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The structure of uncertainty underlying certain decision problems may be so complex as to elude decision-makers’ full understanding, curtailing their willingness to pay for payoff-relevant information — a puzzle manifesting itself in, for instance, low stock-market participation rates. I present a decision-theoretic method that enables an analyst to identify decision-makers’ information-processing abilities from observing their preferences for information. A decision-maker who is capable of understanding only those events that either almost always or almost never happen fails to attach instrumental value to any information source. On the other hand, non-trivial preferences for information allow perfect identification of the decision-maker’s mental capacity.

When faced with important decisions, we tend to base our decision-making on a rational framework, which often includes multiple decision criteria. Spatial decision problems have been characterized as a set of geographically defined decision alternatives (locations) with associated criterion values (e.g., Malczewski 1999). Within Geographic Information Systems (GIS), multi-criteria decision analyses (MCDA) tools have been used for decision support in environmental, transportation, and urban/regional planning, in waste management, as well as in hydrology, agriculture, and forestry, to name but a few areas of application (Malczewski 2006). Often, MCDA tools are only loosely coupled with GIS software (e.g., calculations completed in a spreadsheet) or take the form of custom implementations in a GIS scripting/programming environment. Few GIS vendors have integrated generic MCDA functionality in their products, with the notable exceptions of Idrisi’s Multi-Criteria Evaluation module and ArcGIS’ Overlay Toolset. Both of these operate on raster data layers using map algebra operations to combine cell values into an evaluation score for each candidate location (raster cell). In this technical note, we present “MCDA4ArcMap”, an open-source tool for MCDA and geovisualization of vector data in Arc-Map. The analytical functionality of the tool includes three MCDA methods: weighted linear combination (WLC), ordered weighted averaging (OWA), and a local variant of WLC (LWLC). WLC corre-sponds to the weighted overlay tool that readers may know from ArcGIS. As an extension of the criterion importance weighting in WLC, the OWA method allows the decision-maker to specify a de-gree of risk in their approach to decision-making. OWA has been implemented previously in Idrisi (Jiang & Eastman 2000). The recently proposed LWLC (Malczewski 2011) adjusts criterion impor-tance weights with regards to the local range of criterion values. Criterion weights are increased in a neighbourhood, if their local range is large relative to their global range in the study area, or decreased if the local range is relatively small. This approach ad-heres to the range-sensitivity principle that stipulates that criterion weights should depend on the ranges of criterion values occurring in a specific decision problem.

This paper proposes an energy management (EM) model for residences in an inclining block rate (IBR) tariff environment. Given a cluster, i.e., a group of appliances which satisfies certain requirements, the proposed scheme is primarily driven by two layers of clustered priorities. The first layer of clusters comprises various services of the residences and governs it with shareable z1 priorities, and the second layer of clusters binds the major power-consuming appliances under each substation and regulates it with dynamically changing z2 priorities. Integer linear programming is used to solve the multi-criteria decision problem crafted to address both grid-connected and isolated modes of operations. The model is tested on MATLAB using the IEEE 123-bus system with modified Pecan Street Dataset. Various results reveal that the proposed model is effective in providing a reliable power supply to the critical loads in all the scenarios while keeping the price within the preference limit with minimum comfort-loss for end-users, even in presence of non-responsive IBR environment

Machine Learning has emerged in the last years as the main solution to many of nowadays’ data-based decision problems. However, while new and more powerful algorithms and the increasing availability of computational resources contributed to a widespread use of Machine Learning, significant challenges still remain. Two of the most significant nowadays are the need to explain a model’s predictions, and the significant costs of training and re-training models, especially with large datasets or in streaming scenarios. In this paper we address both issues by proposing an approach we deem predictive and user-centric. It is predictive in the sense that it estimates the benefit of re-training a model with new data, and it is user-centric in the sense that it implements an explainable interface that produces interpretable explanations that accompany predictions. The former allows to reduce necessary resources (e.g. time, costs) spent on retraining models when no improvements are expected, while the latter allows for human users to have additional information to support decision-making. We validate the proposed approach with a group of public datasets and present a real application scenario.

Nowadays, a large number of intelligent devices involved in the Industrial Internet of Things (IIoT) environment lead to unprecedented challenges in security. Due to limited resources with weak security protection, the IIoT devices can be easily compromised to launch Distributed Denial-of-Service (DDoS) attacks, resulting in catastrophic results. Although there are many DDoS mitigations of traditional static schemes, the proactive defense method to resist attacks has not been well studied. Furthermore, existing proactive schemes ignored the delay-sensitive characteristic of applications under the IIoT environments. To address these issues, we first adopt two kinds of Moving Target Defense (MTD) techniques that dynamically control the admission of devices and migrate service replicas to isolate attackers on limited edge clouds and mitigate DDoS attacks early near its source. Then, we formulate a multi-stage optimization problem of MTD mechanisms deployment and model it as Constrained Markov Decision Processes (CMDP) in order to maximize the available resources of the system under the limitations of the IIoT environments. Besides, we present an MTD Optimal Strategy (MOS) algorithm to solve decision problems in a cost-effective manner. The proposed algorithm can achieve an optimal admission allocation by means of attackers gathering within the same service where the service migration decisions are assisted by means of value iteration. The experimental results verify that the proposed algorithm, compared with existing strategies, can effectively mitigate DDoS attacks with acceptable degradation of the quality of service (QoS).

