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• Inventory games and carbon emissions in multi-echelon supply chains are studied. • Cost and emissions reduction are achieved by synchronizing the ordering cycles. • An algebraic method is used to find solutions for vertical cooperation. • The models are developed under the carbon tax policy. • The effect of the carbon tax on cost, emissions, and cooperation savings are examined. This paper studies carbon tax policy in inventory games of multi-echelon supply chains. Four structures are considered with regard to the decision-making of supply chain members (decentralized, vertical downward cooperation, vertical upward cooperation, and horizontal cooperation). The purposes are to find the solutions of inventory games at each cooperation scheme, to compare strategies, and to examine the impact of the carbon tax on costs, emissions, and cooperation savings. A coordination mechanism with synchronized cycles is proposed, using cooperative game theory concepts in SC centralization. Closed-form equilibrium values were calculated for the optimal replenishment cycles, costs, and carbon emissions functions. For this intent, an algebraic method was deployed for vertical cooperation games. Though, other games were solved by an exact solution method. The numerical examples examined the impacts of the carbon tax on each structure and compared optimal solutions. Moreover, the impact of inventory and carbon emissions parameters on the cost and carbon emissions savings from cooperation settings were evaluated, indicating that SCs can reduce both costs and carbon emissions through cooperation. Additionally, it was found that the carbon tax can reduce the level of carbon emissions, while simultaneously decreasing coalition costs and emissions savings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Transmission and maintenance departments cooperate to follow UN sustainability goals. • Sustainability management of water distribution network by cooperative game theory. • Network dynamic zoning through cooperative game theory during privatization. • Non-linear mixing of qualitative and quantitative factors in network utilities. • Fair operation and income sharing of water distribution network (WDN). Sustainability approaches in transmission and maintenance planning of network utilities with different maintenance zones, which involve increasing the reliability of the transfer while employing maintenance teams fairly, pose decision-making challenges. For these utilities, the best model to employ is the maximum-flow model, which considers network reliability and destination stocks. Therefore, the crucial point is to understand how zone owners can cooperate to maximize reliable transmission and increases maintenance income while considering fairness in allocating maintenance teams to maintenance centers over several periods. A Mixed-Integer Nonlinear Programming (MINLP) model is presented in this paper for maximizing the reliability in network utility issues, allocating maintenance teams, and exploring edge statuses. Cooperative Transmission and Maintenance (CTM) planning is a dynamic, multi-duration decision-making problem. The problem scale in this study is significantly enlarged to separate the lines into active, inactive, and in-repair sets, and consequently, a genetic algorithm is proposed. This study examines these approaches and analyses them (including collaborative outputs and managerial insights) in the context of three major water pipeline zones. Based on sensitivity analysis for 81 collaborative scenarios, the highest and lowest synergies were 83.61% and 36.7%, respectively. Finally, several collaborative income-sharing strategies based on game theory are investigated. [ABSTRACT FROM AUTHOR]

Nowadays, a serious environmental challenge is the pollution emitted by combustion engine vehicles. Governments have found that replacing these with electric vehicles (EVs) is a viable solution for sustainable transportation. The sustainable operation of fast charging stations for EVs is a critical parameter in these vehicles' popularity. Herein, a propose a model for the sustainable evaluation of energy sources of charging stations based on the R-numbers and combined compromise solution method (R–COCOSO). The construction of EV charging stations is done based on new and developing technologies, and they are often expensive, complex, and high-risk. Thus, we developed the combinative distance-based assessment (CODAS) method based on R-numbers to assess the project risk. Moreover, a linear programming model was developed to assess the contractors' risk response and the optimal allocation of their workload based on R-numbers and the ε − constraint approach. A real case of building charging stations in Tehran, Iran, was presented to test the accuracy of the model. [ABSTRACT FROM AUTHOR]

