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Online platforms provide sellers' products on their websites and earn a commission fee for each unit sold. Recently, many platforms have tried to refer customers to their direct competitors. In this paper, we explain this counter-intuitive practice by developing a game-theoretic model where two competing platforms contracting with one common seller or two competing sellers. We first analyze a benchmark case where platforms and sellers are integrated, finding that competitor referral will aggregate competition and thus neither platform is willing to refer its competitor voluntarily. However, when each platform serves as a marketplace for an independent seller, it is possible that a platform voluntarily refers its competitor. The rationale is that there exists a double marginalization problem when platforms set commission fees and sellers set prices, resulting in low efficiency of product selling. By referring visitors to their competitors, platforms can introduce cannibalization to cope with the double marginalization problem. We also investigate the case when the two platforms serve one common seller, finding that as long as the seller does not charge discriminated prices for the same product, the platforms may also apply referral. This is because the benefits of the two platforms are more aligned when the common seller sets a common price. Thus, online referral which helps the competitor may also help the referring platform itself. This paper also makes serval extensions to check the robustness of the model. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the optimal order quantities for a risk-averse newsvendor with backlogging, where it is assumed that all or part of the unsatisfied demands can be fullfilled by backordering. The optimal order quantities are derived under the famous conditional value-at-risk (CVaR) criterion and mean-CVaR criterion on controlling the profit loss due to the uncertainty of market demands. With the optimal order quantities to the proposed models, several important monotone properties are obtained and their relationships with the existing results are discussed. Moreover, it is shown in this paper that low risk means low expected profit while high expected profit comes with high risk. Finally, we present some managerial insights for the risk-averse newsvendor model under backlogging. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

Waste management is an important issue in the field of green logistics. It has consequently drawn the attention of the scientific community and has been extensively investigated over the past few years. Through an analysis of the existing waste management literature, we attempt in this paper to better understand past developments in this area as well as emerging trends and recent developments. Emphasis will be put mainly on Operations Research and Management Science techniques when dealing with waste management problems. To reach this target, we follow bibliometric-based methods, specifically Co-citation Analysis, Betweenness Centrality and Burst Detection combined with network visualization. After identifying the research papers published between 1990 and 2018 within the Thomson Reuters Web of Science database, a Co-citation network has been constructed. We propose an algorithm for modularity-based clustering in small networks that iteratively solves a sequence of Mixed Integer Non-linear Programming problems to maximize the modularity therefore providing a non-overlapping partition of the network. A display of the principal research areas and landmark articles that shape the intellectual structure of the waste management problems during the last 30 years is reported. [ABSTRACT FROM AUTHOR]

Stochastic multicriteria acceptability analysis (SMAA) is a family of multiple criteria decision making (MCDM) methods dealing with incomplete, imprecise, and uncertain information on the evaluations and preference model parameters. As it provides a general framework that has extensions to deal with various specificities in MCDM problems, the development of SMAA methods and their applications in real-life decision-making problems have been increased over the recent years. This paper provides an up-to-date literature review of different SMAA methods and their applications in various areas. First, we selected, from different on-line data base, 118 articles published between 1998 and 2017. We categorized the selected papers into theoretical and applied. While the theoretical papers were analyzed based on the method's aggregation procedure, type of problem, type of method's outputs and inputs, the applied papers were separated and analyzed by application areas. Then, we provide some descriptive statistics, analyzing the papers regarding to publication year and journals of publication. Finally, we provide some guidelines to assist decision-makers in the choice of a SMAA method on a specific decision-making context and some future research directions. [ABSTRACT FROM AUTHOR]

Humanitarian organizations work diligently to save lives using scarce resources, competing for donor money, and operating in complex environments. It is no surprise that they need information to effectively execute their task. As there have been tremendous developments in data analytics it is imperative that the domain of humanitarian supply chain management leverage the benefits offered by the advancement of big data. In this study, we have conducted a systematic literature review in the field of big data and humanitarian supply chain. The data was collected using Scopus which is the largest digital database. After careful screening, only 28 journal papers were selected for literature review. These papers have been classified and grouped into various categorizations. Future research directions in this field have been suggested that are based on various organizational theories. [ABSTRACT FROM AUTHOR]