Cloud-edge heterogeneous network is an emerging technique built on edge infrastructure, which is based on the core of cloud computing technology and edge computing capabilities. The joint problem of computation offloading, cache decision, and resource allocation for cloud-edge heterogeneous network system is a challenging issue. In this paper, we investigate the joint problem of computation offloading, cache decision, transmission power allocation, and CPU frequency allocation for cloud-edge heterogeneous network system with multiple independent tasks. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the transmission power and CPU frequency constraint of the tasks. The constraint of computing resource and cache capacity of each Access Point (AP) are considered as well. The formulated problem is a mixed integer non-linear optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Reinforcement Learning (RL) and Sequential Quadratic Programming (SQP). In the upper level, given the allocated transmission power and CPU frequency, the task offloading decision and cache decision problem is solved using Deep Q-Network method. In the lower level, the optimal transmission power and CPU frequency allocation with the offloading decision and cache decision is obtained by using SQP technique. Simulation results demonstrate that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

The Nash equilibrium is an important concept in game theory. It describes the least exploitability of one player from any opponents. We combine game theory, dynamic programming, and recent deep reinforcement learning (DRL) techniques to online learn the Nash equilibrium policy for two-player zero-sum Markov games (TZMGs). The problem is first formulated as a Bellman minimax equation, and generalized policy iteration (GPI) provides a double-loop iterative way to find the equilibrium. Then, neural networks are introduced to approximate Q functions for large-scale problems. An online minimax Q network learning algorithm is proposed to train the network with observations. Experience replay, dueling network, and double Q-learning are applied to improve the learning process. The contributions are twofold: 1) DRL techniques are combined with GPI to find the TZMG Nash equilibrium for the first time and 2) the convergence of the online learning algorithm with a lookup table and experience replay is proven, whose proof is not only useful for TZMGs but also instructive for single-agent Markov decision problems. Experiments on different examples validate the effectiveness of the proposed algorithm on TZMG problems.

Buildings are exposed to risks from environmental hazards such as earthquakes, windstorms and floods. Substantial uncertainties from various sources are inevitably involved in the risk estimation and decision-making for activities such as design and disaster risk mitigation for buildings. Decision makers seek to achieve economic efficiency while ensure building safety by managing the extreme tail risk that is typically a concern when facing low-probability, high-consequence events. Thus, risk preferences and tolerances play an important role in the decision process, which often vary among different decision makers. The conventionally used minimum expected life-cycle cost criterion (MELC) fails to adequately cope with large uncertainty and risk preferences. To this end, this paper presents the application of a set of decision models beyond the MELC to support decision-making under uncertainty for buildings exposed to environmental hazards. The objective is to provide risk-informed decision support for decision-makers with a wide range of risk appetites while taking into account uncertainties involved in the life-cycle cost. The features, strengths and weaknesses of these decision models are discussed from a practical point of view. The application and selection of the decision models are demonstrated by two practical decision problems: (i) seismic design of a high-rise commercial building, and (ii) wind hazard mitigation for a low-rise residential building. These examples illustrate how the decisions for choosing seismic design levels and wind mitigation measures vary when different decision models and model settings are applied.

We study a general class of dynamic multi-agent decision problems with asymmetric information and non-strategic agents, which includes dynamic teams as a special case. When agents are non-strategic, an agent's strategy is known to the other agents. Nevertheless, the agents strategy choices and beliefs are interdependent over times, a phenomenon known as signaling. We introduce the notion of sufficient information that effectively compresses the agents information in a mutually consistent manner. Accordingly, we propose an information state for each agent that is sufficient for decision making purposes. We present instances where we can determine an information state with a time-invariant domain for each agent. We present a generalization of the policy-independence property of belief in Partially Observed Markov Decision Processes to dynamic multi-agent decision problems. Furthermore, we propose a sequential decomposition that decouples the interdependence between the agents strategies and beliefs over time, and enables us to formulate a dynamic program to determine a globally optimal policy in dynamic teams via backward induction.

Energy harvesting (EH) enabled cellular internet of things (IoT) is a promising solution to handle the charging and accessing of massive IoT nodes. However, limited by the high frequency band of future 5G, the radius of the small base station (SBS) is reduced, hence greatly increasing the cost of the network operators (NOs). In this paper, we consider the joint cell association, cell sleeping, and incentive decision problem for EH aided cellular IoT with full duplex (FD) self-backhauls. We formulate a Stackelberg game to investigate the coordination between the utilities of NO and energy transmitters (ETs), where both the features of FD self-backhauls and cell sleeping are introduced to reduce the expense of NO. We then propose an alternative direction algorithm to solve the equilibrium of the game efficiently, where the relationship of the formulated constraints and variables are utilized to transform the original problem into two sub-problems. We propose a two-level Lagrangian relaxation to solve the first sub-problem, while the other is proved to be convex and solve by an efficient iteration. Simulation results demonstrate the benefits of our algorithm in utility improvement and expense reduction. Moveover, it shows that our algorithm can obtain high efficiency by adjusting the tradeoff between the number of active SBS and transmitting power of ETs according to the network deployment.

In this paper, we study a multi-agent project staffing problem involving a single project, which has to be scheduled under resource constraints. We consider a functional organisational structure where a team leader and a project manager are together responsible for the operational execution of a project. The team leader, which has the formal authority over the resources, is standing at the top of the hierarchy and determines the number and mix of (additional) employees and tries to level the workload over the planning period in order to avoid idle resource times. The project manager is responsible for the scheduling of the project activities and his/her objective is to minimise the project duration. The interaction between both agents in the decision-making process is, on the one hand, hierarchical, i.e. the team leader imposes his/her decision on the project manager. On the other hand, the decision taken by the team leader should comply to the objective of the project manager such that the staffing plan and project schedule is agreed by both parties. We propose a bilevel optimisation model that embeds a nested inner optimisation problem, i.e. the project manager decision problem, as a constraint in the outer optimisation problem, i.e. the decision problem of the team leader. The algorithm is a mathematical programming method thriving on the generation of additional lazy constraints via feasibility callbacks so that the team leader problem has to respect the requirements formulated in the project manager problem. In the computational experiments, we compare this solution approach to alternative classical optimisation approaches and we validate the design choices related to the proposed speed-up mechanisms and parameter settings.

