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Probabilistic linguistic term set (PLTS), which consists of multiple linguistic terms and their probabilities, has been proposed to tackle qualitative information in informs of linguistic expressions. Since experts are inclined to compare alternatives in pairs, the probabilistic linguistic preference relation (PLPR), a matrix whose elements are PLTSs, has attracted wide attention since it was first introduced in 2016. Fruitful research achievements regarding PLPRs have been generated, especially in group decision making (GDM). This paper reviews 88 selected articles published from 2016 to June 4, 2023 regarding PLPRs, and presents a review of researches on theory and applications of PLPRs. First, we conduct a bibliometric study of these selected articles in terms of publication and citation trends, most productive countries/regions, categories of publications and keyword co-occurrence relationships. Next, the theory of PLPRs including definition, missing element deduction, consistency checking, prioritization, and decision-making analysis methods are recalled. We also review real-world applications of these publications and find that PLPRs have been mainly applied in four areas, including healthcare management, project evaluation, environment and energy management, and emergency management. In the end, we propose future research directions related to the probabilistic linguistic decision-making theory and provide insights for researchers and practitioners who have an interest in complex linguistic decision making. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

Fuzzy sets, which have a crucial role in the decision making theory, model uncertainty by means of membership and non-membership functions. q-rung orthopair fuzzy sets, which are the natural extension of fuzzy, intuitionistic fuzzy and Pythagorean fuzzy sets, are quite successful in modeling data thanks to their larger domains. However, in a q-rung orthopair fuzzy set the membership and non-membership degrees of an element to a set are given just by a pair of certain numbers from the closed interval [0, 1] that causes a strict modelling. Various types of interval valued fuzzy sets, multi fuzzy sets or circular fuzzy sets change these strict modelling with a sensitive one. In this paper, we introduce a new fuzzy set notion via continuous functions that take values on a closed interval to provide a more sensitive tool in decision making theory. In this new fuzzy set notion, the membership and non-membership degrees of an element to a fuzzy set are represented by continuous functions instead of numbers. Actually, we study not only with points, but also with functions by taking into account the sufficiently large and continuous neighborhoods of the points. Thus more sensitive and realistic models are made by relieving the precision of the fuzzy data or linguistic argument. The data carried to function space environment is processed with the function theoretic tools via aggregation functions, distance measures or score functions. This fact distinguishes the new fuzzy set notion from the other continuous extensions in the literature such as interval valued or circular structures. Moreover, we provide an extended Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in this new fuzzy environment and apply it to a multi criteria group decision making problem from the literature. Finally, we provide a comparison analysis and a complexity analysis. We also visualise the time complexity of the proposed extended TOPSIS for different numbers of decision makers. [ABSTRACT FROM AUTHOR]

Three-way decision models have received substantial interest grounded in decision-theoretic rough sets and Bayesian decision theory. Single-valued neutrosophic sets are extremely useful for handling uncertain and inconsistent information, making them a valuable tool that is commonly applied in decision-making. In a three-way decision problem involving a piece of single-value neutrosophic information, the losses of each equivalence class under different actions can usually be identified with some accuracy. A critical aspect of the three-way decision problem centers around appropriate handling of the loss function. This paper proposes a novel approach to rank loss functions in each equivalence class of three-way decisions, based on the TODIM method and operates within a singlevalued neutrosophic environment. Furthermore, a numerical experiment on the location of a breakfast restaurant is used to to assess the model compared to some existing related models, with the aim of demonstrating its validity and soundness. [ABSTRACT FROM AUTHOR]

