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Existing task offloading strategy usually makes offloading decision within one time slot without considering the internal relationship between multiple offload time slots, so they cannot be offloaded according to the actual needs of tasks. To solve this problem, this paper proposed a task secondary application offloading strategy based on deep Q network（DQN-TSAO）. Firstly, this paper introduced a three-layer of cloud-edge-end architecture that supports task secondary application offloading, and established priority model, delay model and energy consumption model for task offloading. Second, aiming at minimizing system energy consumption, the energy consumption optimization problem is transformed into a Markov decision process problem of maximum cumulative offloading reward. Finally, DQNTSAO algorithm can extract the task offload characteristics of each time slot, which enabled the task to obtain the optimal offloading decision of multiple time slots in the continuous interaction with the environment. Simulation results validated that DQN-TSAO algorithm can effectively reduce the total energy consumption of the system in a period of time. [ABSTRACT FROM AUTHOR]

The basic notions in rough set theory are lower and upper approximation operators defined by a fixed binary relation. This paper proposes an intuitionistic fuzzy rough set (IFRS) model which is a combination of intuitionistic fuzzy set (IFS) and rough set. We approximate an IFS by using soft binary relations instead of fixed binary relations. By using this technique, we get two pairs of intuitionistic fuzzy (IF) soft sets, called the upper approximation and lower approximation with respect to foresets and aftersets. Properties of newly defined rough set model (IFRS) are studied. Similarity relations between IFS with respect to this rough set model (IFRS) are also studied. Finally, an algorithm is constructed depending on these approximations of IFSs and score function for decision-making problems, although a method of decision-making algorithm has been introduced for fuzzy sets already. But, this new IFRS model is more accurate to solve the problem because IFS has degree of nonmembership and degree of hesitant. [ABSTRACT FROM AUTHOR]

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users' experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce and study different kinds of fuzzy soft β-neighborhoods called fuzzy soft β-adhesion neighborhoods and to analyze some of their properties. Further, the concepts of soft β-adhesion neighborhoods are investigated and the related properties are studied. Then, we present new kinds of lower and upper approximations by means of different fuzzy soft β-neighborhoods. The relationships among our models (i.e., Definitions 3.9, 3.12, 3.15 and 3.18) and Zhang models [48] are also discussed. Finally, we construct an algorithm based on Definition 3.12, when k = 1 to solve the decision-making problems and illustrate its applicability through a numerical example. [ABSTRACT FROM AUTHOR]

Selecting a cohort from a set of candidates is a common task within and beyond academia. Admitting students, awarding grants, and choosing speakers for a conference are situations where human biases may affect the selection of any particular candidate, and, thereby the composition of the final cohort. In this paper, we propose a new algorithm, entrofy, designed to be part of a human-in-the-loop decision making strategy aimed at making cohort selection as just, transparent, and accountable as possible. We suggest embedding entrofy in a two-step selection procedure. During a merit review, the committee selects all applicants, submissions, or other entities that meet their merit-based criteria. This often yields a cohort larger than the admissible number. In the second stage, the target cohort can be chosen from this meritorious pool via a new algorithm and software tool called entrofy. entrofy optimizes differences across an assignable set of categories selected by the human committee. Criteria could include academic discipline, home country, experience with certain technologies, or other quantifiable characteristics. The entrofy algorithm then yields the approximation of pre-defined target proportions for each category by solving the tie-breaking problem with provable performance guarantees. We show how entrofy selects cohorts according to pre-determined characteristics in simulated sets of applications and demonstrate its use in a case study of Astro Hack Week. This two stage candidate and cohort selection process allows human judgment and debate to guide the assessment of candidates' merit in step 1. Then the human committee defines relevant diversity criteria which will be used as computational parameters in entrofy. Once the parameters are defined, the set of candidates who meet the minimum threshold for merit are passed through the entrofy cohort selection procedure in step 2 which yields a cohort of a composition as close as possible to the computational parameters defined by the committee. This process has the benefit of separating the meritorious assessment of candidates from certain elements of their diversity and from some considerations around cohort composition. It also increases the transparency and auditability of the process, which enables, but does not guarantee, fairness. Splitting merit and diversity considerations into their own assessment stages makes it easier to explain why a given candidate was selected or rejected, though it does not eliminate the possibility of objectionable bias. [ABSTRACT FROM AUTHOR]