Three-way decision provides a new perspective for dealing with uncertainty and complexity in decision-making problems. However, behaviors of decision-makers may be influenced by different risk attitudes in reality. To address this problem, we construct a regret-based three-way decision model under interval type-2 fuzzy environment. Basically, regret theory and interval type-2 fuzzy set are utilized to improve three-way decision in coping with the risk and uncertainty. Two core issues focus on the determination of decision rules and estimation of conditional probabilities for different decision-makers under interval type-2 fuzzy environment. The maximum-utility decision rules are derived based on regret theory. An interval type-2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is utilized to estimate the conditional probability. The results of the illustrative example show that the proposed model can effectively solve uncertain decision problems. The comparative analysis and experimental evaluations are utilized to elaborate on the performance of the regret-based three-way decision model.

Consider a multiobjective decision problem with uncertainty in the objective functions, given as a set of scenarios. In the single-criterion case, robust optimization methodology helps to identify solutions which remain feasible and of good quality for all possible scenarios. A well-known alternative method in the single-objective case is to compare possible decisions under uncertainty with the optimal decision with the benefit of hindsight, i.e. to minimize the (possibly scaled) regret of not having chosen the optimal decision. In this contribution, we extend the concept of regret from the single-objective case to the multiobjective setting and introduce a proper definition of multivariate (robust) (relative) regret. In contrast to the few existing ideas that mix scalarization and optimization, we clearly separate the modelling of multiobjective (robust) regret from its numerical solution. Moreover, our approach is not limited to a finite uncertainty set or interval uncertainty and furthermore, computations or at least approximations remain tractable in several important special cases. We illustrate all approaches based on a biobjective shortest path problem under uncertainty.

This paper aims to provide a scientific approach that indicates the need to focus on renewable energy potential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy technologies a few years ago. Accordingly, the issue of determining the best renewable resources for the country has been brought to the agenda in recent years. The issue was considered to be a limited multi-objective optimization problem allowing us to achieve a reliable result. However, the parameter of each resource was considered as a range value, rather than traditionally expressed as an exact value, and a multi-objective decision problem was developed with an interval coefficient. This method allows us to obtain more accurate and reliable results without the need to resort to normalization methods used to eliminate unit differences. According to the results of this study, the most convenient alternatives for Turkey are hydro, wind, and solar power. The findings also support decision policies aimed at reaching targets for the electricity sector in 2023, as put forth by the Ministry of Energy and Natural Resources (MENR).

This paper treats the Piggyback Transportation Problem: A large vehicle moves successive batches of small vehicles from a depot to a single launching point. Here, the small vehicles depart toward assigned customers, supply shipments, and return to the depot. Once the large vehicle has returned and another batch of small vehicles has been loaded at the depot, the process repeats until all customers are serviced. With autonomous driving on the verge of practical application, this general setting occurs whenever small autonomous delivery vehicles with limited operating range, e.g., unmanned aerial vehicles (drones) or delivery robots, need to be brought in the proximity of the customers by a larger vehicle, e.g., a truck. We aim at the most elementary decision problem in this context, which is inspired by Amazon’s novel last-mile concept, the flying warehouse. According to this concept, drones are launched from a flying warehouse and – after their return to an earthbound depot – are resupplied to the flying warehouse by an air shuttle. We formulate the Piggyback Transportation Problem, investigate its computational complexity, and derive suited solution procedures. From a theoretical perspective, we prove different important structural problem properties. From a practical point of view, we explore the impact of the two main cost drivers, the capacity of the large vehicle and the fleet size of small vehicles, on service quality.

Judging an act’s causal efficacy plays a crucial role in causal decision theory. A recent development appeals to the causal modeling framework with an emphasis on the analysis of intervention based on the causal Bayes net for clarifying what causally depends on our acts. However, few writers have focused on exploring the usefulness of extending structural causal models to decision problems that are not ideal for intervention analysis. I found that it is structural models, rather than intervention analysis, serves as a valuable formal tool for a range of realistic decision problems that involve mixed causal mechanisms. The thesis concludes that structural models provide a more general framework for rational decision-makers.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

The rapid and uncontrolled growth of the world's population technological developments, increase in the social welfare and the transformation of societies into consumer societies have changed the dimensions of environmental problems. Nowadays waste management has become an important issue for the solution of environmental problems. Hence, we discussed the municipal solid waste management. Municipal solid waste management problem is a complex and it has many different aspects as political, social, technological and economical criteria have to consider. The evaluation of these criteria numerically is complicated and vague. This paper deals with this complexity by proposed methodology. Also the contribution of the article to the literature is that the proposed methodology is applied for the first time in municipal solid waste management problems. In this paper two fuzzy decision making approaches are combined for sitting a waste to energy plant in the Kırıkkale in Turkey. Four alternative locations and nine criteria are defined from the expert opinions and the literature survey. A new hybrid methodology that has not been applied before for this decision problem is proposed. In proposed methodology, there are two main stages. Criteria weights determination is the first stage and ranking of the alternative locations is the second stage of the methodology. In first stage Interval type 2 Fuzzy Analytic Hierarchy process (AHP) method is performed and in the second stage hesitant fuzzy Technique for order preference by similarity to an ideal solution (TOPSIS) method is used for ranking the alternative locations. Also decision makers have different experience level and knowledge about the problem and different decision makers’ weights are considered for group decision making. Two fuzzy methods are combined for the solid waste energy production plant location selection problem. As a result of the study, the second alternative (Bahsılı-Bedesten) is determined as the most suitable area for waste to energy production plant. Besides, with scenario analysis the effect of criteria on ranking of the alternatives is analyzed.

