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Concerning the overwhelming advantages of solar energy, controlling and exploiting solar energy by using solar panels is one of the main fields of research in the domain of renewable energy. The choice of solar panel technology is highly significant to exploit as much energy as possible. In this paper, the main goal is to select the best technology for solar panels by investigating nine technologies from the first, second, and third generations of solar panels. Moreover, five sustainable criteria of electrical, mechanical, economic, technical, and climate, and 20 subcriteria are given for making decision analysis. Then, the best–worst method is employed according to the experts' opinions for weighting the criteria and for comparing the ranking of the solar energy technologies a framework based on the MULTIMOOSRAL multiple criteria decision‐making method is proposed. Finally, sensitivity analysis will be conducted on the ranking of the technologies. By exploiting the proposed methodology, CIS/CIGS and Perovskite Solar cell are ranked 1 and 2 as the best solar panel technologies for the selected locations. [ABSTRACT FROM AUTHOR]

• 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]

• We prepare on overview of MULTIMOORA by analyzing 106 main studies. • We scrutinize the theoretical features of MULTIMOORA. • We discuss the developments of MULTIMOORA based on seven uncertainty theories. • We present the practical applications of MULTIMOORA categorized in five sectors. • The challenges are posed concerning theoretical features and practical applications. MULTIMOORA is a useful multi-criteria decision-making technique. The output of the MULTIMOORA is a ranking obtained by aggregating the results of the ternary ranking methods: Ratio System, Reference Point Approach, and Full Multiplicative Form. In the literature of MULTIMOORA, there is not a comprehensive review study. In this paper, we conduct an overview of MULTIMOORA by categorizing and analyzing main researches, theoretically and practically. First, we go through an theoretical survey of MULTIMOORA in terms of the subordinate ranking methods, ranking aggregation tools, weighting methods, group decision-making, combination with other models, and the robustness of the method. We scrutinize the developments of MULTIMOORA based on uncertainty theories accompanied by analyzing the mathematical formulations of breakthrough models. Practical problems of MULTIMOORA are categorized into application sectors concerning industries, economics, civil services and environmental policy-making, healthcare management, and information and communications technologies. Bibliometric analyses are implemented into all studies. Also, we pose major theoretical and practical challenges. From the theoretical viewpoint, extensions of Reference Point Approach, cooperative group decision-making structure, and utilization of new uncertainty sets in MULTIMOORA model are the main challenges. From the practical viewpoint, industrial and socio-economic fields are appealing to be studied intensively. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Sustainability is one of the most significant problems in today's world and the concept of sustainable development is being an important objective in construction projects. Since the large projects have enormous and different activities that should be scheduled by considering resource constraint and precedence relationship between them, the resource-constrained project scheduling problem (RCPSP) is a NP-hard problem. In this study, fuzzy project scheduling model is presented to solve RCPSP under uncertainty in availability of the resources and activities duration simultaneously. Subcontractors have incentive to share their resources in the form of coalition in order to reduce activities time and cost. In this research, cooperative game methods are introduced for fair allocating utility of the project, as well as, present supper-additivity, stability and satisfaction level of each coalition. Finally, sustainability concept is analyzed in RCPSP and cooperation of subcontractors in a coalition form, whereas in the previous researches these topics addressed separately or generally in project scheduling to minimize the makespan and maximize the profit of project. The results of the proposed model indicate that the subcontractors can obtain more profit and the balance between sustainability indicators in project management arises by collaboration. [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 paper studies the green new product development (GNPD) problem of a risk-averse capital constrained supply chain (SC). The SC is managed by an SME entrepreneur, seeking financial support from a multi-sided FinTech platform (MSP) to develop a portfolio of green and non-green products. The MSP offers the SC a combination of equity financing (EF) and debt financing (DF) facilities and must decide on the interest rate of its DF facility. Using a benchmark model, we first characterize the SC's production and the MSP's financing decisions under a deregulated scenario. Focusing on an alternative case with government intervention (i.e., hybrid environmental-green entrepreneurship policy), we next develop a three-level game theoretical model and sequentially characterize the decision-making behavior of government, MSP, and SC. The model outcomes are analyzed by considering the policy approach (i.e., economic influence vs. social welfare) and the platform's risk attitude. The results reveal that, when coupled with an appropriate government intervention policy, a regulated scenario leads to a better outcome, particularly when the MSP is risk-neutral and strikes a right balance between the EF and DF. The win–win situation may not be realized when the MSP is risk-averse and the host government is merely focused on its economic influence. To successfully promote sustainable supply chain finance (SSCF) through digital platforms, policy makers are urged to leverage their legislative power and prioritize green entrepreneurship and social welfare over their financial maximization agenda. [ABSTRACT FROM AUTHOR]