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community. [ABSTRACT FROM AUTHOR]

This paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (Econom Theory, 14:783–793, 1998) and Park et al. (Econom Theory, 16:855–877, 2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (Manag Sci 44, 49–61, 1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications. [ABSTRACT FROM AUTHOR]

Nowadays, the integration of sustainability into supply chain management (SCM) is a key issue for ensuring corporate competitiveness in face of dynamic ecological and social environments. This paper reviews 185 journal publications of the last 20 years that formalize issues related to sustainable supply chain management (SSCM) in quantitative models. In a content analysis, modeling and SCM characteristics as well as sustainability and SSCM constructs are elaborated. The models are assessed numerically by counting frequencies of occurrence and by clustering the paper sample according to selected characteristics. The findings indicate that SSCM models predominantly focus on deterministic approaches and the integration of environmental aspects of sustainability while neglecting stochastic modeling techniques and the consideration of social factors. By now, comprehensive modeling approaches are most often employed on intra-organizational levels whereas broader application areas are assessed by less complex models. The integration of pressures and incentives of external stakeholders or the formalization of sustainable supplier management and sustainability risks are identified as future research perspectives. Furthermore, the interrelationships between the triple bottom line dimensions are to be scrutinized in greater detail in order to avoid focused optimization of selected sustainability criteria. Seven modeling guidelines are derived from the reviewed literature to facilitate future model-based SSCM research. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss risk management, data envelopment analysis (DEA) and peer-to-peer (P2P) lending.

Additionally, using the interpretation of the Banzhaf-Owen value, an alternative estimation procedure based on two-stage sampling is presented reducing the required computation time. Assuming these fixed costs to be independent of the number of passengers, suitable mechanisms for sharing the fixed costs are defined, including the uniform split, the proportional allocation and an intermediate method. This special issue is centered around papers that were submitted to the 2018 European Meeting on Game Theory SING 14, held in Bayreuth. [Extracted from the article]

Recent papers have provided a number of overviews in connection with the fast growing literature on bad outputs in Data Envelopment Analysis (DEA), such as defective products, emissions and waste. However, there does not seem to exist any comprehensive overview of the opposite phenomenon, particularly not regarding DEA, namely of bads as undesirable objects or factors which are desirable input (flow) into transformation processes, e.g. into waste incineration. Moreover, the terms 'bad input' and '(un-)desirable input/factor' are not clearly defined. We use a purely preference-based notion for the desirability of inputs and outputs. A systematic literature search reveals only 22 DEA articles which explicitly address the (desirable) input of bads as original undesirable factors, i.e. as input into the first stage of a single- or multi-stage process. Their detailed analysis shows that current approaches are based on two core ideas involving various efficiency measures. Only four papers deal with real applications of original undesirable factors, namely waste water treatment. Moreover, those disposability assumptions for DEA models often critically discussed in relation to bad outputs (e.g. weak disposability) are not used in these papers, presumably because the processes modelled are themselves disposal processes. Finally, we exemplarily demonstrate how DEA models with bads as inputs (and outputs) can be systematically derived from a decision-theoretic generalization of DEA methodology proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper we consider decision functions in which voters have three options and one of them is abstention or indifference and the output is a binary decision between two alternatives so that a tie is not possible. These resolute decision functions appear frequently in grading, in some sport competitions, in voting situations in which the status quo is put to the vote, etc. It is a more restricted case of the voting context considered in the seminal article by Kenneth May on decision functions, published in Econometrica in 1952, because the output set does not admit a tie. Among these resolute decision functions we focus on the study of majority functions, in which the number of favorable votes to an alternative must be strictly greater than the number of votes against it. This work provides an axiomatic characterization for the set of majority functions and for the relative majority function with status quo bias. Both characterizations are based on weaker versions of neutrality. Other complementary characterizations are also provided. [ABSTRACT FROM AUTHOR]

