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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.

In this paper, we consider a natural generalization of the concept of order of an element in a group: an element $g \in G$ is said to have order $k$ in a subgroup $H$ of $G$ (\resp \wrt a coset $Hu$) if $k$ is the first strictly positive integer such that $g^k \in H$ (\resp $g^k \in Hu$). We study this notion and its algorithmic properties in the realm of free groups and some related families. Both positive and negative (algorithmic) results emerge in this setting. On the positive side, among other results, we prove that the order of elements, the set of orders (called spectrum), and the set of preorders (\ie the set of elements of a given order) \wrt finitely generated subgroups are always computable in free and free times free-abelian groups. On the negative side, we provide examples of groups and subgroups having essentially any subset of natural numbers as relative spectrum; in particular, non-recursive and even non-recursively enumerable sets of natural numbers. Also, we take advantage of Mikhailova's construction to see that the spectrum membership problem is unsolvable for direct products of nonabelian free groups., Comment: 44 pages, 6 figures

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.

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and dynamic optimization machinery in a consistent and efficient way to build a bridge from data to decisions. The proposed framework tackles a relevant class of dynamic decision problems comprising many important practical applications. The basic building blocks of our proposed framework are: (1) a Hidden Markov Model as a predictive (machine learning) method to represent uncertainty; and (2) a distributionally robust dynamic optimization model as a prescriptive method that takes into account estimation errors associated with the predictive model and allows for control of the risk associated with decisions. Moreover, we present an evaluation framework to assess out-of-sample performance in rolling horizon schemes. A complete case study on dynamic asset allocation illustrates the proposed framework showing superior out-of-sample performance against selected benchmarks. The numerical results show the practical importance and applicability of the proposed framework since it extracts valuable information from data to obtain robustified decisions with an empirical certificate of out-of-sample performance evaluation.

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.

The Satisfiability Modulo Theories (SMT) issue concerns the satisfiability of formulae from multiple background theories, usually expressed in the language of first-order predicate logic with equality. SMT solvers are often based on variants of the Nelson-Oppen combination method, a solver for the quantifier-free fragment of the combination of theories with disjoint signatures, via cooperation among their decision procedures. When each of the theories to be combined by the Nelson-Oppen method is convex (that is, any conjunction of its literals can imply a disjunction of equalities only when it implies at least one of the equalities) and decidable in polynomial time, the running time of the combination procedure is guaranteed to be polynomial in the size of the input formula. In this paper, we prove the convexity of a fragment of Zermelo-Fraenkel set theory, called Multi-Level Syllogistic, most of whose polynomially decidable fragments we have recently characterized., In Proceedings GandALF 2021, arXiv:2109.07798

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 ...

Dünya, 2020 yılı başından itibaren insan sağlığı için ciddi bir tehdit oluşturan Covid-19 ile karşı karşıya kalmıştır. Covid-19 kaynaklı ölümler üzerinde etkili faktörlerin belirlenmesi virüsle mücadele kapsamında oldukça önemli olup birçok araştırmaya konu olmuştur. Çok ölçütlü karar verme yöntemleri, karar problemi üzerinde etkili olabilecek tüm faktörleri birlikte değerlendirme olanağı sağlayarak karar vericinin etkin sonuca ulaşmasını destekleyen yöntemlerdir. Bu çalışmada, Covid-19 hastalığının risk grubunu oluşturan faktörler çok ölçütlü karar verme yapısına uygun olarak incelenmiştir. Bir problemi etkileyen faktörlerin etkileme gücünü ölçebilen yöntemlerden biri olan DEMATEL (Decision Making Trial and Evaluation Laboratory) yöntemi yardımıyla genel kabul görmüş faktörlerin farklı bir açıdan değerlendirilmesi amaçlanmıştır. Literatürden çalışmanın amacına uygun veriler derlenmiş ve çeşitli kaynaklarda referans olarak verilen faktörler tespit edilmiştir. Ölümler üzerinde etkili olduğu bilinen bu risk faktörlerinin farklı eşik değerleri için sayısal analizleri yapılarak birbirlerini etkileme düzeyleri araştırılmıştır. Eşik değerler belirlenirken virüsün yayılma seyri olan göz önünde bulundurulmuş ve farklı periyotlar için uygulanacak stratejiler bakımından baz alınabilecek sonuçlar üzerinde durulmuştur. Son olarak, çok ölçütlü analiz sonuçları DEMATEL yönteminin temel prensiplerine uygun olarak yorumlanmıştır.

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-criteria decision-making (MCDM) is concerned with structuring and solving decision problems involving multiple criteria for decision-makers in vague and inadequate environment. The “Technique for Order Preference by Similarity to Ideal Solution’’ (TOPSIS) is one of the mainly used tactic to deal with MCDM setbacks. In this article, we put forward an extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers (IT2TrNNs) using the concept of (α, β, γ)-cut. First, we present a novel approach to compute the distance between two IT2TrNNs using ordered weighted averaging (OWA) operator and (α, β, γ)-cut. Subsequently, we broaden the TOPSIS method in the context of IT2TrNNs and implemented it on a MCDM problem. Lastly, a constructive demonstration and several contrasts with the other prevailing techniques are employed to articulate the practicability of the proposed technique. The presented strategy yields a flexible solution for MCDM problems by considering the attitudes and perspectives of the decision-makers.

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

In this paper, we review some results of the solution of generalized stable sets introduced by Van Deemen (1991) as a variant of stable sets for abstract decision problems. This solution will be investigated and reviewed for the more general case of irreflexive but not necessarily asymmetric or complete dominance relations. It is proven that the fundamental properties of this solution are preserved also for this kind of dominance relations. Two main shortcomings of the solution of generalized stable sets are firstly that it may contain Pareto-suboptimal alternatives when the dominance relation is derived from pairwise majority comparison, and secondly that it may fail to discriminate among the alternatives under consideration when the dominance relation is a Hamilton cycle, i.e. a cycle that includes all alternatives. A refinement of generalized stable sets is proposed in order to address these two shortcomings. This refinement is called the solution of undisturbed generalized stable sets. It will be shown that undisturbed generalized stable sets are Pareto optimal and have discriminating power in the case of Hamilton cycles. In addition, and perhaps more important, we prove that this refinement is always a subset of the solutions of the uncovered set and of the unsurpassed set. This implies that undisturbed generalized stable set also may be seen as a refinement of both the uncovered set and the unsurpassed set.