Increasing competition as a result of globalization has forced the businesses to increase efficiency by focusing on the core competencies. This led businesses to outsource logistics. As a result, most of the businesses started to use third-party logistics (3PLs). There are various and conflicting criteria involved in 3PL selection, thus the selection of 3PL is a complex task for businesses. There are several methods in the literature used for 3PL selection, most of them are multi-criteria decision-making methods. A new hybrid model including Rough step-wise weight assessment ratio analysis (SWARA), Rough preference selection Index (RPSI), and Rough improved operational competitiveness rating analysis (IOCRA) has been used to select optimal third-party logistics providers in this study. There are two contributions to this study. First one is the development of a new hybrid rough model for 3PL selection. Second one is addressing the 3PL selection decision problem with newly developed Rough PSI and Rough IOCRA methods. Using this model and conducting a comprehensive analysis, the best option as a 3PL provider may be determined as a partner for logistics. The model effectiveness has been tested in a real case with a textile business and several 3PL providers.

Decision making is a process that ranks or chooses subsets from given sets of options, for example, project proposals or machine tools, with high relevance in industry and economics. Each decision option may be associated with a degree of utility with respect to a specific criterion. Often experts are not willing or able to quantify utilities but rather compare individual pairs of options, which leads to fuzzy pairwise preference matrices. Conventionally, decision making on preference matrices uses scoring indices that exhibit different characteristics. Selecting the most appropriate scoring index is often difficult. Scoring indices are associated with different levels of risk, and none of these indices alone can be considered superior. We show that any fuzzy preference matrix induces interval utilities which can be interpreted as memberships of interval type-2 fuzzy sets, so preference-based decision making has to take into account uncertainty and therefore has to be risk sensitive. We propose a risk-sensitive preference-based decision making method called Pareto optimization of interval utilities (PIU) that chooses a subset of options ranked by degrees of risk. This allows the decision maker to choose an option that represents an appropriate trade-off between opportunity and risk for the given decision problem. Experiments with the YouTube Comedy Slam data set show that PIU compared with conventional scoring methods allows to trade a slight decrease in the best-case utility for a strong increase in the worst-case utility, and vice versa.

Bonferroni mean (BM) operators have been established as a powerful tool for handling the interrelationship between the input arguments under various decision-making information. However, the existing BM operators do not take into account the overall interaction among decision makers or criteria. To overcome this limitation, this study considers the Shapley fuzzy measure (SFM) with the normalized weighted BM (NWBM) operator under a neutrosophic environment. In addition, the current research ignores the bipolarity and hesitancy during decision elicitations, resulting in the imprecise decision results. In this paper, the hesitant bipolar-valued neutrosophic set (HBNS) which is the extension of hesitant fuzzy set and bipolar neutrosophic set is employed. The main focus of this paper is in the development of an aggregation operator for HBNS. Based on the literature review, we would like to fill in the gaps by developing a hesitant bipolar-valued neutrosophic Shapley NWBM (HBN-SNWBM) operator where the overall interaction among decision makers can be considered. Besides that, a three-phase decision making framework is also proposed to show the applicability of the proposed aggregation operator to the real-world decision problems. The HBN-SNWBM operator and the decision making framework are applied to two examples of investment selection where evaluations are implemented using the proposed aggregations that based upon hesitant bipolar-valued neutrosophic sets. In the first example, it is found that a weapon company is the best alternative for investment followed by a food company. Sensitivity of parameters of the aggregation operator is also analysed and it is found that the ranking results are consistent despite of different parameter values used. This verifies the insensitivity of p,q parameters in the developed aggregation operator. The proposed decision making framework and hesitant bipolar-valued neutrosophic sets would be a great significance for the practical implementation of the aggregation operators.

The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.

The decision problems in the design and control of warehousing order picking process include layout design, storage assignment, routing, and order batching policies. Previous researches on layout design for pick-and-pass systems are not abundant. However, a pick-and-pass system is a commonly used approach because of the growth of e-commerce and the faster delivery of small and frequent orders. This paper presents an algorithm to determine the number of zones, and the number of picking bins and racks in each zones for the pick-and-pass systems such that both investment and operational costs can be minimized. A cycle time evaluation model is developed and incorporated into the proposed algorithm to aid a warehouse designer to quickly evaluate design alternatives for warehouse configurations. The result indicates that the cycle time evaluation model appears to be sufficiently accurate for practical purpose and the optimal layout can be determined by the proposed algorithm.