The generation maintenance scheduling deals with a time sequence of preventive maintenance outages for a given set of generation units in an electricity market subject to power system restrictions. Incorporating a leader–follower structure in generation maintenance scheduling models is essential because of the inherent conflict between the interests of an independent system operator (ISO) and generation companies (GENCOs). The present paper proposes a new preventive maintenance scheduling model for generation companies facing the risk of involving generation units' disruption and demand variations while ensuring the reliability of the power system. Each GENCO proposes the maintenance schedule of its generation units to the ISO in a non-cooperative manner intending to maximize its net profit. Then ISO reacts to the aggregated schedule according to the power system's reliability index. Thus, a new formula is developed to consider all the interactions between the power system's stakeholders. In this regard, a stochastic multi-leader one-follower approach is applied. The GENCOs are considered independent leaders at the upper-level and the ISO is considered a follower at the lower-level. Then an equivalent single-level counterpart model is presented for each leader. So, the whole problem is converted into multiple individual stochastic single-level models, and then the Nash Equilibrium concept is used to determine GENCO equilibrium strategies. The proposed methodology is evaluated using some modified IEEE reliability test systems. The numerical analysis confirms that the proposed model is more effective in cases with higher uncertainties. Moreover, the performed analysis demonstrated the importance of applying a bi-level approach to the problem. Finally, the superiority of the proposed approach compared to the existing one is confirmed. [ABSTRACT FROM AUTHOR]

In this paper, we study unimodality conditions for distributions that describe markets with stochastic demand. Such conditions naturally emerge in the analysis of game-theoretic models of market competition (Cournot games) and supply chain coordination (Stackelberg games). We express the price elasticity of expected demand in terms of the mean residual life (MRL) function of the demand distribution and characterize optimal prices or equivalently, points of unitary elasticity, as fixed points of the MRL function. This leads to economic interpretable conditions on the demand distribution under which such fixed points exist and are unique. We find that markets with increasing price elasticity of expected demand that eventually become elastic correspond to distributions with decreasing generalized mean residual life (DGMRL) and finite second moment. DGMRL distributions strictly generalize the widely used increasing generalized failure rate (IGFR) distributions. We further elaborate on the relationship of the two classes, link their limiting behavior at infinity and examine moment and closure properties of DGMRL distributions that are important in economic applications. The DGMRL unimodality condition is useful in the analysis of optimal decisions under uncertainty in settings that are not covered by the widely-used IGFR condition; thus, it can be of broader interest to the game-theory and operations research literature. [ABSTRACT FROM AUTHOR]

The study on how equilibria behave when perturbations occur in the data of a game is a fundamental theme, since actions and payoffs of the players may be affected by uncertainty or trembles. In this paper, we investigate the asymptotic behavior and the variational stability of the subgame perfect Nash equilibrium (SPNE) in one-leader one-follower Stackelberg games under perturbations both of the action sets and of the payoff functions. To pursue this aim, we consider a general sequence of perturbed Stackelberg games and a set of assumptions that fit the usual types of perturbations. We study if the limit of SPNEs of the perturbed games is an SPNE of the original game and if the limit of SPNE-outcomes of perturbed games is an SPNE-outcome of the original game. We fully positively answer when the follower's best reply correspondence is single-valued. When the follower's best reply correspondence is not single-valued, the answer is positive only for the SPNE-outcomes; whereas the answer for SPNEs may be negative, even if the perturbation does not affect the sets and affects only one payoff function. However, we show that under suitable non-restrictive assumptions it is possible to obtain an SPNE starting from the limit of SPNEs of perturbed games, possibly modifying the limit at just one point. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a notion of mergeable weighted majority games with the aim of providing the first characterization of the Colomer–Martínez power index (Colomer and Martínez in J Theor Polit 7(1):41–63, 1995). Furthermore, we define and characterize a new power index for the family of weighted majority games that combines ideas of the Public Good (Holler in Polit Stud 30(2):262–271, 1982) and Colomer–Martínez power indices. Finally, we analyze the National Assembly of Ecuador using these and some other well-known power indices. [ABSTRACT FROM AUTHOR]