Olive is considered as one of the most important and useful products, but the traditional harvesting methods are failing to fulfill the current need; therefore, it is crucial to make the olive harvesting mechanized. In order to expedite the olive harvesting mechanization process, the engineers have designed various machines and equipment, which have their own special advantages. Now, the main challenge is to select the best olive harvesting machine to develop and improve the economic conditions in agricultural field to maintain the food demand. In the present study, we intend to present a decision support system to aid decision-making about olive harvesting machines. To achieve this target, we evaluate six candidate machines with nine important criteria and classify them into three groups: beneficial, non-beneficial, and target-based criteria. For weighting the criteria, the best-worst method is applied, and because of having target criterion in the selection problem, the decision matrix is normalized by the target-based technique. Finally, using two proposed methods, target-based MULTIMOORA and WASPAS, we select the best harvesting machine. In addition, we employ dominance method to integrate the resultant rankings of harvesting machines. [ABSTRACT FROM AUTHOR]

Production scheduling has a considerable impact on productivity and resource assignment. In many situations, every job has an owner, which is called an agent. Since the agents are independent and selfish, it is possible that they have not any incentive to cooperate. Scheduling games help us to understand interactions between the agents. In this study, we consider a real firm with a flow shop manufacturing system that receives various orders from different agents so that each order belongs to a unique agent and includes some jobs. We propose a Nash bargaining model to find a compromise solution among agents. We suppose the utilities of the agents in disagreement point are non-deterministic. Therefore, to overcome this problem, we used linear programming with interval coefficients in order to find the best and the worst Nash bargaining solution. To find a compromised solution, we propose an improved genetic algorithm and compare it with other meta-heuristic algorithms. The comparisons indicate that the proposed algorithm has a good potential to evaluation of Nash bargaining problem in hybrid flow shop environment. Based on the results to reach an agreement between agents, it is required to create a trade-off between usage rates of fastest machines at each stage especially in bottleneck stages and total processing time of orders. The results indicate that the Nash bargaining solution is suitable to solve real-life agent-based production scheduling with the consideration of interactions among the agents when disagreement points are under uncertainty. [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]

Metaheuristics have been extensively used for solving difficult optimization problems in recent years. However, according to the famous theory (No Free Lunch), it is infeasible for a metaheuristic to optimally solve all problems. As a result, novel metaheuristics have been incessantly introduced. In this paper, we provide a bibliography on recent development in metaheuristics. This work lists and categorizes 112 metaheuristic articles published from 2009 to 2015. The key aim of this work is to gather a group of metaheuristics that plays a fundamental role in development of metaheuristics in the coming years. This paper reveals that the growth of new metaheuristics continues as well as has not seen any interruption. Although this study cannot assert to be comprehensive, it involves a main part of novel publications and therefore, is a helpful guide for new metaheuristic researches. [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]

The selection of proper material is a formative stage in engineering design and production process. Without a systematic approach, many practical engineering materials may be neglected for selection. Multiple attribute decision making techniques as efficient methodologies can aid decision-makers in finding the optimal option. As a robust multiple attribute decision making approach, the MULTIMOORA method can be usefully employed in materials selection process. In this study, we extend the MULTIMOORA approach based on Shannon entropy concept under fuzzy environment to solve a materials selection case that is a group multiple attribute decision making problem. In the present paper, subjective weights of attributes and the ratings of alternatives on attributes are assumed as fuzzy sets. The entropy concept is considered to assign objective relative importance to decision-making attributes. The fuzzy subjective weights of attributes that are based on decision-makers comments and the crisp objective weights of attributes obtained using the entropy concept are consolidated to generate the fuzzy integrated entropy weights. We evaluate a practical materials selection problem related to automotive industry. We perform a sensitivity analysis by altering aggregation and normalization techniques besides the types of weighting. The materials rankings of the example are compared with the related study. [ABSTRACT FROM AUTHOR]

This study investigates a multiowner maximum-flow network problem, which suffers from risky events. Uncertain conditions effect on proper estimation and ignoring them may mislead decision makers by overestimation. A key question is how self-governing owners in the network can cooperate with each other to maintain a reliable flow. Hence, the question is answered by providing a mathematical programming model based on applying the triangular reliability function in the decentralized networks. The proposed method concentrates on multiowner networks which suffer from risky time, cost, and capacity parameters for each network’s arcs. Some cooperative game methods such as τ-value, Shapley, and core center are presented to fairly distribute extra profit of cooperation. A numerical example including sensitivity analysis and the results of comparisons are presented. Indeed, the proposed method provides more reality in decision-making for risky systems, hence leading to significant profits in terms of real cost estimation when compared with unforeseen effects. [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]