Abstract The level set method for modeling multi-fluid interfaces gives a simple approach for the simulation of multiphase flow problems. However, many of the components of this method, such as the redistancing of the level set function and discretization of the singular surface tension force, become more difficult on unstructured meshes in a control volume framework; furthermore, these operations are shown to lead to errors in conservation of mass of the individual phases, as well as unphysical currents near the interface. This paper presents a novel formulation for the level set method combined with a balanced-force algorithm using a control volume finite element method (CVFEM) for unstructured grids. A diffuse interface description is used, allowing a straightforward inclusion of surface tension forces; we show that by consistently evaluating the pressure gradient and surface tension, we are able to discretely balance these two forces and suppress any parasitic currents given a prescribed curvature. By converting the redistancing equation to a form that is suitable for CVFEM and enforcing a constraint directly linked with the phase fraction in each cell, we are able conserve mass and preserve the shape of the interface to a high degree of accuracy in multiphase flow problems with high density and viscosity ratios. The accuracy of the of our proposed method is assessed by comparing with benchmark results from the literature. Highlights • A novel level set formulation utilizing CVFEM is proposed. • A balanced-force algorithm is formulated for purely unstructured meshes. • The redistancing equation is reformulated to allow higher order upwinding methods. • A new constraint is introduced to preserve the shape of the interface. • Benchmark examples show convergence for high density and viscosity ratio problems. [ABSTRACT FROM AUTHOR]

Distribution centers (DCs) are an important part of the modern logistics system. The selection of a location for a DC is significant for saving costs and reducing externalities caused by distribution. In this paper, we propose a new hybrid method based on the analytic hierarchy process (AHP) and 2-tuple hybrid ordered weighted averaging (THOWA) to select the location of a DC in a megacity. First, we propose a new set of evaluation criteria integrating economic, political, social and ecological information based on the characteristics of Chinese megacities. Second, subjective criteria weights are calculated by AHP combining the evaluation of logistics experts. Third, experts from academia, enterprise and government assess the performance of alternatives. In addition, the overall evaluation values are aggregated by an improved THOWA operator to rank the alternatives. Finally, we conduct a sensitivity analysis to investigate the influence of criteria weights on the decision-making process. The proposed method is novel and addresses the uncertainty under limited quantitative information, which has the advantages of avoiding information loss and distortion problems in the integrating process and operating linguistic evaluation information effectively. The proposed method can be practically applied by municipal planning departments in deciding on the location of new DCs. A numerical application of the proposed method is provided. [ABSTRACT FROM AUTHOR]

Little is known about the interplay between affective and cognitive processes of decision making within the bounded rationality perspective, in particular for the debate on adaptive decision making and strategy selection. This gap in the knowledge is particularly important as affect and deliberation may direct preferences in opposite directions. How do decision makers solve such dissonance? In this paper, we address this question by exploring the use of integral affect as a choice heuristic in comparison with and in conjunction to “take the best,” and weighted addition of attributes (WADD). We operationalize theories of reliance on affect in choice through a "Take the emotionally best" algorithm. Its predictive power is experimentally tested against other models, including mixed-sequential cognitive/affective procedures. We find that individual decisions are better predicted by a sequential combination of "Take the emotionally best" and "Take the best" with a slight dominance of the former. Conditions of cognitive/affective ambivalence, low discrimination ability and high complexity provide the cognitive architecture where such blended choice strategies predict decisions more precisely. This implies that reliance on integral affect may precede the use of cognitive cues following an ecological rationality perspective rather than supporting a kind of competition between affect and cognition as implied in current literature. [ABSTRACT FROM AUTHOR]

This paper considers the role of costless decisions relating to the extraction of a non-renewable resource in the presence of uncertainty. We begin by deriving a size scale of the extractable resource, above which the solution to the valuation and optimal control strategy can be described by analytic solutions; we produce solutions for a general form of operating cost function. Below this critical resource size level the valuation and optimal control strategy must be solved by numerical means; we present a robust numerical algorithm that can solve such a class of problem. We also allow for the embedding of an irreversible investment decision (abandonment) into the optimisation. Finally, we conduct experimentation for each of these two approaches (analytical and numerical), and show how they are consistent with one another when used appropriately. The extensions of this paper's techniques to renewable resources are explored. [ABSTRACT FROM AUTHOR]