There is growing recognition that multiple forms of uncertainty influence policy reform, but we need case studies illustrating how uncertainties influence reform, and strategies used to manage uncertainties in practice. We present a case study of how science was generated and incorporated into national water policy. We estimated turbidity (suspended fine sediment) reference states throughout the river network of New Zealand. A key decision-problem was: how region-specific should reference states be? We developed classifications of the national river network that grouped rivers with similar turbidity regimes into classes at four spatial resolutions. Choosing a classification was complicated by ontological uncertainties. Scientific actors framed these ontological uncertainties in different ways, introducing framing uncertainties (‘ambiguities’) into the decision-making process. Well-established statistical techniques were employed to objectively identify the most parsimonious classification, but those techniques had little impact on decision-making. Reframing ontological uncertainties in terms of risks to management objectives was required to reconcile divergent frames, and showed that the classification with the highest resolution was the most credible option. Nonetheless, local governments highlighted that the most credible classification was not the most relevant or legitimate one in light of implementation constraints. The final decision was made against a backdrop of ambiguities, was partially subjective, and balanced trade-offs among credibility, relevance and legitimacy. Looking ahead, ambiguities could be reduced by framing decision problems in terms of risks to management objectives. A barrier to doing this is the short duration of policy windows, so translational science must take place when such windows are shut.

As a side-effect of the Covid-19 pandemic, significant decreases in medical procedures for noncommunicable diseases have been observed. This calls for a decision support assisting in the analysis of opportunities to relocate procedures among hospitals in an efficient or, preferably, optimal manner. In the current paper we formulate corresponding decision problems and develop linear (mixed integer) programming models for them. Since solving mixed integer programming problems is NP-complete, we verify experimentally their usefulness using real-world data about urological procedures. We show that even for large models, with millions of variables, the problems' instances are solved in perfectly acceptable time.

The capacities and the Choquet integral are powerful tools to represent decision problems with dependencies and aggregate correlated decision criteria. Random generation of suitable capacities is a vital and challenging task in this decision model because of an exponential number of the involved parameters as well as the associated monotonicity restrictions. In this paper we present various approaches to representing decision makers’ preferences on aggregation through linear constraints , random generation of capacities from the selected polytope , testing the uniformity of the resulting distribution, and constructing the dominance relations between the alternatives, which are subsequently used to get the most credible ranking of the alternatives.

This paper investigates the joint problem of access point (AP) assignment and power allocation (PA) in a three-tier hybrid radio frequency/visible light communication (VLC) heterogeneous network (HetNet). The main goal is to maximize the HetNet’s sum-rate under practical constraints such as APs’ power budgets and users’ quality-of-service (QoS) requirements, while maintaining an acceptable level of illumination in the VLC system. When this design problem is formulated mathematically, it turns out to be a combinatorial decision problem that involves non-linear constraints, and hence is NP hard. To efficiently obtain good quality solutions for the formulated problem, a reformulation into the college admission model is first performed. Then, a distributed and low-complexity algorithm based on matching theory and an efficient heuristic PA scheme are proposed to obtain a good quality suboptimal solution for the joint problem. Simulation results highlight the robustness of the proposed solution and its significant gain in network sum-rate as compared to different benchmark schemes. The effect of various system parameters such as the minimum QoS and maximum illumination requirements on the performance of the proposed solution is studied. Finally, the theoretical analysis of convergence, stability, and complexity of the proposed technique is performed.

The basic axioms or formal conditions of decision theory, especially the ordering condition put on preferences and the axioms underlying the expected utility (EU) formula, are subject to a number of counter-examples, some of which can be endowed with normative value and thus fall within the ambit of a philosophical reflection on practical rationality. Against such counter-examples, a defensive strategy has been developed which consists in redescribing the outcomes of the available options in such a way that the threatened axioms or conditions continue to hold. We examine how this strategy performs in three major cases: Sen's counterexamples to the binariness property of preferences, the Allais paradox of EU theory under risk, and the Ellsberg paradox of EU theory under uncertainty. We find that the strategy typically proves to be lacking in several major respects, suffering from logical triviality, incompleteness, and theoretical insularity (i.e., being cut off from the methods and results of decision theory). To give the strategy more structure, philosophers have developed "principles of individuation"; but we observe that these do not address the aforementioned defects. Instead, we propose the method of checking whether the strategy can overcome its typical defects once it is given a proper theoretical expansion (i.e., it is duly developed using the available tools of decision theory). We find that the strategy passes the test imperfectly in Sen's case and not at all in Allais's. In Ellsberg's case, however, it comes close to meeting our requirement. But even the analysis of this more promising application suggests that the strategy ought to address the decision problem as a whole, rather than just the outcomes, and that it should extend its revision process to the very statements it is meant to protect. Thus, by and large, the same cautionary tale against redescription practices runs through the analysis of all three cases. A more general lesson, simply put, is that there is no easy way out from the paradoxes of decision theory.

This paper studies robust decision problems in a rough environment that is described by Volterra processes. Various alternative dominated models are introduced to reflect decision makers' concerns ...

Satellite assisted vehicle-to-vehicle (V2V) communication can provide services for vehicles in depopulated areas, and it can be employed as an effective complementary component for terrestrial vehicular networks. Since the available communication and computing resource for satellites are scarce, task offloading, computing and communication resource allocation, which are coupled with each other, are critical issues for satellite assisted V2V communication. To tackle these problems, we formulate the joint offloading decision, computing and communication resource allocation problem for satellite assisted V2V communication as a mixed-integer nonlinear programming problem with minimum weighted-sum end-to-end latency, and we decouple it into two subproblems. First, the Lagrange multiplier method is adopted to obtain the optimal computing and communication resource allocation with fixed offloading decision. Then, the results of the resource allocation subproblem are fed into the offloading decision problem, which is formulated as a Markov decision process. To maximize the long-term reward of offloading decision, a deep reinforcement learning based method is adopted to learn the optimal offloading decision. Finally, the simulation results show that the proposed joint task offloading and resource allocation approach has superior performance compared with other schemes.

