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This study aims to propose a new scheme for designing a high-sensitivity optical biosensor. For this, two agents have been considered: reflection-type micro-resonators, which filter the noise of the pump, and coupled-ring reflectors (CRRs), which are coupled to partial reflecting elements in the bus waveguide to create Fano-resonance. These two agents improve the sensor sensitivity and have low-power optical switching/modulation.

The proposed model is based on the coupling of the CRRs with the Fabry–Pérot cavity. The slope of the Fano-resonance line shape and consequently the sensitivity of the proposed CRRs are higher than those of conventional microring resonators.

The proposed scheme has many characteristics: CRRs have been used to create a higher slope of the Fano-resonance line shape; the sensitivity of the sensor shows improvement on the basis of reflection-type micro-resonators and by the removal of the pump noise; the designed sensor has low-power optical switching/modulation; and the modeling and designing of a novel high-sensitivity resonator is based on coupling the CRRs with the Fabry–Pérot cavity.

This study has proposed a new scheme for designing a high-sensitivity optical biosensor. This method is based on the improvement of the sensitivity by two agents: reflection-type micro-resonators, which filter the noise of the pump, and coupled-ring reflectors, which are coupled to partial reflecting elements in the bus waveguide to create Fano-resonance.

A growing body of evidence points to the influence of location and allocation decisions on the structure of healthcare networks. The authors introduced a three-level hierarchical facility location model to minimize travel time in the healthcare system under uncertainty.

Most healthcare networks are hierarchical and, as a result, the linkage between their levels makes it difficult to specify the location of the facilities. In this article, the authors present a hybrid approach according to data envelopment analysis and robust programming to design a healthcare network. In the first phase, the efficiency of each potential location is calculated based on the non-radial range-adjusted measure considering desirable and undesirable outputs based on a number of criteria such as the target area's population, proximity to earthquake faults, quality of urban life, urban decrepitude, etc. The locations deemed suitable are then used as candidate locations in the mathematical model. In the second phase, based on the proposed robust optimization model, called light robustness, the location and allocation decisions are adopted.

The developed model is evaluated using an actual-world case study in District 1 of Tehran, Iran and relevant results and different sensitivity analyses were presented as well. When the percentage of referral parameters changes, the value of the robust model's objective function increases.

The contributions of this article are listed as follows: Considering desirable and undesirable criteria to selecting candidate locations, providing a robust programming model for building a service network and applying the developed model to an actual-world case study.

This study aims to minimize the expected arrival time of relief vehicles to the affected areas, considering the destruction of potential routes and disruptions due to disasters. In relief operations, required relief items in each affected area and disrupted routes are considered as uncertain parameters. Additionally, for a more realistic consideration of the situations, it is assumed that the demand of each affected area could be met by multiple vehicles and distribution centers (DCs) and vehicles have limited capacity.

The current study developed a two-stage stochastic programming model for the distribution of relief items from DCs to the affected areas. Locating the DCs was the first-stage decisions in the introduced model. The second-stage decisions consisted of routing and scheduling of the vehicles to reach the affected areas.

In this paper, 7th district of Tehran was selected as a case study to assess the applicability of the model, and related results and different sensitivity analyses were presented as well. By carrying out a simultaneous sensitivity analysis on the capacity of vehicles and the maximum number of DCs that can be opened, optimal values for these parameters were determined, that would help making optimal decisions upon the occurrence of a disaster to decrease total relief time and to maximize the exploitation of available facilities.

The contributions of this paper are as below: presenting an integrated model for the distribution of relief items among affected areas in the response phase of a disaster, using a two-stage stochastic programming approach to cope with route disruptions and uncertain demands for relief items, determining location of the DCs and routing and scheduling of vehicles to relief operations and considering a heterogeneous fleet of capacitated relief vehicles and DCs with limited capacity and fulfilling the demand of each affected area by more than one vehicle to represent more realistic situations.

The Russia–Ukraine war has disrupted the wheat supply worldwide. Given that wheat is one of the most important agri-food products in the world, it is necessary to pay attention to the wheat supply chain during the global crises. The use of resilience strategies is one of the solutions to face the supply chain disruptions. In addition, there is a possibility of multiple crises occurring in global societies simultaneously.