The paper presents an interactive programming approach to find the compromise optimal solution of the multi-level multi-objective linear programming problem. The solving process can be divided into analysis stage and decision-making stage. In the analysis stage, an evaluation function is constructed to express the difference between the objective and ideal values. In the decision-making stage, decision maker can compare the objective values with ideal values. If there has unsatisfied objective values, decision maker can make a concession of the satisfied values to improve the unsatisfied values. When the satisfactory degree of decision maker in upper levels have been mat, the problem of lower levels will be in solving. A characteristic of the proposed method comes from its continuous interaction with the decision maker. Finally, a numerical example is solved by this algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel complex neutrosophic soft expert set (CNSES) concept. The range of values of CNSES is extended to the unit circle in the complex plane by adding an additional term called the phase term which describes CNSES's elements in terms of the time aspect. CNSES is a hybrid structure of soft sets and single-valued neutrosophic sets (SVNSs) defined in a complex setting where the experts' opinions are included, thus making it highly suitable for use in decision-making problems that involve uncertain and indeterminate data where the time factor plays a key role in determining the final decision. Based on this new concept we define some concepts related to this notion as well as some basic operations namely the complement, union, intersection, AND and OR. The basic properties and relevant laws pertaining to this concept such as the De Morgan's laws are also verified. Lastly, we propose an algorithm to solve complex neutrosophic soft expert decision-making problem by converting it from the complex state to the real state and subsequently provided the detailed decision steps. This study is supported by the comparison with other existing methods. [ABSTRACT FROM AUTHOR]

A new grey phase plane control algorithm is proposed to overcome the problem of low control accuracy in normal phase plane control algorithm. The grey phase plane control rules can be established by the experts' control experience. By the process of greying, grey decision-making and whitening for the error and the error change rate of the control system, the control variables can be obtained, thereafter the grey phase plane control is implemented A pneumatic position servo experimental system with the DGPL type rod-less cylinder selected as control object has been developed to compare the accuracy between the normal phase plane control method and the proposed method in this paper. The experimental results demonstrated that the method proposed in this paper is simple, feasible, effective and of higher control accuracy. [ABSTRACT FROM AUTHOR]

This paper discusses characteristics unique in the two-phase high-frequency-testing (HFT) environment in semiconductor manufacturing. We believe this paper is the first to define, formalize, and analyze the decision making problem associated with the two-phase HFT. Specifically, this paper defines the problem of minimizing the total HFT capacity usage by systematically finding the optimal number of preliminary HFT bin-1 chips subject to the main HFT, with the existence of the target service rate. We also propose a heuristic algorithm that exploits the special structure of the problem for efficiently obtaining a near-optimal solution. Finally, a numerical analysis and a case study have been conducted to gain more insights on the problem structure and the proposed algorithm. [ABSTRACT FROM AUTHOR]

• New point operators for picture fuzzy sets are defined. • Memoryless multiple rounds voting process is stated. • A multi-attribute decision making algorithm is presented to address the problems. • Superiority of the algorithm is demonstrated through numerical examples. Picture fuzzy set (PFS), a generalization of the Atanassov-intuitionistic fuzzy set, is one of the most trustworthy tool to handle the uncertainties in the data during the decision-making process. The objective of this paper is to present some different kinds of the point operators for the PFS. The key feature of the presented point operators is to provide a smaller degree of uncertainty which is equivalent to the corresponding PFS. The desirable properties of the proposed operators are also derived in details. Also, we provide a memoryless multiple rounds voting process under the PFS environment to strength the result, and hence deduce a relationship between the proposed PFS and the existing set. In addition to this, we address the MADM (multiple attribute decision-making) problems by presented an algorithm to solve the problem based on the proposed point operators. The applicability and the superiority of the proposed algorithm are shown through two numerical examples. At last, the validity of the proposed algorithm is tested using different test criteria. [ABSTRACT FROM AUTHOR]