This study investigates equilibrium between green and non-green product types under different government intervention schemas. To this end, we establish production competition models of a set of green and non-green supply chains (GSCs and NGSCs, respectively). GSCs and NGSCs are two-echelon supply chains (SCs) that present green and non-green types of a product to a market, respectively. We consider two schemas of governmental intervention: direct tariffs (DTs) and tradable permits (TPs), both with and without baselines. This research seeks to evaluate how the GSCs and NGSCs respond to the DT or TP schemas. To establish the best SC response strategies, we formulate three-level non-linear programming problems for four possible governmental intervention scenarios. We find that this problem is multidimensional with different system stakeholders including the government, SCs, consumers, and the environment. In fact, different schemas result in different satisfaction levels of stakeholders. Thus, an appropriate schema can be selected by considering corresponding effects on the stakeholders. The comprehensive evaluation of a case study on residential building construction SCs yields significant managerial insights. [ABSTRACT FROM AUTHOR]

We develop price-energy-saving competition and cooperation models for two green supply chains (GSCs) under government financial intervention. First, we study the best response strategies of the chains for the given tariffs of a government. Second, we formulate 16 mathematical programming models regarding governments’ energy-saving, social welfare, and revenue-seeking policies. We find that the government can orchestrate GSCs to fulfil the financial, social, and environmental objectives by an appropriate tariff mechanism. Moreover, cooperation in a GSC and between GSCs may facilitate the government’s sustainable development policies. A comprehensive analysis on case study of brick production GSCs reveals some important managerial insights. [ABSTRACT FROM AUTHOR]

By considering target values for attributes in addition to beneficial and non-beneficial attributes, a traditional MADM technique is converted to a comprehensive form. In many machine selection problems, some attributes have given target values. The target value regarding a machine attribute can be reported as a range of data. Some target-based decision-making methods have recently been developed; however, a research gap exists in the area. For example, fuzzy axiomatic design approach presents a target-based decision-making supported on common area of membership functions of alternative ratings and target values of attributes. However, it has detects on finding a complete ranking because of probable infinite values of assessment index. Two target-based VIKOR models with interval data exist in the literature; however, the target values of attributes or ratings of alternatives on attributes are crisp numbers in the models and their formulations may have some limitations. The present paper tries to fill the gap by developing the VIKOR method with both interval target values of attributes and interval ratings of alternatives on attributes. Moreover, we attempt to utilize the power of interval computations to minimize degeneration of uncertain information. In this regard, we employ interval arithmetic and introduce a new normalization technique based on interval distance of interval numbers. We use a preference matrix to determine extremum and rank interval numbers. Two machine selection problems concerning punching equipment and continuous fluid bed tea dryer are solved employing the proposed method. Preference-degree-based ranking lists are formed by calculating the relative degrees of preference for the arranged assessment values of the candidate machines. The resultant rankings for the problems are compared with the results of fuzzy axiomatic design approach and the interval target-based MULTIMOORA method and its subordinate parts. [ABSTRACT FROM AUTHOR]

Currently, governments that maintain sustainable objectives often adopt financial incentives and deterrents to orchestrate the outsourcing decisions of manufacturers. This work investigates the effect of government financial intervention on the competition and cooperation of two manufacturers. One manufacturer pursues an in-house production strategy, and the other outsources production to a foreign supplier. Regarding the financial, environmental and social objectives of the government and the leadership role of the government in the market, this problem is formulated as a multi-level, multi-objective decision making model. We found that specific boundaries for tariffs set by the government lead to a stable competitive or monopolistic market. A comprehensive analysis of the government policies reveals the possible outcomes of the policies regarding the sustainable objectives. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model is proposed to maximize market performance including tradable certificates. • The long-term impacts of regulations on market are discussed by fine and subsidy. • Three important objectives for government are investigated under four scenarios. • The obtained results are validated by Turkey's energy data as a real case study. • Fine policy is more effective than a subsidy to empower renewable electricity. In the last decades, due to the increasing environmental concerns such as energy-resource depletion and global warming, energy efficiency instruments and renewable energy production has been promoted more than ever. This paper investigates a comprehensive comparison between two energy markets, i.e., the tradable white certificate (TWC) market and the tradable green certificate (TGC) market. How to coordinate the two energy markets and what the coordination impacts become the main issues under a reward-penalty mechanism. This multi-agent problem is discussed in an electricity supply chain consisting of two populations of traditional energy suppliers who want to decide about using one of them under government intervention. In this regard, the behavior of the suppliers is analyzed under three different Scenarios, including traditional power generation, energy efficiency program (EEP), and renewable portfolio standards (RPSs). An evolutionary game is applied to formulate the problem. The results show that a mandatory fine policy is more useful than an energy-saving subsidy to motivate the traditional suppliers to move toward sustainable energy generation. This policy also helps governments to meet their sustainable-oriented goals, as we discussed in this work. The findings can be applied to extend sustainable-based energy projects in developing countries. [ABSTRACT FROM AUTHOR]

