Parking Management, Traffic Congestion, Security Surveillance, and many more discussed as case study relating to the functioning of the circular economy. In the last, we have summarized the impact of AI on the CE and its future scope where AI can play a better role in increased productivity, increased efficiency, robust safety and finally economic benefit for long-term stable economic stability, development and inclusive growth.

Internet of things (IoT) is the technology enabler in smart city adoption and creates circular economy (CE). The purpose of this chapter is to find out the relationship nexus between IoT, smart city and CE and its practical constraints in execution. This chapter elaborately maps the empirical evidence available in the literature survey of smart city development, information and communication technology (ICT) and CE. The contributions from outstanding scholars and current topics are analyzed using high citations index. The authors referred the topics with scholarly publishers and validated the data. Following that, significant technology subjects such as big data and analytics, as well as intelligent information systems, are individually examined. The findings are the challenges in implementing IoT and its interoperability capacity due to interlinking of different devices and systems. Another key challenger as well as drivers are data volume and quality, privacy, complexity and governance. The research can help policymakers, IT infrastructure provider, bureaucrats and all other concerned stakeholders to get sensitized on the implementation of IoT at smart city infrastructure. It also describes about the importance of collaboration and embracing open innovation (OI) while implementing IoT. This study is innovative in its approach and referred literature in the field of IoT, smart city, and CE by high citation index (HCI). It also researched about the various constraints in building CE through technology deployment and discusses about multilayer and multifunctional collaboration.

The advent of the 21st century marks a pivotal era where rapid urbanization intersects with technological advancements, giving rise to the concept of smart cities. These urban environments harness information and communication technologies to improve service efficiency and enhance the quality of life. Parallel to this development is the emergence of circular economy (CE) models, recognized globally as an essential response to environmental challenges. This chapter delves into the integration of CE principles in smart cities, emphasizing a shift from traditional linear models towards sustainable, resource-efficient urban landscapes. Exploring the evolution of smart cities and CEs, this chapter highlights synergies and potential benefits of this integration while acknowledging significant challenges. These include technological, infrastructural, financial, policy-related and social–cultural barriers. Through a comprehensive analysis of literature, case studies and best practices, effective strategies to overcome these challenges are presented. This chapter emphasizes the roles of technological innovation, policy reform, stakeholder engagement and community involvement in driving this transformation. This chapter identifies future research areas and emerging trends, underscoring the profound impact of integrating CE principles in smart cities. This integration is pivotal for shaping sustainable and resilient urban futures, thereby redefining the paradigm of urban development in the modern era.