In this research, the resilience strategies of backup suppliers (BS) and inventory pre-prepositioning (IP) were discussed in order to cope with the wheat supply chain disruptions. Furthermore, the p-Robust Scenario-based Stochastic Programming (PRSSP) approach was used to optimize the wheat supply chain under conditions of disruptions from two perspectives, feasibility and optimality.

After implementing the problem of a real case in Iran, the results showed that the use of resilience strategy reduced costs by 9.33%. It was also found that if resilience strategies were used, system's flexibility and decision-making power increased. Besides, the results indicated that if resilience strategies were used and another crisis like the COVID-19 pandemic occurred, supply chain costs would increase less than when resilience strategies were not used.

In this study, the design of the wheat supply chain was discussed according to the wheat supply disruptions due to the Russia–Ukraine war and its implementation on a real case. In the following, various resilience strategies were used to cope with the wheat supply chain disruptions. Finally, the effect of the COVID-19 pandemic on the wheat supply chain in the conditions of disruptions caused by the Russia–Ukraine war was investigated.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

Resilience is a fundamental component of healthcare supply chains, as the quality and endurance of human life are dependent on them. However, there are numerous resilience-building measures, and there is a need for prioritization of those strategies. This research study aims to prioritize resilience strategies for healthcare supply chains while considering the risks that most severe, probable to occur and have the lengthiest periods of recovery.

This research study has used multi-criteria decision-making (MCDM) techniques for analysis. Initially, the criteria for prioritization of risks, i.e. severity, probability of occurrence and recovery time were assigned with importance weights through the fuzzy analytical hierarchy process (AHP). Then, these weights were used in the fuzzy technique for order preference by similarity to ideal solution (TOPIS) analysis for prioritization of risks. Subsequently, the identified risks were used for highlighting the appropriate resilience strategies through the fuzzy quality function deployment (QFD) technique.

Results indicate that Industry 4.0, multiple sourcing, risk awareness, agility and global diversification of suppliers, markets and operations are the most significant resilience strategies.

This study's limitation is that it is conducted in a general perspective, rather than reducing the context to a developing or developed country. Different areas have variable market factors, due to which potential risks occur in a different form. Moreover, resilience strategies work differently in different environments. Therefore, for future endeavors, the studies should be carried out in a limited context.

This research study proposes a novel MCDM-based approach for ranking resilience strategies, in light of the most probable, severe and long-lasting risks. In addition, this approach has been employed for the enhancement of resilience in healthcare supply chains.

In recent years, both academics and institutions have acknowledged the crucial role multinational enterprises (MNEs) can play in addressing the sustainability challenges, as formalized by the sustainable development goals (SDGs). Nevertheless, because of their extensiveness and their design as country-level targets, SDGs have proven challenging to operationalize at a firm level. This problem opens new and relevant avenues for research in international business (IB). This chapter attempts to frame the topic of extended value chain sustainability in the IB literature. In particular, it addresses a specific topic, that is, how sustainability and resilience-building practices interact in global value chains (GVCs). To do so, the present study develops the case of STMicroelectronics (ST), one of the biggest semiconductor companies worldwide.

This paper aims to propose a novel matrix-based systematic approach for vulnerability assessment.

The proposed method consists of two major steps. First, the power network is modeled as a topological combination of edges (transmission lines, transformers, etc.) and nodes (buses, substations, etc.). The second step is to use an axiomatic design-based index for topology analysis. This index is based on the systematic counting of possible routes from the start (generators) to destination (loads), considering load importance, before and after a disruption.

The effectiveness of the proposed method is demonstrated through an illustrative example and the Institute of Electrical and Electronics Engineers (IEEE) 14-bus power system. It was shown that the load’s importance influences the results of the vulnerability analysis. The proposed method has some advantages over traditional graph theory such as an explicit description of multiple transmission nodes and assets with multiple conversion processes. Furthermore, it would help the power grid operators and asset investment managers to be better to assess the vulnerable components.

The proposed method can be used in planning, optimization, robustness and hardening of power systems.

The paper presents a matrix-based systematic approach to evaluate and quantify the vulnerability of the power grid’s components.