The MULTIMOORA method is developed using interval numbers based on fuzzy logic concept and a novel preference technique to deal with complex uncertain problems. By utilizing the fuzzy concept and considering relative weights for attributes, assessment values are obtained for subordinate parts of the interval weighted MULTIMOORA. We suggest an interval comparison matrix to calculate aggregate preference as an index to find the interval maximal objective reference point vector and rank the assessment values of the interval weighted ratio system and the full multiplicative form. The theory of dominance is employed to consolidate the subordinate ranks into the final ranking. Because of the reasonable assumption of alternative ratings as interval numbers in industrial problems and straightforward mathematics of the interval weighted MULTIMOORA, the proposed method can be effectively utilized by decision makers. A case study regarding materials selection of power gears is solved using the proposed method. Our final ranking for the practical case is verified with two interval-based and several crisp MADM methods. [ABSTRACT FROM AUTHOR]

Materials selection as a significant step in engineering design process can be effectively implemented with the aid of MADM methods. The traditional MADM approaches only focus on beneficial or non-beneficial attributes to choose the optimal material; however, the selection should be made considering target values of attributes in some cases. In spite of practical applications of the target-based decision-making specially in engineering design problems, few studies have focused on the field. In the present paper, we developed a target-based MULTIMOORA method through introducing a normalization technique for materials selection in biomedical applications. We utilized two combinations of subjective, objective, and inter-attribute correlation effect significant coefficients to give importance to attributes. The information entropy and standard deviation concepts were used to assign two objective significant coefficients. This assumption leads to two modes of solution. Each mode has three subordinate and a final rankings. The final ranks of the two modes were consolidated into an aggregate ranking. The final and aggregate rankings were obtained using the dominance theory. We discussed two real-world biomedical engineering problems including materials selection for femoral components of hip and knee joint prostheses. Our aggregate rankings for the practical cases were compared with the results of previous studies. [ABSTRACT FROM AUTHOR]

• Proposing a competitive approach in hybrid flow shop scheduling. • Using multiple leaders and multiple followers game for hybrid flow shop scheduling. • Using a competitive genetic algorithm for evaluating Nash equilibrium. It is logical that organizations pay more attention to their major customers which have more impacts on their abilities or benefits. In many real cases, decision makers in scheduling system must prioritize the sequence of processing orders according to the agent of orders while the classic production scheduling do not have the ability to consider the relationships between agents. These problems can be modeled by the Stackelberg problems in the field of game theory. In this study, an agent-based hybrid flow shop problem is formulated by using the multi-leader multi-follower mathematical model. Each level is evaluated by Nash equilibrium and Nash bargaining with the new co-evolutionary and genetic algorithm. The proposed model is validated in a real case problem in the tire manufacturing industry. The results show that Nash equilibrium is superior to Nash bargaining for all agents. [ABSTRACT FROM AUTHOR]

