Resource planning strategies for healthcare systems during a pandemic.

We study resource planning strategies, including the integrated healthcare resources' allocation and sharing as well as patients' transfer, to improve the response of health systems to massive increases in demand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which mitigates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our optimization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters.

Issues and solutions of electronic waste urban mining for circular economy transition: An Indian context.

The rapid consumption of advanced e-products has intensified problems for the linear economy; constantly diminishing natural resources employed in production processes have created a need of recycle and reuse. Although the transition to a circular economy proposes to end the loop of e-products, it needs the application of processes such as urban mining to recover resources as secondary raw material. The present study intends to examine the issues and challenges of electronic waste urban mining (EWUM) in India that need to be assessed for the development of a sustainable economy. To accomplish this, the current study employs integrated Multi-Criteria-Decision making methods (MCDM). Step-Wise Weight Assessment Ratio Analysis (SWARA) is used to prioritize issues and their possible solutions with Weighted Assessment Sum Product Assessment (WASPAS) methods introduced to explore these challenges and provide solutions for managing EWUM. There is an immediate need to acknowledge the issues confronted by stakeholders in urban mining processes for successful transition to a circular economy. A better understanding of the issues will help policy makers and decision makers to implement best practices to enhance the urban mining process in India. This study has shown that socio-economic (SE) issues are the most critical issues in EWUM in India. The possible solutions that would have most impact are to enhance awareness campaigns for people to educate themselves regarding e-waste, train staff to handle safe disposal of e-waste and produce eco-friendly electronic products.

Environmental management partner selection for reverse supply chain collaboration: A sustainable approach.

The Indian electronics industry is facing immense pressure to include sustainability practices in order to meet customer expectations, comply with legislation and create an edge over competitors. This growing need for inclusion of sustainability is driving original equipment manufacturers (OEMs) to collaborate with third-party reverse logistics providers (3PRLPs) to sustainably manage returns. Collaboration with 3PRLP will put OEMs in a stronger position regarding compliance with government legislation, improving upon their corporate image and customer satisfaction. It is a win-win situation for the OEM. On the other hand, from the 3PRLP's point of view, it is important to know the capabilities of the OEMs before entering into a collaborative endeavour. Hence, it is firstly necessary to identify the most appropriate attributes of the manufacturer which are essential for a sustainable collaboration. In this context, the study proposes a novel framework for investigating the performance attributes of the OEMs from the economic, environmental and social aspects of sustainability. Since the evaluation of each attribute for each dimension of sustainability differs in terms of the nature of the information needed and the time and resources required, it is necessary to conduct a pre-evaluation of the attributes using the Complex Proportional Assessment (COPRAS) method. Furthermore, the shortlisted attributes for each sustainability dimension are evaluated and prioritized using the best worst method (BWM). The novelty of the attribute selection process lies in simultaneously considering the degree of importance of the attribute as well as the degree of difficulty of the collection of data required for the assessment of the OEM. The result of this study helps decision-makers and practitioners to comprehend the most influential attributes of OEMs which are crucial for collaboration, thus enhancing the overall sustainability impact of the supply chain. The managerial implications drawn from the resulting analysis provide the 3PRLP with a sustainable evaluation framework which can be ideally used for the selection of collaborative partners. The above model is validated using the case of an Indian 3PRLP company that handles electronic products.

"There is no carnival without samba": Revealing barriers hampering biodiversity-based R&D and eco-design in Brazil.

Considering the unique relevance of Brazilian biodiversity, this research aims to investigate the main barriers to biodiversity-based R&D and eco-design development in a leading national company which has been commended for its innovation and sustainability. The methodology for this research was based on on-location visits, in-depth interviews, and consensus building among R&D, sustainability, and quality managers. A multi-criteria decision-making (MCDM) approach was adopted through interpretive structural modelling (ISM), a method that assists decision makers to transform complex models with unclear data into structural models. Some of the most influential barriers to biodiversity-based eco-design initiatives are "lack of legal incentive", "not enough demand from the market", and "not enough available knowledge/scientific data." The most relevant barrier was "no legal incentive" from government. Consequently, managers should concentrate their efforts in tackling those barriers that may affect other barriers known as 'key barriers'. Government should work decisively toward promoting a framework of legal incentives for bio-based eco-design; otherwise, metaphorically, "there is not carnival without the samba singer who pushes the rhythm". The results given here reveal the barriers for bio-based eco-design in a Brazilian leading company, and this is the first work combining ISM to barriers to biodiversity R&D and eco-design.

A causality analysis of risks to perishable product supply chain networks during the COVID-19 outbreak era: An extended DEMATEL method under Pythagorean fuzzy environment.