Multi-access edge computing (MEC) has already shown great potential in enabling mobile devices to bear the computation-intensive applications by offloading some computing jobs to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computational resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which may not be optimized with the lowest cost. In this paper, we propose a novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading. Specifically, we formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed and trained to jointly optimize the offloading decision and computational resource allocation. Numerical results show that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computational complexity.

Hierarchical multilevel multi-leader multi-follower problems are non-cooperative decision problems in which multiple decision-makers of equal status in the upper-level and multiple decision-makers of equal status are involved at each of the lower-levels of the hierarchy. Much of solution methods proposed so far on the topic are either model specific which may work only for a particular sub-class of problems or are based on some strong assumptions and only for two level cases. In this paper, we have considered hierarchical multilevel multi-leader multi-follower problems in which the objective functions contain separable and non-separable terms (but the non-separable terms can be written as a factor of two functions, a function which depends on other level decision variables and a function which is common to all objectives across the same level) and shared constraint. We have proposed a solution algorithm to such problems by equivalent reformulation as a hierarchical multilevel problem involving single decision maker at all levels of the hierarchy. Then, we applied a multi-parametric algorithm to solve the resulting single leader single followers problem.

Extending the popular Answer Set Programming (ASP) paradigm by introspective reasoning capacities has received increasing interest within the last years. Particular attention is given to the formalism of epistemic logic programs (ELPs) where standard rules are equipped with modal operators which allow to express conditions on literals for being known or possible, i.e., contained in all or some answer sets, respectively. ELPs thus deliver multiple collections of answer sets, known as world views. Employing ELPs for reasoning problems so far has mainly been restricted to standard decision problems (complexity analysis) and enumeration (development of systems) of world views. In this paper, we take a next step and contribute to epistemic logic programming in two ways: First, we establish quantitative reasoning for ELPs, where the acceptance of a certain set of literals depends on the number (proportion) of world views that are compatible with the set. Second, we present a novel system that is capable of efficiently solving the underlying counting problems required to answer such quantitative reasoning problems. Our system exploits the graph-based measure treewidth and works by iteratively finding and refining (graph) abstractions of an ELP program. On top of these abstractions, we apply dynamic programming that is combined with utilizing existing search-based solvers like (e)clingo for hard combinatorial subproblems that appear during solving. It turns out that our approach is competitive with existing systems that were introduced recently. This work is under consideration for acceptance in TPLP., Comment: Paper presented at the 37th International Conference on Logic Programming (ICLP 2021), 16 pages

Insurance companies and banks regularly have to face stress tests performed by regulatory instances. To model their investment decision problems that includes stress scenarios, we propose the worst-case portfolio approach. Thus, the resulting optimal portfolios are already stress test prone by construction. A central issue of the worst-case portfolio approach is that neither the time nor the order of occurrence of the stress scenarios are known. Even more, there are no probabilistic assumptions regarding the occurrence of the stresses. By defining the relative worst-case loss and introducing the concept of minimum constant portfolio processes, we generalize the traditional concepts of the indifference frontier and the indifference-optimality principle. We prove the existence of a minimum constant portfolio process that is optimal for the multi-stress worst-case problem. As a main result we derive a verification theorem that provides conditions on Lagrange multipliers and nonlinear ordinary differential equations that support the construction of optimal worst-case portfolio strategies. The practical applicability of the verification theorem is demonstrated via numerical solution of various worst-case problems with stresses. There, it is in particular shown that an investor who chooses the worst-case optimal portfolio process may have a preference regarding the order of stresses, but there may also be stress scenarios where he/she is indifferent regarding the order and time of occurrence.

In recent years, interval-valued Pythagorean fuzzy number is playing a more and more important role in decision management. It is a more effective and powerful tool to handle fuzzy information in decision problems. The multicriteria decision-making theory has been widely used in solving practical problems, such as the risk assessment of financial investment, engineering and construction, medical and health care, and information security. The main purpose of this paper is to apply a new interval-valued Pythagorean fuzzy decision-making method to practice and to analyze and solve the problem of wireless communication infrastructure. In this paper, a new interval-valued Pythagorean fuzzy ranking method, extending scope of application of the VIKOR method to interval-valued Pythagorean fuzzy set, is proposed. In order to adapt to actual needs, subjective and objective weights are combined to solve decision-making problems to enhance its practicality, validity, and effectiveness. An example of wireless communication infrastructure problem is provided to illustrate the rationality of this method and verify its advantages.

Novel interventions that are effective and safe but costly suggest the importance of questions about value, accessibility, and affordability. Economic evaluation is one useful tool that health care systems draw upon to help make investment decisions and set priorities. But translating results from economic evaluations into actionable insights about a novel intervention's value remains elusive, given ethical and practical complexities. This article illuminates 5 key factors to consider in interpretating economic evaluations of novel interventions: the health care decision problem, the design and structure of the mathematical model, characterization of uncertainty, awareness of health outcome measure limitations, and the contrast between optimal decision-making conditions and real-world decision-making conditions.

To ensure the quality and progress of the project, it is very important to select the suitable construction enterprise. Intuitionistic fuzzy preference relations (IFPRs) are useful tools for showing the positive and negative judgments of decision makers. The main purpose of preference relations is to rank compared objects fully. Thus, the analysis of consistency is necessary to guarantee the reasonable ranking. However, the current concepts of consistency are inadequate to define the consistency of IFPRs, particularly for inconsistent and incomplete IFPRs. Therefore, the utilization of IFPRs in practical decision problems is restricted because inconsistent and/or incomplete IFPRs are usually encountered. To solve this issue, a new additive consistency concept for IFPRs is defined in this paper, which is unrelated to object labels and satisfies several reasonable properties. Considering inconsistent case, a mathematical model based method for getting additively consistent IFPRs is provided. A mathematical model for acquiring unknown judgments in IFPRs is established. A procedure for decision making with IFPRs is then given. Meanwhile, numerical examples about evaluating the construction company are offered to indicate the concrete application of the new results, and a comparative analysis is performed.