Guidance in visual analytics aims to support users in accomplishing their analytical goals and generating insights. Different approaches for guidance are widely adopted in many tools and frameworks for various purposes – from helping to focus on relevant data subspaces to selecting suitable visualization techniques. With each of these different purposes come specific considerations on how to provide the needed guidance. In this paper, we propose a generic method for making these considerations by framing the guidance problem as a decision problem and applying decision making theory and models toward its solution. This method passes through three stages: (1) identifying decision points; (2) deriving and evaluating alternatives; (3) visualizing the resulting alternatives to support users in comparing them and making their choice. Our method is realized as a set of practical worksheets and illustrated by applying it to a use case of providing guidance among different clustering methods. Finally, we compare our method with existing guidance frameworks to relate and delineate the respective goals and contributions of each. [ABSTRACT FROM AUTHOR]

The paper aims to question the required decision theory concept, structure, and mechanism under the current challenges of optimality model of rationality crepuscule, sustainability model of rationality emergence, and evolutionism extending from the biological species to the symbolic ones (as decision theory is). To this end, the three symbolic species are logically described based on their sufficiency predicates, then the three sets of sufficiency predicates are put together in order to find the compatibility among them, as either binomial or trinomial compatibility. So, three complete compatibilities are found based on which some concluding remarks are provided. [ABSTRACT FROM AUTHOR]

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. [ABSTRACT FROM AUTHOR]

An Regional Market Manager (RMM) is supposed to take into account a variety of items including the participants in the market, technical constraint, price variation/reaction, electricity-price uncertainty, and types of the applied demand response program, to name a few. One of the demand response programs is Emergency Demand Response Program (EDRP) which is employed in this paper. In the present study, the objective function of the RMM is formulated in a market environment in order to determine the optimal demand, incentive, and power purchased with considering some of technical constraints such as incentive limits, demand limits, power purchased, and power balance. Co-evolutionary Improved Teaching Learning-Based Optimization (C-ITLBO) is applied to maximize the RMM's profit. In addition, the demand level in the EDRP is determined based on a logarithmic model that includes Price Elasticity Matrix (PEM). The reserve supplied due to Aggregators (AGGs) is also prioritized using Reserve Margin Factor (RMF). Further, Information-Gap Decision Theory (IGDT) is applied to model uncertainty in the initial electricity price. The above-mentioned items are modeled in a multi-level formulation. [ABSTRACT FROM AUTHOR]

Group decision-making is one of the most important topics in decision-making theory. In today's complex world, when there are a large number of conflicting criteria, it is difficult for a decision maker (DM) to make decisions considering all aspects of a problem. The quality of the decision is determined by the level of knowledge and experience of the DM. In order to incorporate as much experience and knowledge as possible, it was passed from the decision made by one DM to a decision made by a group of DMs. This passage has increased the complexity of Multi-Criteria Decision-Making (MCDM) methods that use groups of DMs. In the present paper a Hybrid Group Weighting Method (HGWM) based on the Borda Method and the Group Best Worst Method (GBWM) for determining weights of a set of criteria is proposed. For the choice of the best and the weakest criteria, by a group of experts/DMs, Borda Method is applied. These criteria (best and worst) are used in GBWM, by every expert from the group, to calculate the individual criteria weights. The final criteria weights are computed as a combination of the individual criteria weights. An application of the HGWM is made for obtaining the weights of indicators from a set of Digital Development Indicators (DDIs). The proposed hybrid method may provide support for group decision making in multi-criteria problems. [ABSTRACT FROM AUTHOR]

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The use of ICT to support activities in the policymaking process is on the increase. At the inception of the COVID-19 pandemic, Government agencies around the world relied on ICTs to either remotely support and/or enable policy-making activities. Policy-making activities occur via collaborative processes between interested parties by means of dialogue. Some extant ICTs utilized by government agencies support and enable collaboration and dialogue. However, the decision on what ICT to adopt is not always easy as a result of the failure of some ICTs to support the task they were designed for. As a result due diligence is needed by public service administrators to decide on which ICT to adopt. This implies a decision process required to decide if the public agency will mobilize resources to acquire and implement the ICT. But as most government agencies around the world have adopted ICT to support dialogue and collaborative activities in their policy making decision. This paper provides the result of a study where the mobilization-decision theory was used to analyse and explain reasons why government agencies around the world, aside the pressure from COVID-19, made the decision to mobilize resources to acquire, implement and utilize ICTs for policy dialogue and collaboration. [ABSTRACT FROM AUTHOR]

