A policy-making model for evolutionary SME behavior during a pandemic recession supported on game theory approach.

The global economy has experienced a tremendous shock caused by the Covid-19 pandemic and its effects on the normal activities of SMEs, which provide essential driving economic force. Considering that there is currently no precise prediction about the end of this pandemic, many SMEs must make critical decisions about whether to remain in the market during the pandemic or to leave it, investing their assets in a more secure sector of the economy. However, in order to convince SMEs to remain in the market, thus maintaining the damaged economy, governments may variously apply punitive or supportive measures. In this regard, the interaction between SMEs strategies and government measures can be considered as an evolutionary game, in which the governments impose various policies after observing the evolutionary behaviors of SMEs. An evolutionary stable strategy (ESS) is derived through a replicator dynamic system, and the available payoff of each player is calculated by Nash equilibrium (NA). Finally, a numerical example is presented, and related managerial insights are proposed at the end of the current study. For instance, contrary to general belief, it can be inferred from investigating possible scenarios that punitive policies are more effective than supportive measures in convincing SMEs to remain in the market.

Government intervention in municipal waste collection with a sustainable approach: a robust bi-level problem.

Conservation of the environment has taken a prime position among areas of concern for managers and practitioners worldwide. This study aims to provide a bi-level mathematical model for municipal waste collection considering the sustainability approach. The mathematical model with conflicting objects was proposed at the upper level of the model of maximizing government revenue from waste recycling and at the lower level of minimizing waste collection and recycling costs, which had stochastic parameters and was scenario based. A case study was conducted in the Saveh processing site (Iran). Due to the complexity of the bi-level model, the KKT approach was adopted to unify the model. Finally, the relevant calculations were performed based on actual information. The results of the problem in the case study showed the efficiency of the proposed method. Several computational analyses randomly generated different waste recycling rates and obtained significant management results.

Cooperation and coopetition among retailers-third party logistics providers alliances under different risk behaviors, uncertainty demand and environmental considerations.

Optimal profits for third-party logistics providers (3PLs) can be analyzed using the networks model to determine decision-making processes within transshipment and logistics, distribution networks, etc. Increasing academic attention is currently being focused upon fields examining 3PLs' operations within logistics networks. This paper studies cooperative game theory (CGT) of retailers-3PLs that make an alliance with each other with a specified demand function. The logistics network involves several suppliers and retailers-3PLs alliances, a distribution graph consisting of several nodes and arcs as well as multiple customers. Retailers-3PLs purchase the same goods from suppliers and sell to customers after shipping via the network; they also consider environmental issues to reduce pollutants and emissions fines. The proposed nonlinear programming (NLP) model aims to find the best flow and price of goods under cooperation conditions among retailers-3PLs by analyzing their risk levels. Controlling uncertainty in the models is accomplished by Mulvey's robust approach. In a general coalition, fair profit distribution methods are applied to share the profits among retailers-3PLs under different risk situations. We conduct a numerical analysis to present the application of our proposed model and find whether coalitions and cooperation between retailers-3PLs reduce costs and increase profits. Finally, we report the sensitivity analysis results regarding the penalties imposed for pollutant emissions, along with suggestions for future research. The results reveal that since their profit is greater in the coalition mode, they tend to cooperate with each other, whatever the amount of pollution fines be.

R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis.

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.

A hybrid mathematical modelling approach for energy generation from hazardous waste during the COVID-19 pandemic.

The COVID-19 virus in a short time has caused a terrible crisis that has been spread around the world. This crisis has affected human life in several dimensions, one of which is a sharp increase in urban waste. This increase in waste volume during the pandemic, in addition to the intense increase in costs associated with the risks of virus contagion through infectious waste. In this study, a hybrid mathematical modelling approach including a Bi-level programming model for infectious waste management has been proposed. At the higher level of the model, government decisions regarding the total costs related to infectious waste must be minimized. At this level, the collected infectious waste is converted into energy, the revenue of which is returned to the system. The lower level relates to the risks of virus contagion through infectious waste, which can be catastrophic if ignored. This study has considered the low, medium, high and very high prevalence scenarios as key parameters for the production of waste. In addition, the uncertainty in citizens' demand for waste collection was also included in the proposed model. The results showed that by energy production from waste during the COVID-19 pandemic, 34% of the total cost of collecting and transporting waste can be compensated. Finally, this paper obtained useful managerial insights using the data of Kermanshah city as a real case.