This paper deals with the theoretical analysis of the spatial concentration and localization of firms and employees over a set of regions. In particular, it provides a simple site-selection theoretical model to describe the probabilistic framework of the location patterns. The adopted quantitative tool is the stochastic theory of urns. The model moves from the empirical evidence of the deviation of the spatial location of companies from the uniform distribution and of employees from the distribution of firms. Factors leading to such deviations are taken into consideration. Specifically, we formalize a decision problem grounded on the economic attributes of the regions and also on the distribution of the existing firms and employees in the territory. To our purpose, the site-selection model is presented as a stepwise process.

Data mining is a valuable tool for many industries, including finance, manufacturing, retail, medical health, insurance and telecommunications. The aim of this paper is to investigate and demonstrate that each class of decision problems needs a suitable or set of data mining techniques. Each category of decision problems applies some data mining techniques that may differ from the other categories. These techniques are determined based on the type of the problem, the scale of the dataset, as well as the dynamic changes of these data. The results illustrated that the choice of the data mining technique is decision problem-dependent meaning that for each class of decision problems there is a suitable class of data mining techniques.

In this paper, we discuss some properties of optimal consideration set in binary state continuous-action rational inattention decision problems. First, we show that it is the form of the utility function that determines the form of posterior distribution, i.e. whether it is a binary distribution or a triple or quadruple distribution, etc. Besides, we will also show that the posterior distribution may not be resulted after information acquisition in some circumstances. Second, we show that the optimal consideration set is essentially the optimal action strategies. Therefore, without further conditions, the rational inattention decision model is unidentifiable. Third, in order to identify the rational inattention decision model, we introduce another condition to the model. The discrete actions after information acquisition that satisfy the endogenously-determined probability (optimal action strategies) should satisfy the properties of probabilities, and therefore the discrete actions should also follow an exogenous probability distribution. This idea is tested by an example with quadratic utility function. Through this example, we find that under this condition, the optimal consideration set can be identified. The elements in the optimal consideration set, or equivalently the discrete signals after information acquisition, exhibit a curve or curves.

Many supply chain stakeholders rely on the cross-docking concept, according to which products delivered in specific transportation management units to the cross-docking terminal (CDT) undergo decomposition, sorting based on the end customer preferences, consolidation, and then transported to the final destinations. Scheduling of the inbound and outbound trucks for service at the CDT doors is considered as one of the convoluted decision problems faced by the CDT operators. This study proposes a new Adaptive Polyploid Memetic Algorithm (APMA) for the problem of scheduling CDT trucks that can assist with proper CDT operations planning. APMA directly relies on the polyploidy concept, where copies of the parent chromosomes (i.e., solutions) are stored before performing the crossover operations and producing the offspring chromosomes. The number of chromosome copies is controlled through the adaptive polyploid mechanism based on the objective function improvements achieved and computational time changes. Moreover, a number of problem-specific hybridization techniques are used within the algorithm to facilitate the search process. Computational experiments show that the application of adaptive polyploidy alone may not be sufficient for the considered decision problem. Hybridization techniques that directly consider problem-specific properties are required in order to improve solution quality at convergence. Furthermore, the APMA algorithm developed in this article substantially outperforms some of the well-known state of the art metaheuristics with regards to solution quality and returns truck schedules that have lower total truck service cost.

We study the dynamic programming approach to revenue management in the context of attended home delivery. We draw on results from dynamic programming theory for Markov decision problems to show that the underlying Bellman operator has a unique fixed point. We then provide a closed-form expression for the resulting fixed point and show that it admits a natural interpretation. Moreover, we also show that – under certain technical assumptions – the value function, which has a discrete domain and a continuous codomain, admits a continuous extension, which is a finite-valued, concave function of its state variables, at every time step. Furthermore, we derive results on the monotonicity of prices with respect to the number of orders placed in our setting. These results open the road for achieving scalable implementations of the proposed formulation, as it allows making informed choices of basis functions in an approximate dynamic programming context. We illustrate our findings on a low-dimensional and an industry-sized numerical example using real-world data, for which we derive an approximately optimal pricing policy based on our theoretical results.

If ever the concept “VUCA” (Volatility, Uncertainty, Complexity, and Ambiguity) seemed appropriate to use, it is now. National and global companies experience the highest level of instability due to the Covid-19 pandemic, which is the classic example of a highly volatile, uncertain, complex, and ambiguous world. In this world, decision-makers have to face more challenges appealing to the VUCA Prime leadership approach: vision against volatility, understanding against uncertainty, clarity against complexity, and agility against ambiguity. Some of the ways through which managers can overcome the VUCA characteristics include: providing a shared vision as a criterion for all decisions to be made, identifying the reason for the decision problems and sharing the idea with the followers, going through the entire decision process, following steps in proper order, and developing quick solutions. In an inventory decision taken in a VUCA context, the above ways are possible if using fuzzy inventory methods dealing with volatility, uncertainty, complexity, and ambiguity. This paper aims to adapt a traditional inventory method, Economic Production Quantity (EPQ), to the challenges of the VUCA world, through the fuzzy logic system (FLS). To achieve the best solution for the decision problem in the shortest time possible, the managers can employ a conversion by using the computing platform MATLAB. There are some advantages of this conversion for these two methods, EPQ and FLS. Firstly, the transformation of EPQ in ELQ (Economic Logic Quantity) allows managers to formulate the decision problem, even if they cannot identify and measure precisely the EPQ parameters. Secondly, using FLS to solve ELQ provides the possibility to simulate more alternatives and generate the solution in the shortest amount of time. Thirdly, it allows the decision-makers to evaluate the impact of the solution provided by each simulation on the company’s performance. Using these methods has the following primary limit: the problem formulation step depends on the managers’ understanding ability and managing a large volume of information. Therefore, there may be a risk of obtaining a relevant solution for a decision problem if the decision-makers do not understand the cause of the problem or do not know how to organize and manage a large volume of information. This limit could be overcome by using AHP (Analytic Hierarchy Process), but this is the topic of further research.

