Novel resilient-sustainable strategies for second-generation biofuel network design considering Neem and Eruca Sativa under hybrid stochastic fuzzy robust approach

Towards facing uncertainties in biofuel supply chain networks: a systematic literature review

Biofuel supply chains (BSCs) face diverse uncertainties that pose serious challenges. This has led to an expanding body of research focused on studying these challenges. Hence, there is a growing need for a comprehensive review that summarizes the current studies, identifies their limitations, and provides essential advancements to support scholars in the field. To overcome these limitations, this research aims to provide insights into managing uncertainties in BSCs. The review utilizes the Systematic Reviews and Meta-Analyses (PRISMA) method, identifying 205 papers for analysis. This study encompasses three key tasks: first, it analyses the general information of the shortlisted papers. Second, it discusses existing methodologies and their limitations in addressing uncertainties. Lastly, it identifies critical research gaps and potential future directions. One notable gap involves the underutilization of machine learning techniques, which show potential for risk identification, resilient planning, demand prediction, and parameter estimations in BSCs but have received limited attention. Another area for investigation is the potential of agent-based simulation, which can contribute to analysing resilient policies, evaluating resilience, predicting parameters, and assessing the impact of emerging technologies on BSC resilience in the twenty-first century. Additionally, the study identifies the omission of various realistic assumptions, such as backward flow, lateral transshipments, and ripple effects in BSC. This study highlights the complexity of managing uncertainties in BSCs and emphasizes the need for further research and attention. It contributes to policymakers' understanding of uncertain sources and suitable approaches while inspiring researchers to address limitations and generate breakthrough ideas in managing BSC uncertainties.

A sustainable and efficient home health care network design model under uncertainty

To cater to the increasing demands, particularly during diseases such as Covid-19, the design and planning of home health care systems is of significant importance. The current study proposes a multi-objective mixed-integer linear model for a home health care network in two stages; the first is the opening of efficient health centres, and the second is the routing and scheduling considering corporate social responsibility and efficiency. There are multiple objectives that we consider, including minimization of total costs and inefficiency considerations, and maximization of social aspects. A novel aspect of this study is the consideration of social responsibility, which includes employment opportunities and regional economic development, and efficiency in terms of time, energy, and mismanagement of budgets. To measure efficiency, an augmented version of the data envelopment analysis approach is incorporated into the proposed optimization model. Additionally, the TH approach is developed as an interactive fuzzy method to deal with the proposed multi-objective model. Within the HHC problem, costs, social factors, and service time are inherently uncertain, and hence, to solve this problem, a robust-fuzzy approach is proposed. The ensuing model is applied to a real case study of Kermanshah in Iran. Moreover, several problem instances motivated by real cases are generated with different characteristics to measure the performance of the proposed model and approach. The results show that decision-makers' preferences play a key role in human resource planning and regional development. Furthermore, the results confirm the efficiency of the proposed approach in different instances within reasonable time frames.

Assessing the development potential of non-food biofuel crops under the water-land-biofuel nexus perspective

Facing water and land scarcity, planting non-food biofuel crops on marginal land depending on natural rainfall has been considered as an attractive means of achieving sustainable biofuel development. However, the complex connection between rainfall and marginal land resources in spatial-temporal distribution affects the optimal planting layout of non-food biofuel crops as well as the assessment of biofuel potential, especially in arid areas. In this study, we constructed a water-land-biofuel nexus centered on non-food biofuel crops, optimized the layout of three non-food biofuel crops, sweet sorghum, Jerusalem artichoke and switchgrass, based on fuzzy mathematics method under the water-land-biofuel nexus perspective, determined yield-rainfall curve to calculate the development potential of non-food biofuel crops. The results showed that sweet sorghum and Jerusalem artichoke are more suitable for planting in Ningxia. Three potential scenarios are set up under different growth conditions and agricultural technologies. The theoretical biofuel production is [9.64× 10⁷, 10.93× 10⁷] GJ, which was verified by the result that the biofuel production per unit area is close to the lower limit of the test production range. It can also be speculated that there may exist irrigation supply and fertilization in the actual crops planting in other studies.

Stochastic versus Fuzzy Models—A Discussion Centered on the Reliability of an Electrical Power Supply System in a Large European Hospital

This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important modules of the system according to the Automatic Transfer Switch. Fuzzy Inference System is used to analyze and assess the reliability value. The stochastic versus fuzzy approach is also used to evaluate the reliability contribution of each system module. This case study aims to identify and analyze possible system failures and propose new solutions to improve the system reliability of the power supply system. The dynamic modeling is based on block diagrams and Petri nets and is evaluated via Markov chains, including a stochastic approach linked to the previous analysis. This holistic approach adds value to this type of research question. A new electrical power supply system design is proposed to increase the system’s reliability based on the results achieved.

Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL modeling (SSCLM), nonetheless selecting a suitable optimization technique and solution method is still of interest as model performance is highly dependent on decision-making variables of the model development process. This study provides insights from the analysis of 95 scholarly articles to identify research gaps in the MOO for SSCLM and to assist decision-makers in selecting suitable MOO techniques and solution methods. The results of the analysis indicate that economic and environmental aspects of sustainability are the main context of SSCLM, where the social aspect is still limited. More SSCLMs for sourcing, distribution, and transportation phases of the supply chain are required. Additionally, more sophisticated techniques and solution methods, including hybrid metaheuristics approaches, are needed in SSCLM.

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts' evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.