In this paper, we discuss aggregate measures of marginal costs of public funds (MCF) in populations that are heterogeneous with respect to observed as well as unobserved characteristics. We first discuss how to compute MCF in selected examples of traditional (textbook) labour supply models. Next, we review two types of discrete labour supply models proposed in the literature. Subsequently, we discuss how to calculate aggregate measures of MCF for discrete labour supply models. Finally, we apply an estimated two-sector discrete labour supply model to compute MCF based on Norwegian data. [ABSTRACT FROM AUTHOR]

Copyright of Soziale Welt is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article studies the use of asymmetric loss functions for the optimal prediction of positive-valued spatial processes. We focus on the family of power-divergence loss functions with properties such as continuity, convexity, connections to well known divergence measures, and the ability to control the asymmetry and behaviour of the loss function via a power parameter. The properties of power-divergence loss functions, optimal power-divergence (OPD) spatial predictors, and related measures of uncertainty quantification are studied. In addition, we examine in general the notion of asymmetry in loss functions defined for positive-valued spatial processes and define an asymmetry measure, which we apply to the family of power-divergence loss functions and other common loss functions. The paper concludes with a simulation study comparing the optimal power-divergence predictor to predictors derived from other common loss functions. Finally, we illustrate OPD spatial prediction on a dataset of zinc measurements in the soil of a floodplain of the Meuse River, Netherlands. • Spatial prediction is examined through a decision-theoretic lens. • The automatic use of squared-error loss for spatial prediction is questioned. • The family of power-divergence loss functions is examined as an alternative. • Optimal power-divergence prediction and uncertainty quantification are studied. • A measure of asymmetry in loss functions for positive-valued processes is proposed. [ABSTRACT FROM AUTHOR]

In multi-label classification, the expansion of output dimension seriously interferes learning performance, and even fails to build a joint prediction model. In order to restrain the proliferation of multi-label classifier's hypothesis space, the current works focus on the application of global positive label correlation. However, the "black or white" mechanism ignore other possible forms of label correlation, such as negative or neutral correlation. By introducing the doctrine of the mean, three-way decision (3WD) theory provides a solution for in-depth research on local label correlation, and aims to handle the uncertainty of multi-label learning tasks. In this paper, a novel learning algorithm for multi-label joint classification, namely ML-3WD, is proposed by considering the 3WD label correlation from the perspective of samples. According to the weights of different features on any label, the comprehensive loss of each sample to three action strategies can be measured. Obviously, the 3WD rules for any label variable in multi-label output space is obtained. By aggregating the cutting thresholds between different labels, the division principles of 3WD label correlation are further established. Given any multi-label sample, the local fuzzy membership to co-occurrence or mutual state for label pair is examined based on kernelized fuzzy rough sets. The 3WD local label relevance of each sample is confirmed, that is, positive, negative or neutral. The global application strategy for multi-label classification is utilized to avoid over-fitting induced by local mining strategy. Based on the integral mean of the distribution of 3WD local label relevance in multi-label sample space, two different versions of empirical label relevance are constructed. By constraining the relative position between sub-separation hyperplanes, the 3WD label correlation distribution-based model for multi-label joint classification is designed. The experiment results on fifteen real world multi-label datasets reflect that our algorithm achieves good classification ability and versatility. The impact of core parameters on learning performance is also dissected. • The doctrine of the mean in three-way decision theory, which accords with human behavior cognition, inspires us to enrich the "black or white" mining mechanism on label correlation. By adding buffer processing to the determined correlation, three possible forms for label correlation are first considered, they are positive, negative or neutral. A multi-label classification algorithm is designed according to 3WD label correlation from the perspective of samples, where global empirical label relevance is explicitly applied to restrict the sub-separation hyperplanes of different labels. [ABSTRACT FROM AUTHOR]