The open shop is a classical scheduling problem known since 1976, which can be described as follows. A number of jobs have to be processed by a given set of machines, each machine should perform an operation on every job, and the processing times of all the operations are given. One has to construct a schedule to perform all the operations to minimize finish time also known as the makespan. The open shop problem is known to be NP-hard for three and more machines, while is polynomially solvable in the case of two machines. We consider the routing open shop problem, being a generalization of both the open shop problem and the metric traveling salesman problem. In this setting, jobs are located at nodes of a transportation network and have to be processed by mobile machines, initially located at a predefined depot. Machines have to process all the jobs and return to the depot to minimize makespan. A feasible schedule is referred to as normal if its makespan coincides with the standard lower bound. We introduce the Irreducible Bin Packing decision problem, use it to describe new sufficient conditions of normality for the two machine problem, and discuss the possibility to extend these results on the problem with three and more machines. To that end, we present two new computer-aided optima localization results.

We review applications of the Fokker–Planck equation for the description of systems with event trains in computational and cognitive neuroscience. The most prominent example is the spike trains generated by integrate-and-fire neurons when driven by correlated (colored) fluctuations, by adaptation currents and/or by other neurons in a recurrent network. We discuss how for a general Gaussian colored noise and an adaptation current can be incorporated into a multidimensional Fokker–Planck equation by Markovian embedding for systems with a fire-and-reset condition and how in particular the spike-train power spectrum can be determined by this equation. We then review how this framework can be used to determine the self-consistent correlation statistics in a recurrent network in which the colored fluctuations arise from the spike trains of statistically similar neurons. We then turn to the popular drift-diffusion models for binary decisions in cognitive neuroscience and demonstrate that very similar Fokker–Planck equations (with two instead of only one threshold) can be used to study the statistics of sequences of decisions. Specifically, we present a novel two-dimensional model that includes an evidence variable and an expectancy variable that can reproduce salient features of key experiments in sequential decision making.

Creation of an efficient informational and analytical subsystem of prevention management processes and localization consequences of the emergency situations (ES) has been offered. It was made by complex inclusion in the operating Unified State Civil Protection System (USCPS) (from object to government) levels of various functional territorial system elements of monitoring emergency and system components of the situational centers. It was shown that one of trial functions of system situational centers at all USCPS management levels is development of efficient response crisis solutions, which is complicated by the fact that necessary conditions of effectiveness decisions is their timeliness, completeness and optimality. For increase in effectiveness of a decision making need of formal development, normative methods and models of a complex solution of the making decision problem in the conditions of the multicriterial and indeterminacy at prevention management processes and consequences emergency localization has been proved.In order to develop the scientific and technical foundations for creating a support system for making anti-crisis decisions in the system of situational centers of the USCPS, the paper presents a methodology for substantiating optimal anti-crisis solutions to ensure an appropriate level of safety of the state in ES of various nature in conditions uncertainty of input information for experts of the system of situational centers.

Portfolio selection can be regarded as a type of multi-objective decision problem. However, traditional solution methods rarely discussed the decision maker’s nonsatisfaction and hesitation degrees with regard to multiple objectives and they require many extra binary variables, which lead to tedious computational burden. Based on the above, the aim of this paper is to develop a new and unified intuitionistic fuzzy multi-objective linear programming (IFMOLP) model for such portfolio selection problems. The nonmembership functions are constructed by the pessimistic, optimistic, and mixed approaches so as to perfect the traditional intuitionistic fizzy (IF) inequalities and IF theory. The decision maker’s hesitation degrees with regard to multiple objectives are represented by using IF inequalities, and the new IFMOLP model based on IF inequalities is proposed. The IFMOLP problems are solved by the S-shaped membership functions without extra binary variables required by the piecewise-linear method. Finally, the portfolio selection model under IF environments based on IFMOLP is established, and a real example is analyzed to demonstrate its validity and superiority. The developed unified IFMOLP model and method can not only effectively solve multi-objective decision problems with nonsatisfaction and hesitation degrees but also remarkably reduce the complexity of the nondeterministic polynomial-hard problems.

Multi-attribute decision making (MADM) refers to a decision problem of choosing the best alternative or carrying out the ranking of alternatives under multiple attributes, and it is a key component of modern decision sciences. Three-way decisions (3WD) can effectively address MADM problems by reducing decision risks compared with traditional two-way decisions (2WD), and a decision model based on probabilistic dominance relations is presented in this paper. Particularly, we put forward a kind of 3WD-based MADM (3WD-MADM) with probabilistic dominance relations, where the two state sets are developed by virtue of probabilistic dominance classes. Afterwards, the independent innovation ability of high-tech enterprises is evaluated by using the proposed new method. At last, the newly proposed 3WD-MADM model in this paper is verified from different perspectives by comparative and experimental analyses.