We develop a distribution network model for a decentralized supply chain to investigate the optimal decisions of participants in an uncertain demand environment. The supply chain consists of one manufacturer and set of independent candidate distributors that distribute products to geographically dispersed markets in a competitive manner. All chain's participants are assumed risk-sensitive. The stability of a distribution network depends on the fuzzy commitment of its distributors. The novelty of the paper lies in the manufacturer concerns about the preferences of the distributors to ensure stability of his distribution network. Hence, the manufacturer considers criteria of expected profit, variance of profit, and stability level to evaluate distribution alternatives. A decision-making methodology is presented for evaluation and selection the distribution network design alternatives. The tactical decisions in each distribution alternative are obtained from a Stackelberg game formulated as a bi-level distributed programming problem. We show that the methodology is able to capture a principal trade-off between the preferences of manufacturer and contractor distributors. Managerial insights are obtained through an illustrative example and its sensitivity analyses. [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]

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]

An integrated equilibrium model for tactical decisions in network design is developed. We consider a decentralized supply chain network operating in markets under uncertain demands when there is a rival decentralized chain. The primary assumption is that two chains provide partial substitutable products to the markets, and markets' demands are affected by tactical decisions such as price, service level, and advertising expenditure. Each chain consists of one risk-averse manufacturer and a set of risk-averse retailers. The strategic decisions are frequently taking precedence over tactical ones. Therefore, we first find equilibrium of tactical decisions for each possible scenario of supply chain network. Afterwards, we find optimal distribution network of the new supply chain by the scenario evaluation method. Numerical example, including sensitivity analysis will illustrate how the conservative behaviors of chains' members affect expected demand, profit, and utility of each distribution scenario. [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]

• 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]

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]

This study proposes a two-stage game–data envelopment analysis (DEA) approach for cost-efficiency (CE) measurement using centralized and Stackelberg game models. Each decision-making unit (DMU) is proposed to make up two-stage network structures (or processes), where all the first-stage outputs are the only second-stage inputs. The main contribution of this study is the development of cooperative and non-cooperative models for CE measurement and implies a unique CE decomposition. We apply centralized and Stackelberg approaches for cooperative and non-cooperative assumption between two stages. We proposed a simplified version of the two-stage DEA network CE model. Applying the proposed model for CE calculation reduces both the number of constraints and variables, resulting in a sharp reduction in computational requirements. The applicability of the presented model is demonstrated in the context of studying the output of the electrical network in Iran. The results of the case study show that in a two-stage network, the proposed DEA model can provide accurate estimates of CE. This paper aids the two-stage network structures to control the costs by weak DMUs management. To the authors’ knowledge, this paper is the first research on the CE in a network by the DEA approach so that the relationships between the internal stages of the network are considered. [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]

The benefits of using eco-friendly technologies along with their efficiency for EMS systems have caused to address the importance of drones in terms of performance and environmental aspects. In this study, by considering the applications of drone capability such as fast delivery along with a focus on the energy consumption of drone, a new bi-objective mathematical model of locationallocation problem of EMS systems is presented. In the first objective function, the impact of drone to maximize the expected survival of patients is investigated and in the second one, the minimization of CO2 emission of drone utilization in EMS systems is considered which is the most documented and well-known greenhouse gas often used to calculate pollution and energy impacts. The importance of patient's lives in comparison with the associated reduction of carbon emission has caused to be solved the model by a preemptive fuzzy goal programming approach to measure the achievement degree of objectives. By using data and obtained results from a similar study, the model is evaluated to show the applicability and benefits of drones in healthcare service and environmental aspects. The results show that drone utilization in comparison with regular ambulance vehicles can save more lives as well as emit less CO2. The results strongly support the notion that using drones for EMS systems is not only efficient but also is environmentally friendly. [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]

This paper addresses a new location-allocation-pricing problem in designing a three-level uncertain supply chain network with stochastic price-sensitive demands. Using the market segmentation problems, a supply-chain network is developed with two distribution channels that consist of Brick & Mortar and online markets, when some demand leakages occur from the market with a higher price. Due to the lack of physical observation of products in online markets, a return policy is used. So, demand behavior is analyzed in terms of the pricing and return policy. The problem established location, allocation, order quantities, pricing and refund price decisions to optimize the total profit of the chain. Furthermore, it is formulated as a mixed-integer non-linear programming (MINLP) model and solved by a Lagrangian relaxation algorithm. The numerical study and computational results indicate the efficiency and effectiveness of the proposed algorithm. [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 selection of waste lubricant oil regenerative technology regarding the complexity of the technologies and financial issues is a complex problem. Some risk factors exist regarding the ratings of technologies on the effective criteria. The current study tackles the selection of the technology based on fuzzy axiomatic design approach considering risk factors. Shannon entropy significance coefficients are computed for criteria. The problem is first solved by considering all criteria and then supplementary solutions are presented by categorizing the criteria to technical and economic groups. Two types of risk factors are identified for the technologies, i.e. general and specific risk factors. [ABSTRACT FROM AUTHOR]