• Proposing optimal management of a local operator under the concept of P2P trading. • Assessing the performance of the local operator in different uncertainty attitudes. • Several sources of uncertainty considered such as load, price, and PV generation. • Risk-averse decision-maker tends to purchase from the grid to reduce potential risks. • Risk-seeking decision-maker targeting demand supply through the P2P trading scheme. Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed. [ABSTRACT FROM AUTHOR]

• Building a one-step rolling forecasting model by using Markov chain. • Selecting the data from an area of China to address charging uncertainty. • Applying demand response for charging optimization with time division for TOU-price. • Conducting four-objective optimization with generation uncertainty modeled via IGDT. • Proposing a joint algorithm based on the combination of ε-constraint and NSGA-II. This paper constructs an electric vehicle (EV) charging optimization model considering demand response and the uncertainties of source and load. First, after obtaining the charging load by using the Markov chain, the charging time is divided into peaks, flats, and valleys, and the charging load is adjusted through price-based demand response. Secondly, aiming at minimizing user charging cost, greenhouse gas (GHG) emissions, and load fluctuation, and maximizing the revenue of power supplier, a deterministic EV charging optimization model is established. Third, by modeling information gap decision theory (IGDT), the uncertainty of wind and photovoltaic powers (WP and PV) is introduced into the charging optimization model to analyze the impact of WP and PV's fluctuations on risk aversion decision makers. Finally, a joint algorithm for solving a many-objective problem based on ε-constraint and NSGA-II algorithm is proposed. The results of case study show that: (1) rolling forecast based on Markov chain has a better effect on dealing with the uncertainty of charging load than Monte Carlo; (2) the price-based demand response combined with time division model can be used to shave peaks and fill valleys and reduce user charging cost; (3) optimized by using the deterministic model, the charging load curve is smoother; (4) with the uncertainty of power generation involved in the many-objective optimization, the PV's fluctuation is greater, and the power supplier revenue increases compared with the deterministic model, but the GHG emissions also increase; (5) In the same range of expected benefit changes, the fluctuation values acceptable to decision makers are different for different objective functions; (6) the user response, the normalized number of the selected sub-objective, and the maximum acceptable benefit deviation have different influences on the ordered charging strategy; (7) the joint algorithm proposed in this paper is an effective method to solve many-objective problems that contain conflictive aims. [ABSTRACT FROM AUTHOR]

• Modeling a multi-energy retailer in the framework of multi-carrier energy systems. • Developing a multi-energy retailer based on gas/cooling/heating/power carriers. • Presenting an IGDT method to cope with uncertainties in the optimization model. • Integrating flexible options, e.g. hybrid energy storage in the optimization model. • Proposing integrated DR to control multiple energy demands simultaneously. Multi-energy systems (MESs) were introduced to enhance the flexibility and efficiency of conventional energy distribution systems. In this new trend, it is possible to supply different energy carriers simultaneously by the multi-energy retailer (MER), under which, without the need to transfer all of them to different locations, the loads are fed centrally, and the customers can purchase all the carriers they need from the desired MER. Motivated by these descriptions, this paper focuses on the optimal scheduling of MER in the integrated energy system, including natural gas, electricity, cooling, and heating, under the information gap decision theory (IGDT) framework. The MER's goal is to maximize profits by considering the uncertainty of the day-ahead power market. The proposed IGDT approach without a probabilistic distribution function analyzes the risk-aversion strategy associated with electricity price uncertainty. In this paper, integrated demand response (IDR) for natural gas, electrical, heating, and cooling loads are developed simultaneously as a flexible demand-side management resource. The proposed model contains multiple technologies owned by MER, including hybrid energy storage technology, combined heat and power, chiller, boiler units, and renewable resources. Numerical results from different cases illustrate the effectiveness of IDR installation in terms of profitability. Besides, the MER can serve multiple demands more efficiently. Under the IGDT approach, the purchasing power cost is reduced up to 24.23%. [ABSTRACT FROM AUTHOR]