In nowadays world, firms are encountered with many challenges that can jeopardize business continuity. Recently, the coronavirus has brought some problems for supply chain networks. Remarkably, perishable product supply chain networks, such as pharmaceutical, dairy, blood, and food supply chains deal with more sophisticated situations. Generally, during pandemic outbreaks, the activities of these industries can play an influential role in society. On the one hand, products of these industries are considered to be daily necessities for living. However, on the other hand, there are many new restrictions to control the coronavirus prevalence, such as closing down all official gatherings and lessening the work hours, which subsequently affect the economic growth and gross domestic product. Therefore, risk assessment can be a useful tool to forestall side-effects of the coronavirus outbreaks on supply chain networks. To that aim, the decision-making trial and evaluation laboratory approach is used to evaluate the risks to perishable product supply chain networks during the coronavirus outbreak era. Feedback from academics was received to identify the most important risks. Then, experts in pharmaceutical, food, and dairy industries were inquired to specify the interrelations among risks. Then, Pythagorean fuzzy sets are employed in order to take the uncertainty of the experts' judgments into account. Finally, analyses demonstrated that the perishability of products, unhealthy working conditions, supply-side risks, and work-hours are highly influential risks that can easily affect other risk factors. Plus, it turned out that competitive risks are the most susceptive risk in the effect category. In other words, competition among perishable product supply chain networks has become even more fierce during the coronavirus outbreak era. The practical outcomes of this study provide a wide range of insights for managers and decision-makers in order to prevent risks to perishable product supply chain networks during the coronavirus outbreak era.

Prioritising risk mitigation strategies for environmentally sustainable clothing supply chains: Insights from selected organisational theories.

Recent outbreak of COVID-19 pandemic has provided strong impetus to supply chain resilience research. In a volatile and uncertain business environment, resilience can be incorporated by developing and implementing effective risk mitigation strategies. In this research, risk mitigation strategies for environmentally sustainable clothing supply chain have been prioritised by considering their efficacy to mitigate various risks. Twelve risks and thirteen mitigation strategies, identified through literature review and experts' opinion, are considered as decision criteria and alternatives respectively. Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (fuzzy TOPSIS) is implemented under a group decision making scenario for prioritising the strategies. Developing supply chain agility; multiple green sourcing and flexible capacities; adoption of green practices; building trust, coordination and collaboration; and alignment of economic incentives and revenue sharing are found to be dominant risk mitigation strategies for environmentally sustainable clothing supply chain. These strategies have been viewed through the lens of resource dependence, change management and transaction cost theories. Organisation desirous to build resilience in their supply chain can prioritise the risk mitigation strategies and adopt a portfolio of strategies based on the outcome of this research.

Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model.

Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes; moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixed-integer linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously, of the population's exposure to pollution. This paper considers some realistic assumptions for the first time, including location-routing problem, time window-based green vehicle routing problem, vehicles scheduling, vehicles failure, split delivery, population risk, and load-dependent fuel consumption to manage both infectious and non-infectious medical waste. We apply a fuzzy goal programming approach for solving the proposed bi-objective model, and the efficiency of the proposed model and solution approach is assessed using data related to 13 nodes of medical waste production in a location west of Tehran.

A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries.

Developing countries scramble to contain and mitigate the spread of coronavirus disease 2019 (COVID-19), and world leaders demand equitable distribution of vaccines to trigger economic recovery. Although numerous strategies, including education, quarantine, and immunization, have been used to control COVID-19, the best method to curb this disease is vaccination. Due to the high demand for COVID 19 vaccine, developing countries must carefully identify and prioritize vulnerable populations and rationalize the vaccine allocation process. This study presents a mixed-integer linear programming model for equitable COVID-19 vaccine distribution in developing countries. Vaccines are grouped into cold, very cold, and ultra-cold categories where specific refrigeration is required for their storage and distribution. The possibility of storage for future periods, facing a shortage, budgetary considerations, manufacturer selection, order allocation, time-dependent capacities, and grouping of the heterogeneous population are among the practical assumptions in the proposed approach. Real-world data is used to demonstrate the efficiency and effectiveness of the mathematical programming approach proposed in this study.

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments.

Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.

From Sustainable Global Value Chains to Circular Economy-Different Silos, Different Perspectives, but Many Opportunities to Build Bridges.

A growing interest in the circular economy concept has pushed the discourse in various management-related disciplines beyond established boundaries, with calls to better address how such a model may be developed in a world of global value chains. Still, the conventional linear economy model continues to dominate business, society, and research. While the concept of better connecting physical output and input flows at multiple production or consumption levels is becoming more accepted, it remains unclear how to make this happen while ensuring that sustainability targets are met or exceeded. Multiple scientific communities contribute different perspectives to this discourse, with promising opportunities for research. Circular economy and sustainability from business and economics perspectives are multifaceted. The existing body of knowledge needs to be advanced to assist private individuals, business managers, investors, or policymakers in making informed decisions. In this article for the inaugural issue, we provide a snapshot of the discourses among those who have studied the circular economy and its related topics. We outline conceptual inroads and potential research questions to encourage further circular economy and sustainability research and discourse from business or economics perspectives as well as from the broader transdisciplinary angle. We propose three research pathways: (1) connecting output with input needs in a global circular economy; (2) beyond today's business logic for a global circular economy; and (3) inclusion of the Global South in North-dominated circular economies. For each, we propose concepts, theories, or methodological approaches and offer various perspectives from the micro, macro, and meso levels.

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19).

The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.

Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management.

In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.