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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.

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.

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.

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.

One of the problems in post-earthquake disaster management in developing countries, such as Iran, is the prediction of the residual network available for disaster relief operations. Therefore, it is important to use methods that are executable in such countries given the limited amount of accurate data. The purpose of this paper is to present a multi-objective model that seeks to determine the set of roads of a transportation network that should preserve its role in carrying out disaster relief operations (i.e. known as “emergency road network” (ERN)) in the aftermath of earthquakes.

In this paper, the total travel time of emergency trips, the total length of network and the provision of coverage to the emergency demand/supply points have been incorporated as three important metrics of ERN into a multi-objective mixed integer linear programming model. The proposed model has been solved by adopting the e-constraint method.

The results of applying the model to Tehran’s highway network indicated that the least possible length for the emergency transportation network is about half the total length of its major roads (freeways and major arterials).

Gathering detailed data about origin-destination pair of emergency trips and network characteristics have a direct effect on designing a suitable emergency network in pre-disaster phase.

To become solvable in a reasonable time, especially in large-scale cases, the problem has been modeled based on a decomposing technique. The model has been solved successfully for the emergency roads of Tehran within about 10 min of CPU time.

Supply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs a resilient retail supply chain network for perishable foods under the dynamic competition to maximize retailer's profits.

A two-stage mixed-integer non-linear model is presented for designing the supply chain network. In the first stage, an equilibrium model that considers the characteristics of perishable foods is developed. In the second stage, a mixed integer non-linear programming model is presented to deal with the strategic decisions. Finally, an efficient memetic algorithm is designed to deal with large-scale problems.

The optimal the selection of suppliers, distribution centers and the order allocation are found among the supply chain entities. Considering the perishability of agri-food products, the equilibrium retail price and selling quantity are determined. Through a numerical example, the optimal inventory period under different maximum shelf life and the impact of three resilient strategies on retailer's profit, selling price and selling quantity are analyzed.

As for future research, the research can be extended in a number of directions. First, this paper studies the retail supply chain network design problem under competition among retailers. It can be an interesting direction to consider retailers competing with suppliers. Second, the authors can try to linearize the non-linear model and solve the large-scale integer programming problem by exact algorithm. Finally, the freshness of perishable foods gradually declines linearly to zero as the maximum shelf life approaches, and it would be a meaningful attempt to consider the freshness of perishable foods declines exponentially.

This paper innovatively designs the resilient supply chain network for perishable foods under dynamic competition. The retailer's dynamic competition and resilient strategies are considered simultaneously when designing supply chain network for perishable foods. In addition, this paper gives insights into how to obtain the optimal inventory period and compare the retailer's resilient strategies.

This study aims to explore the effect of power-based behaviours on pharmaceutical supply chain (PSC) resilience.

This study used a mixed-method approach to explore the role of power-based behaviours in PSC resilience. Qualitative interviews from 23 key PSC stakeholders, followed by thematic analysis, revealed the underlying perceptions regarding PSC resilience. Quantitative propositions were then developed based on the themes adopted from PSC resilience literature and the qualitative findings. These were tested via a survey questionnaire administered to 106 key stakeholders across the various levels in the PSC. Structural equation modelling with partial least squares was used to analyse the data.

The data analysed identified proactive and reactive strategies as resilience strategies in the PSC. However, power-based behaviours represented by quota systems, information and price control influenced these resilience strategies. From a complex adaptive system (CAS) perspective, the authors found that when power-based behaviours were exhibited, the interactions between PSC actors were mixed. There was a negative influence on reactive strategies and a positive influence on proactive strategies. The analysis also showed that PSC complexities measured by stringent regulations, long lead times and complex production moderated the effect of power-based behaviour on reactive strategies. Thus, the negative impact of power-based behaviours on reactive strategies stemmed from PSC complexities.

This research particularly reveals the role of power-based behaviours in building PSC resilience. By evaluating the nexus from a CAS perspective, the analysis considered power-based behaviours and the moderating role of PSC complexities in developing resilience strategies. This study considers the interactions of PSC actors. This study shows that power asymmetry is a relational concept that inhibits the efficacy of reactive strategies. This study thus advocates the importance of power in achieving a more resilient PSC from a holistic perspective by highlighting the importance of the decision-making process among supply chain (SC) partners. The findings are particularly relevant if PSC resilience is viewed as a CAS. All the interactions and decision-making processes affect outcomes because of their inherent complexities. Although this study focused on the PSC, its implications could be extended to other SCs.

The authors identified that power-based behaviours influenced resilience strategies. It was detrimental to reactive strategies because of the complexities of the PSC but beneficial to proactive strategies through resource-sharing. PSC actors are therefore encouraged to pursue proactive strategies as this may aid in mitigating the impact of disruptions. However, power-based behaviours bred partner dissatisfaction. This dissatisfaction may occur even within strategic alliances indicating that power could be detrimental to proactive strategies. Therefore, it is pertinent to identify conditions that lead to dissatisfaction when pursuing strategic partnerships. This study provides insight into actual behaviours influencing resilience and quantifies their effects on the PSC. These insights will be valuable for all SC partners wanting to improve their resilience strategies.

Previous PSC management and resilience studies have not examined the role of power in building resilience in the PSC. This paper thus provides a unique contribution by identifying the role of power in PSC resilience, offers empirical evidence and a novel theoretical perspective for future practice and research in building PSC resilience strategies.

The estimation of market value of intangible benefits of afforestation has always been a challenging task, and the contingent valuation method is a popular method used in environmental assessment. The NTPC set up a coal-based power plant in Korba, India and planted 1.6 million trees on 19% of the project area.

The individual's mean and median willingness to pay (WTP) for four intangible benefits, namely, pollution control (PC), improvement in underground water level (IUGWL), soil conservation and remediation (SCR) in addition to total WTP from the afforestation program of NTPC were estimated using a customized procedure for logit model based upon respondent's age, education, occupation, income and bid amount asked to pay. Stratified multistage random sampling has been used to select the respondents.

The procedure increases the number of respondents who are willing to pay as compared to conventional CVM. The finding of the study shows that the highest WTP was observed for PC (Rs. 462.84 per month per household) followed by SCR and IUGWL, whereas for total WTP it was Rs. 972.60.

The proposed customized procedure and the results thereof would be useful in improving the WTP estimates for other similar studies in order to conserve the environment.