Road target detection is a very challenging task in the field of computer vision because it is easily affected by complex backgrounds and sparse features of small targets. YOLOv3 (You Only Look Once v3) is currently one of the state-of-the-art object detection methods of deep learning. However, because the k-means clustering algorithm is sensitive to the initial clustering center, the local fragile visual field features related to small objects in the prediction map are severely lost and the final decision-making theory (The grid located in the center of the foreground object is responsible for predicting this object) of the network ignores the detailed information of the neighboring grid, there are still many problems in object detection. In this paper, we propose an improved algorithm based on YOLOv3 for small-scale object detection. We use the improved k-medians clustering method instead of the previous k-means to improve the model instability caused by the singularity; We propose a local enhancement method to strengthen weak features for small-scale object detection by paralleling a branch on the backbone. Besides, a flexible offset sampling structure added in parallel for information compensation is also designed. A series of experiments showing that our system has achieved good detection results on the KITTI and UA-DETRAC public datasets, and the distinguishing performance for small-scale objects is significantly improved. Therefore, our method is effective in road target detection tasks. • The paper focuses on the long-distance detection problem with rare features. • The clustering algorithm effectively reduces the sensitivity to shields noise. • A local feature enhancement algorithm that focuses on fragile vision is proposed. • The strategy of offset sampling combines more comprehensive target features. • The aid of weights for final decision-making, especially for small targets. [ABSTRACT FROM AUTHOR]

Decision-making is a systematic process of solving real-world problems that provide an optimal solution after checking the achievable set of alternatives. As decision-making theory plays an increasingly important role in real life, there is more and more research on decision-making. All possible options are sorted through a multi-criteria decision-making system, allowing users to choose the suitable solution. In the face of many alternatives, the lack of appropriate skills and limited time has led to eventful human judgments. In this framework, the paper introduces the definition of a Temporal Complex neutrosophic set (TCNS) and the operations and properties of TCNS. Then, the TCNS-ANP-TOPSIS model based on TCNS is presented, which extends the ANP model to determine the weights for group multi-criteria and TOPSIS to rank alternatives collected from different time intervals. Further, to prove the feasibility and potentiality of the proposed model, a case study of choosing a tourist destination in Vietnam is described under the TCNS context. A comparative analysis with existing algorithms demonstrates the proposed model's effectiveness, consistency, and availability. • Propose the definition of a Temporal Complex Neutrosophic Set as well as its operations and properties. • Propose a hybrid TCNS-ANP-TOPSIS decision-making process based on TCNS. • Apply The TCNS-ANP-TOPSIS model to choosing a tourist destination in Vietnam. [ABSTRACT FROM AUTHOR]

Spatial planning defines objectives for spatial ordering of a region, together with instruments required to realize them. However, since the future is uncertain, many factors involved in spatial planning are unknown in advance. Scenario-based forecasting is a common way to deal with this fundamental uncertainty. This prospective approach offers guidance to decision makers regarding problems that are likely to appear in the future, and possible ways to manage them in advance. The performance of the forecasting can be assessed in retrospect once the future arrives. However, a method for assessing past management of uncertainty is lacking. This is important because learning from past performance under uncertainty can provide useful insights for the future. These insights can help to design future scenario-based forecasts that are more accurate, and more robust to uncertainty. This paper develops a methodology to combine retrospective analyses focused on past performance with prospective scenario-based forecasting. We use info-gap decision theory to model and manage uncertainty in scenario-based forecasting assessing efforts to contain residential sprawl in the Netherlands. The suggested approach informs prospective scenario-based forecasting, learning from previous experiences regarding their performance and their management of uncertainty and robustness. • Scenario-based forecasting planning is usually prospective. • Learning from past performance under uncertainty provide useful insights for the future. • We model uncertainty in residential sprawl in the Netherlands using info-gap theory. • We combine retrospective with prospective scenario-based assessment of uncertainty. • The approach informs prospective scenario-based forecasting learning from the past. [ABSTRACT FROM AUTHOR]