This paper investigates the green technology development (GTD) problem of a capital-constrained manufacturing entrepreneur who seeks financial support from external and internal multi-sided platforms (MSPs). Following the government's environmental policies, MSPs (i.e., outsourcing and insourcing alternatives) determine their strategies and offer platform-driven solutions to the manufacturing entrepreneur pursuing green innovation. Using benchmark models, we first analyze decisions made by the manufacturing entrepreneur and the MSP under the deregulated scenarios. We then develop a three-level game-theoretical model and sequentially characterize the decision-making behavior of players under three platform power structures. The model outcomes are compared by considering the government's policymaking approach and platforms' power structure. Results reveal that, when coupled with appropriately designed trade-credit and revenue-sharing mechanisms and the government's innovation-based social welfare approach, the internal MSP system always outperforms the external alternative. Our study demonstrates that a win-win agreement among GTD players can only be achieved if the MSPs take the balance of power and remain committed to empowering the initiated sustainable ecosystem. In power struggle scenarios, however, GTD players prioritize the internal MSP over the external MSP. To fulfill the GTD goals, players are encouraged to use their structural and legislative power and adjust their strategies, investment decisions, and power preferences accordingly. • Green technology development under trade credit and revenue sharing mechanisms is considered. • Interactions between the manufacturing entrepreneur, platforms, and government are investigated. • Financing and retailing solutions of external and internal digital platforms are studied. • The government involvement in platform economy is evaluated. • The impact of platforms' power structure on sustainable development and eco-innovation is studied. [ABSTRACT FROM AUTHOR]

Many decision-making problems in the context of transhipment and logistics, distribution networks, airline planning and so on, can best be analyzed by the means of maximum-flow models in networks. In a multiple-owner network, several players possess arcs and nodes of a network. Since parameters of the network in many real problems are highly uncertain, maintaining a stable flow is as much important as maximizing the flow passing through the network. Thus, a key question is how the independent owners of a network should collaborate to maintain a reliable maximum flow. We address this question by presenting a stochastic mathematical programming model for the multiple-owner graph problem under uncertainty. Afterwards, a number of collaboration methods are studied based on the game theory. These methods are illustrated with an example to gain an insight into properties of the corresponding game results and behavior of the different solution concepts. [ABSTRACT FROM AUTHOR]

Safe and economical solid and hazardous waste disposal is a serious global concern and challenge. Therefore, the production of new environmentally friendly products for safe waste disposal and its conversion into energy is a major need of governments and companies manufacturing waste management products. An R&D project mainly aims to present an innovation and solve a problem in the real world, and is thus very expensive and riddled with technical complexity and uncertainty. The accurate selection of the project and its risk assessment improve the project's success rate and limit the companies' costs. This article aims to review the important criteria for the accurate selection of new projects for safe municipal solid waste disposal and also to identify and evaluate the risk factors affecting these projects' performance. Herein, a new fuzzy risk-based decision model is presented to achieve the stated goals. This model utilizes MCDM methods and develops them based on R-numbers method which has recently been developed to model the risk of fuzzy numbers in dynamic environments. The proposed model is successfully applied in the Waste Management Organization of Tehran, Iran. The results reveal the superiority of the plasma pyrolysis system project over other projects, e.g., launching power plants based on landfill, plasma gasification, and waste pyrolysis system. The scores of the four technologies evaluated based on the R-TODIM method are1, 0.835, 0.789, and 0, respectively. The results of risk assessment of the plasma pyrolysis system also indicate the importance of risk factors, e.g., access to technology and laboratory equipment and new scientific resources, compared to other risk factors. The scores of the three mentioned risk factors are 4.864, 4.798, and 1.1, respectively. • We considered green NPD in Sustainable Municipal Waste Management. • A new model was developed to assess the risk factors of green NPD projects. • We compared waste-to-energy technologies from new aspects in developing countries. • R-TODIM and R-MULTIMOOSRAL methods were used to select waste-to-energy technologies. • Plasma pyrolysis was recognized as one of the best technologies for waste disposal. [ABSTRACT FROM AUTHOR]