Hazardous infectious waste collection and government aid distribution during COVID-19: A robust mathematical leader-follower model approach.

The outbreak of COVID-19 has posed significant challenges to governments across the world. The increase in hazardous infectious waste (HIW) caused by the pandemic is associated with the risk of transmitting the virus. In this study, hazardous waste includes infectious waste generated both by individuals and by hospitals during the COVID-19 pandemic. To control the outbreak by maintaining social distance and home quarantine protocols, daily necessities and health supplies must be provided to the people affected. Governments play an essential role in the management of the crisis, creating an elaborate plan for collecting HIW and providing necessities and health supplies. This paper proposes a leader-follower approach for hazardous infectious waste collection and government aid distribution to control COVID-19. At the top level of the model, government policies are designed to support people by distributing daily necessities and health supplies, and to support contractors by waste collection. The lower level of the model is related to the operational decisions of contractors with limited capacities. Due to the potential risk of virus transmission via contaminated waste, the proposed model considers the complications imposed on contractors at the lower level. Applying a stochastic programming approach, four possible scenarios are examined, dependent of the severity of the outbreak. As a solution approach, the Benders decomposition method is combined with Karush-Kuhn-Tucker conditions. The results show that government support, in addition to much better management of citizen demand, can control the spread of the virus by implementing quarantine decisions.

Developing the modified R-numbers for risk-based fuzzy information fusion and its application to failure modes, effects, and system resilience analysis (FMESRA).

In order to identify and eliminate known or potential failures from the process of product design, development and production, failure mode and effect analysis (FMEA) have been widely used in a variety of industries as a useful tool in prognostics and health management, safety and reliability analysis. The traditional FMEA shows two significant flaws while calculating the risk priority number (RPN). First, recovery time that considerably affects the safety, cost, and sustainability of the system is not considered in the RPN calculation. Second, in order to capture different conflicting experts' views, especially when the obtained data are fuzzy, there is no mechanism. In order to overcome these issues, this paper presents a resilience-based risk priority number for considering the recovery and repair time of each failure mode, then a risk-based fuzzy information processing and decision-making is developed by modifying the R-numbers methodology and on the basis of simultaneous evaluation of criteria and alternatives (SECA) approach which is so-called R-SECA method. The capability of proposed models is tested by a case study of a centrifugal air compressor in a steel manufacturing company. Results show the robustness of proposed R-SECA model in dealing with different scenarios of risky information.

Interval MULTIMOORA Method Integrating Interval Borda Rule and Interval Best-Worst-Method-Based Weighting Model: Case Study on Hybrid Vehicle Engine Selection.

In this paper, we present an interval MULTIMOORA method with complete interval computation in which the interval distances of interval numbers and preference matrix are used. In addition, we propose a group interval best-worst method (BWM) with interval preference degree. The group interval BWM has a hierarchical structure of group decision making with two levels of experts. Beside employing the dominance theory to integrate subordinate rankings, we introduce the interval Borda rule as an aggregation function which does not have the defects of the dominance theory. We calculate the objective interval weights of criteria based on the interval entropy method, which are integrated by the subjective weights computed by the group interval BWM. The approach presented in this paper is verified by a real-world engineering selection problem of a hybrid vehicle engine based on real data and opinions of engineering design experts of the automotive industry of Iran. The preference-based and dominance-based ranking lists are presented for the problem. We solve the same case by employing the interval TOPSIS and VIKOR methods. Eventually, all resultant rankings are compared based on Spearman rank correlation coefficients.