In this paper we review how the uncertainty in spatial information has been characterized. This includes both continuous predictions of spatial variables, and thematic maps of landcover classes. We contend that much work in this area has failed to engage adequately with the decision processes of the end-user of information, and that the engagement of spatial statisticians is essential to achieve this. We examine generalized measures of uncertainty, and those focussed on particular decision models. We conclude that the latter are likely to be the most fruitful, particularly if they emerge from a formal decision analysis. We outline the principles of value of information theory, and suggest that this represents an ideal framework in which to develop measures of uncertainty which can support both the rational collection of data and the interpretation of the resulting information. [ABSTRACT FROM AUTHOR]

Application of combined heat and power (CHP) units beside the distributed energy resources (DERs) persuades the power systems via the formation of multi-carrier microgrids (MGs). This paper presents the day-ahead scheduling of multi-carrier MGs. Renewable distributed generators are known as undeniable parts of modern power systems, which may increase the chanciness of the system. Information gap decision theory (IGDT) is applied to address the uncertainties of renewable sources. Moreover, a scenario-based stochastic approach is used to model the uncertainty of electricity prices in this method. Indeed a hybrid stochastic-IGDT-based optimization problem is proposed for optimal energy management of multi-carrier MGs. According to this method, the operation of multi-carrier MGs would be robust against uncertainties and contain a minimum profit. The proposed problem has been formulated as mixed-integer linear programming (MILP). Finally, the proposed energy management has been implemented into a sample multi-carrier microgrid. The results showed that how the MG operator's risk-averse character can change her/his decision-making. • Robust Optimal Scheduling of CHP-based Microgrids. • Proposing an IGDT-based approach to handle uncertainties of renewable generations. • Proposing a single level model for the IGDT method to obtain a global optimum. • Considering demand response programs for both thermal and electrical loads. [ABSTRACT FROM AUTHOR]

There is little room for doubt that distributed generation systems including renewable energy, microgrids (MGs), combined heat and power (CHP) units and storage systems have been of particular importance in sustaining low-carbon and cost-effective operations due to the tremendous increase in greenhouse gas emissions in recent years. Additionally, hydrogen-based power technologies have earned a great deal of publicity that hydrogen can serve as a zero-emission fuel for electrical power and thermal energy production. In this regard, the current paper proposes an optimal energy management strategy for a combined hydrogen, heat, and power MG (CHHP-MG) with hydrogen fueling stations (HFSs) for hydrogen vehicles (HVs), electric vehicle parking lots (EVPLs) and fuel cell micro-CHP (FC-MCHP) units to meet power and heat requirements. In order to reduce the regular operating expense, the presented CHHP-MG could also communicate with both electricity and hydrogen markets. In addition, to compensate for the associated heat and hydrogen requirements, power-to-X technologies such as the power to heat (P2HT) and power to hydrogen (P2H) are integrated. In order to improve flexibility and build a low carbon MG, multi-energy storage (MES) system along with heat and power demand response (HPDR) programs will be taken into consideration. As the uncertainties associated with the predicted wind and photovoltaic power have a major impact on the energy management of the CHHP-MG, a multi-objective information gap decision theory (IGDT)-based robust approach is applied as an effective non-probabilistic modeling technique for handling such uncertainties. The empirical results show that the proposed model can efficiently handle the uncertainties and reduce the overall operation cost by 76.35%. • A novel low-carbon CHHP-MG is presented by considering FC-MCHP units, HFs and EVPLs. • The P2H, H2P and P2HT technologies are incorporated to minimize the operation cost. • A multi-objective IGDT is proposed to model wind and PV power uncertainty simultaneously. • The augmented ε-constraint approach is utilized to solve the multi-objective problem. [ABSTRACT FROM AUTHOR]