The design of network utilities which share resources or infrastructural elements, and which aim for cleaner transmission processes, involves many decision-making issues. These utilities can best be designed with regard to the mean of various maximum-flow models, while considering network reliability as a quality factor. The development of such innovative interconnectivity within industrial domains including information and communication technology (ICT) results in synergistic genesis and increased sustainability, creating an interdependent network of multi-industrial actors. In multiple-owner network utilities, various industries control edges and vertices within infrastructural networks. Network parameters differ widely in many real-world problems; engineering and designing a reliable flow can be as important as maximizing network flow while remaining compatible with environmental conditions. A key question, therefore, is how independent industry owners can collaborate symbiotically to implement a maximum green reliable flow (MGRF), one which decreases greenhouse gases (GHGs) while maximizing network worth. This paper addresses this question by providing a mixed-integer nonlinear programming model (MINLP) for multi-ownership network utility issues under budgetary conditions and constraints, and with permissible GHG emission consideration. Penalty factors for GHG emissions are also examined. Subsequently, an analysis is performed on the effectiveness of acceptable reliability levels and GHG fines payable for various emissions permit levels. These methods and analyses are then reviewed in a multi-faceted study of three main service industries that use fiber-optic networks, along with the results of collaborative outputs and relevant managerial insights. Additionally, several collaborative revenue-sharing methods are analyzed, based on game theoretical studies in real-world situations. • Cooperative modeling of network utilities' fiber optic sector to reduce GHG emissions • Increasing fiber optic network reliability using a cooperative game approach • Simultaneous modeling of network flow and reliability allocation problem • Considering different dimensions to increase the coalitions' sustainability • Revenue sharing from the cooperation of network utilities' fiber optic sector fairly [ABSTRACT FROM AUTHOR]

• Complex conservationist-poachers interactions over a food web are modeled. • The model is built on a non-cooperative Stackelberg type multi-players game. • An overarching biotic intervention strategy keeps web at or near equilibrium. • Financing budget deficits from funds generated by poachers is mathematically modeled. • Population and poaching data alone are insufficient for setting deterrent penalties. Mitigating the poaching pressure on food webs under multiple constraints (including financial and ecological ones) remains an open problem within conservation. Within this field, mathematically modeling the effects of poaching threats on managerial decision-making is a novel approach. To fill this scientific gap, the present paper uses a security game approach to model the interactions between an environmental manager (defender) and a group of profit-seeking pursuit poachers (attackers) who target species which are nodes of the food web. Based upon the non-cooperative Stackelberg game, the objective of the defender (as leader) is to keep the food web at or near equilibrium through optimally manipulating the populations of an optimal subset of species. In contrast, each attacker strives to maximize monetary profit by hunting an optimal population size of the selected species. The model is validated by a numerical example examining the food web of the endangered Persian leopard (Panthera pardus saxicolor), which lives in Golestan National Park (GNP), Iran. The model provides an overarching biotic intervention strategy to keep the (1 predator-4 prey) food web near equilibrium, while only 2 prey species (the urial (Ovis vignei) and the red deer (Cervus elaphus)) are directly threatened by poachers. The examination revealed that both species population data and poaching data should be taken into account to set effective multi-species conservation prioritization levels. In a sensitivity analysis approach, it was found that, despite the fact that red deer is endangered and preferred by poachers, the deterrent penalty measure should be 1.5 times greater for poaching urial than red deer. The output analysis illustrated that, in order to bring the urial deterrent penalty measure closer to the red deer one, enforcement measures should be about 2.5 times stricter for poaching urial than red deer. The results specifically yield insight into how to optimally conserve a food web equilibrium under poaching pressure and within several constraints. [ABSTRACT FROM AUTHOR]

• Proposing a game-based simulation–optimization approach in HFS scheduling. • Using bargaining approach to determine the optimal priority processing rule in HFS. • Using scheduling game in an HFS exposed to the stochastic failure of machinery. • The efficiency of the approach due to the significant reduction of makespan. Dynamic scheduling using real-time data in manufacturing systems enables quick response to unforeseen system events to reduce costs and makespan while increasing customer satisfaction. Since many production systems are multi-product, each product's customers aim to receive the product in the shortest possible time, thus competing with each other. Extant research neglects to consider not only competition between customers, but also bargaining strategies. In this paper, a hybrid flow shop system with multi-product is regarded. The production system studied is the Alborz Tire Company (Iran), which uses multi-type machines subject to stochastic failure. The objective is to determine the product processing prioritization in workstations, based on the Nash bargaining model, to minimize makespan. To this end, a simulation–optimization approach based on discrete-event simulation and Simulated annealing is employed. The results of the case study show that makespan is reduced significantly for all players. [ABSTRACT FROM AUTHOR]