To achieve a competitive advantage, corporations are growingly adopting strategies to effectively promote their market demand. Trade credit payment and pricing strategies provided by corporates can efficiently influence customers' purchasing behavior. Although granting a trade credit strategy can increase corporations' market share, such a strategy is a risk-based financing program for corporations. Therefore, corporates should choose whether to use trade credit financing in their long-term. This paper proposes an analytical model to investigate the evolutionary behaviors of retailers regarding pricing and trade credit strategies in the long term. In the study under investigation, retailers can use two financing strategies: risk-based trade credit and non-trade credit (i.e., pricing). This study provides both numerical and analytical findings. Our findings demonstrate that the risk-based trade credit strategy is the stationary financing solution for retailers in the long term. The result indicates that when customers are financially constrained, providing a trade credit scheme to customers is a successful marketing policy in both short-term and long-term frameworks. [ABSTRACT FROM AUTHOR]

This paper studies a simple process of demand adjustment in cooperative games. In the process, a randomly chosen player makes the highest possible demand subject to the demands of other coalition members being satisfied. This process converges to the aspiration set; in convex games, this implies convergence to the core. We further introduce perturbations into the process, where players sometimes make a higher demand than feasible. These perturbations make the set of separating aspirations, i.e., demand vectors in which no player is indispensable in order for other players to achieve their demands, the one most resistant to mutations. We fully analyze this process for 3-player games. We further look at weighted majority games with two types of players. In these games, if the coalition of all small players is winning, the process converges to the unique separating aspiration; otherwise, there are many separating aspirations and the process reaches a neighbourhood of a separating aspiration. [ABSTRACT FROM AUTHOR]

This paper introduces the new class of two-bound core games, where the core can be described by a lower bound and an upper bound on the payoffs of the players. Many classes of games turn out to be two-bound core games. We show that the core of each two-bound core game can be described equivalently by the pair of exact core bounds, and study to what extent the exact core bounds can be stretched while retaining the core description. We provide explicit expressions of the nucleolus for two-bound core games in terms of all pairs of bounds describing the core, using the Talmud rule for bankruptcy problems, and study to what extent these expressions are robust against game changes. [ABSTRACT FROM AUTHOR]

In this paper, we examine how mutual fund managers behave to fluctuations in market volatility. We use a sample of daily return from countries around the world to evaluate how manager perform to publicly available information. There is a lack of empirical studies that examine the relation between conditional market returns and conditional volatility on a global scale; we provide evidence across countries to answer this question. Our study provides new evidence about conditional mutual fund performance across countries. We find that during periods of high market volatility mutual funds reduce market exposure across all countries; this implies that systemic risk is particularly sensitive to changes in market volatility around the world. [ABSTRACT FROM AUTHOR]

Previous literature shows that prevalent risk measures such as value at risk or expected shortfall are ineffective to curb excessive risk-taking by a tail-risk-seeking trader with S-shaped utility function in the context of portfolio optimisation. However, these conclusions hold only when the constraints are static in the sense that the risk measure is just applied to the terminal portfolio value. In this paper, we consider a portfolio optimisation problem featuring S-shaped utility and a dynamic risk constraint which is imposed throughout the entire trading horizon. Provided that the risk control policy is sufficiently strict relative to the Sharpe ratio of the asset, the trader's portfolio strategies and the resulting maximal expected utility can be effectively constrained by a dynamic risk measure. Finally, we argue that dynamic risk constraints might still be ineffective if the trader has access to a derivatives market. [ABSTRACT FROM AUTHOR]

To profit from price oscillations, investors frequently use threshold-type strategies where changes in the portfolio position are triggered by some indicators reaching prescribed levels. In this paper we investigate threshold-type strategies in the context of ergodic control. We make the first steps towards their optimization by proving ergodic properties of related functionals. Assuming Markovian price increments satisfying a minorization condition and (one-sided) boundedness we show, in particular, that for given thresholds, the distribution of the gains converges in the long run. We also extend recent results on the stability of overshoots of random walks from the i.i.d. increment case to Markovian increments, under suitable conditions. [ABSTRACT FROM AUTHOR]