• We consider food aid and life necessities by government during the home quarantine. • We considered temporary stations for waste collection and warehouses for distribution daily necessities and health supplies. • Separated hazardous infectious waste by citizens reduces the risk of COVID-19 virus. • Combination of Benders decomposition method with KKT conditions as solution method. • We examine 4 possible scenarios for the severity of the outbreak in a bi-level game. 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. [ABSTRACT FROM AUTHOR]

• Strategic interactions over the food web significantly affect conservation prioritization. • Security game approach and system reliability theory are used for ecological modeling. • Model prioritizes food web species to allocate defensive resources vs. a strategic threat. • Model quantifies the variation in food web reliability as the agents' strategies change. • The model provides an overarching intervention strategy to minimize web unreliability. Multi-species conservation is of critical concern in ecosystem management science. In this context, modeling the effect of strategic threats on decision-making is a challenging problem that has not been sufficiently addressed. Using a security game approach, this paper investigates the optimal conservation of a food web against a strategic threat. The model builds upon the non-cooperative Stackelberg game, wherein conservator (defender) and adversary (attacker) play as leader and follower, respectively. The objective of the defender is to preventively maximize the entire web reliability, under financial and ecological constraints. The defender optimally manipulates the populations of an optimal subset of species to achieve this. In contrast, the attacker attempts to maximize web unreliability by decreasing the population of selected species, using limited resources. A meta-heuristic algorithm is developed to compute the equilibrium strategy, and the model is validated through numerical examples. Additionally, in a scenario-based approach, it is examined how the defense and attack strategies, as well as food web reliability, change as the population of keystone species change. The results also show that the combinational use of mathematical optimization and food web-specific conservation prioritization indices yields a practical tool for food web conservation prioritization. The results specifically yield theoretical insights into how to optimally control trophic cascade effects due to changing keystone species populations. A step-wise methodology is proposed to implement the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We considered a humanitarian environment composed of donors and non-governmental organizations (NGOs) that the non-profits may adopt competitive or coopetitive inter-organizational interaction for managing the disasters. We also assumed that the government intervenes in the relief operations by applying one of its two policies; social welfare maximization (SWM) or budget consumption minimization (BCM). Using game theory (GT) approach, we develop 4 scenarios and, as a result, 4 mathematical programming models for examining the effect of the NGOs interactions and the government policies on the performance of donors, NGOs and government. We find that coopetition of NGOs facilitates the achievement of the government's objectives, and it also helps the non-profits to become more successful in providing relief. The government prefers to provide the indirect relief to the nonprofits to manage emergency operations successfully, and the financial aids are given only in the condition of reducing the level of NGOs cooperation. We also conclude that the cooperation of NGOs increases the donors' utility. A numerical example is conducted to test the findings of the models. • Cooperation and competition models are developed for humanitarian SCs interaction. • Leader-follower game model is established for interactions of the SCs and government. • Welfare maximization or budget consumption minimization are the government's policies. • The government prefers to give financial aids rather than reliefs to the NGOs. • Cooperation among NGOs in relief operations is beneficial for government and donors. [ABSTRACT FROM AUTHOR]

This paper introduces a framework comprising Geographic Information System (GIS) techniques and fuzzy Multi-Criteria Decision-Making (MCDM) methods for application in landfill site selection problem. The developed methodology is utilized in an environmentally critical area related to Mahallat city located in the northwest of Iran. In this process, fourteen effective parameters related to sustainability of the location of landfills are identified and categorized into three groups including environmental, economic, and social criteria. First, criteria weights are obtained by applying the group fuzzy Best-Worst Method (BWM) and then suitability maps are generated based on GIS analysis. From the spatial analysis, eleven potential sites are identified as the most suitable alternatives. These sites are then analyzed and ranked using group fuzzy MULTIMOORA method. This study is different from the previous researches applied the GIS-MCDM approach, in that group fuzzy BWM for weighting criteria is more reliable compared to the other common methods. The group fuzzy MULTIMOORA method, and its three subordinate approaches, select a same candidate, "Site A8" a region near urban area of Mahallat, as the most suitable location for disposing municipal solid waste. Image 1 • A hybrid approach to tackle a sustainable landfill site selection problem is suggested. • The fuzzy group BWM-MULTIMOORA for criteria weighting and alternatives ranking is used. • A GIS tool, i.e., ARCGIS10.5 software, to obtain suitability maps for landfill sites is employed. • The candidate locations for eco-friendly landfilling in Mahallat, Iran are evaluated. • The criteria of the problem are classified as environmental, economic, and social groups. [ABSTRACT FROM AUTHOR]

Highlights • An uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model is proposed. • Two meta-heuristic method and one exact method are provided to solve the model. • Several types of injured people are considered in earthquake evacuation planning. • Failure rate of the centers are modeled using reliability and probability concepts. • The proposed model has been applied in a real case study in Tehran, Iran. Abstract In this paper, an uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation mixed-integer mathematical programing model is proposed for the response phase of the earthquake. The proposed model includes five echelons as affected areas, distribution centers, hospitals, temporary accommodation centers and temporary care centers. Two objective functions as minimizing the total cost of the location-allocation of facilities and minimizing the amount of the shortage of relief supplies, are considered. The uncertainty is modeled using a scenario-based probability approach. The main decisions made by the proposed model are locating of the temporary care and accommodation centers, allocation of the affected areas to the located centers and hospitals, as well as the allocation of the distribution centers to temporary accommodation centers. Several decisions associated with flow of injured people and commodities between facilities, and decisions associated with number of vehicles between facilities and shortage and inventory level at centers are also made by the proposed model. Several sets of constraints including demand and flow of relief commodities, capacity of centers, transportation of injured people, capacity of transportation vehicles for commodities and injured people, and back up centers are considered in multiple periods of planning in the proposed model. The proposed model is applied in a real case study in Tehran. The model is solved using modified multiple-objective particle swarm optimization (MMOPSO), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and epsilon constraint method. The performance of the solution procedures is analyzed using multi-objective performance evaluation metrics. The results reveal the superiority of the MMOPSO over the other solution approaches. A preferred solution, from the set of non-dominated solutions generated by MMOPSO, has been selected, analyzed and described. Sensitivity analysis on main parameters of proposed model and the probabilities of the earthquake and failures of the facilities has also been accomplished. [ABSTRACT FROM AUTHOR]

Highlights • Using geographical location in distance matrixes cause extra optimization process. • The result of solved mixed integer programing is the input for metaheuristics. • Ant colony algorithm is selected metaheuristic for applying in promotion stage. • Horizontal collaboration of suppliers would lead to cost saving for these agents. • Shapley value and tau value are reliable payoff sharing technique for the problem. Abstract The impact of the horizontal collaboration of logistic providers as upstream supply chain elements is studied in this paper. The logistic providers can be considered as different players and, in order to analyze different types of coalitions by computing the synergy between the players in each cooperative structure, we developed a mixed integer programming (MIP) formulation for a cooperative inventory routing problem (Co-IRP). In this paper, each player has one depot and some retailers, which are considered as its available market. The model will have a many-to-many network structure once a coalition is formed, where a single product from different depots will be delivered to different retailers. We establish a robust version of Co-IRP, which is called Co-RIRP, considering uncertainty in the network. We developed a few metaheuristic algorithms to promote the gained results once MIP is solved. Finally, in order to share the possible cost saving between players and compare the assignments, we applied a few payoff distribution methods. In addition, a comprehensive analysis of example results and some important managerial insights are presented. [ABSTRACT FROM AUTHOR]

• An alternative SCF mechanism through a crowdfunding FinTech platform is proposed. • The role of multi-sided platforms in supporting supply chain reglobalization is studied. • The sequential interactions between the platform players are investigated. • The joint impact of a government's policy making and the platform's power structure is analyzed. This paper investigates the moderating role of a host government that promotes a multi-sided platform (MSP) as an alternative supply chain finance (SCF) solution. The MSP comprises equity crowdfunding, fixed-income funds, and low-rate lending facilities. We examine the sequential interactions between the host government (as the dominant legislator), a crowd of risk-averse small investors, and two competing SCs (local and foreign) that are engaged in Cournot competition. The players' equilibrium strategies are characterized under two platform power structures, namely small investors-led and SC-led. The joint impact of government legislation and platform's configuration on the performance of the proposed SCF mechanism is investigated. Results reveal that, while the regulated MSP outperforms a deregulated scenario, the profit-seeking behavior of the host government may intensify the power struggle between the local SC and small investors, and restrict the platform's overall performance. To successfully practice smart protectionism, policy makers are urged to reframe existing SCF schemes by leveraging their moderating influence and prioritizing social welfare over their short-term economic goals. This not only abates the power imbalance in MSPs, but also enhances the players' participation and enables host governments to further support their digital platform economy in the era of reglobalization. [ABSTRACT FROM AUTHOR]

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model. The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way. • Stochastic multi-objective optimization model is proposed for logistic distribution and evacuation planning. • Demands are assumed to be probabilistic and estimated using a simulation approach through several scenarios. • The stochastic optimization model is transformed to its deterministic equivalent using chance constraint programming. • The model is solved using an efficient epsilon-constraint approach and non-dominated sorting genetic algorithm (NSGA-II). • The proposed optimization model and solution approach are used to handle the case study of Tehran earthquake. [ABSTRACT FROM AUTHOR]

Performance measurement is one of the most essential real life decision making problems faces several criteria and different types of uncertainty and vagueness. Human expertise is mandatory for such situation although it is not enough to overcome such complicated cases. Expert systems which can sense, analyze and incorporate all details of real life decision making problems are required. In this paper an expert system is proposed in form of an uncertain Data Envelopment Analysis (DEA) approach. More formally a mixed fuzzy-robust uncertainty is addressed in the proposed DEA. Two scenario-based robust DEA models under Constant Return to Scale (CRS) and Variable Return to Scale (VRS) conditions have been proposed. Since the observed values for the inputs and outputs of Decision Making Units (DMUs) in each scenario may be ambiguous or vague, fuzzy DEA models corresponding to each of the robust scenarios are developed. So, this paper proposes four fuzzy-robust DEA models, which simultaneously maintain the advantage of each of the fuzzy and robust approaches, and at same time calculate the upper and lower bounds of the efficiency scores of DMUs under CRS and VRS conditions. Finally, to evaluate the validity and applicability of the proposed models, two numerical examples and a real case study in Small and Medium-sized Enterprises (SMEs) are presented and discussed. [ABSTRACT FROM AUTHOR]

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. [Display omitted] [ABSTRACT FROM AUTHOR]

Governments apply economic motivating forces and penalties to manage environmental effects of enterprises. Therefore, companies and manufactures will need to move toward environmental and sustainability assessment to survive and remain competitive in market. We consider market demands so that non-green and green products can be substituted with each other. If green products cannot overwhelm the market, manufacturers might select hybrid production mode including green and regular products. The government acts as a leader and sets special tariffs for non-green and green products as a controlling tool. We formulate a game theoretical model in twelve scenarios based on government policies and different types of production modes of a supply chain (SC). We calculate the optimal tariffs, selling prices, and prices of raw materials. The results indicate that environmental protection strategy of government has significant impact on the government revenue and profit of the SC's members. Moreover, the profits of a centralized SC is higher than a decentralized SC. When the government chooses the environmental protection and revenue seeking policies, the profit functions of the government and members of SC increase in centralized scenarios under hybrid production mode. Besides, it can be seen that Hybrid production mode has positive role on participating enterprises. Results also show that when government focuses on increasing income, the environmental impacts of SCs will increase in both decentralized and centralized SCs. • We examine effects of three policies of government on the hybrid supply chain. • The policies' efforts on prices of products and row materials and greening efforts are evaluated. • We study the hybrid production strategy of the supply chain in each policy. • Environmental production policy improves green production in the hybrid strategy. • In all scenarios, the profit of centralized supply chain is greater than decentralized one. [ABSTRACT FROM AUTHOR]