Abstract: Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study. [Copyright &y& Elsevier]

In this paper, one of the multi-criteria models in making decision, a Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), is described. Some of the advantages of TOPSIS methods are: simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form. The paper has a review character. It systematises the knowledge within the scope of techniques of decision taking with the use of the TOPSIS method. Simple numerical examples that reference real situations show practical applications of different aspects of this method. The paper is organized as follows. The Introduction presents a short overview of the decision making steps as well as MCDM techniques. Section 1 presents matrix representation of the MCDM problem. Section 2 describes the TOPSIS procedure for crisp data, and Section 3 for interval data. The TOPSIS algorithm in group decision environment in the case of crisp and interval data is also presented. In Section 4 the problem of qualitative data in TOPSIS model is discussed. The numerical examples showing applications of those techniques in the negotiation process are presented in Section 5. Finally, conclusions and some concluding remarks are made in last section. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we extend technique for order preference by similarity ideal solution (TOPSIS) for solving large scale multiple objective programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the α-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the α-level sets of fuzzy numbers. An interactive fuzzy decision making algorithm for generating α-Pareto optimal solution through TOPSIS approach is provided where the decision maker (DM) is asked to specify the degree α and the relative importance of objectives. Finally, a numerical example is given to clarify the main results developed in the paper. [Copyright &y& Elsevier]

Abstract: In this paper, from among multi-criteria models in making complex decisions and multiple attribute models for the most preferable choice, technique for order preference by similarity ideal solution (TOPSIS) approach has been dealt with. In some cases, determining precisely the exact value of the attributes is difficult and that, as a result of this, their values are considered as intervals. Therefore, the aim of this paper is to extend the TOPSIS method for decision-making problems with interval data. By extension of TOPSIS method, an algorithm to determine the most preferable choice among all possible choices, when data is interval, is presented. Finally, an example is shown to highlight the procedure of the proposed algorithm at the end of this paper. [Copyright &y& Elsevier]

In a real construction project, the duration and cost of each activity could change dynamically as a result of many uncertain variables, such as weather, resource availability, productivity, etc. Managers/planners must take these uncertainties into account and provide an optimal balance of time and cost based on their own experience and knowledge. In this paper, fuzzy sets theory is applied to model the managers’ behavior in predicting time and cost pertinent to a specific option within an activity. Genetic algorithms are used as a searching mechanism to establish the optimal time–cost profiles under different risk levels. In addition, the nonreplaceable front concept is proposed to assist managers in recognizing promising solutions from numerous candidates on the Pareto front. Economic analysis skills, such as the utility theory and opportunity cost, are integrated into the new model to mimic the decision making process of human experts. A simple case study is used for testing the new model developed. In comparison with the previous models, the new model provides managers with greater flexibility to analyze their decisions in a more realistic manner. The results also indicate that greater robustness may be achieved by taking some risks. This research is relevant to both industry practitioners and researchers. By incorporating the concept of fuzzy sets, managers can represent the range of possible time–cost values as well as their associated degree of belief. The model presented in this paper can, therefore, support decision makers in analyzing their time–cost optimization decision in a more flexible and realistic manner. Many novel ideas have also been incorporated in this paper to benefit the research community. Examples of these include the use of fuzzy sets theory, nonreplaceable front concept, utility theory, opportunity cost, etc. With suitable modifications, these concepts can be applied to model to other similar optimization problems in construction. [ABSTRACT FROM AUTHOR]

This paper addresses the planning issue in hinterland barge transport domain, characterized by limited information sharing, lack of cooperation and conflict of interest among different parties (terminal and barge party). The planning problem is formulated as a novel Stackelberg game to model a leader–followers bi-level optimization problem. A hybrid algorithm is developed that concerns different objectives (vessel turn around and terminal berthing capacity) simultaneously while fulling pre-defined operational constraints. The presented algorithm is outlined in a hierarchical way and embedded into dedicated agents as decision-making kernel. We describe the architecture and the implementation of the proposed mediator-based multi agent system and overall coupling framework include agent identification, coordination and decision making. A case study evaluates the performance of our approach in terms of global optimality compared with other related approach. [ABSTRACT FROM AUTHOR]

This paper presents a robust tabu search approach to the traveling-salesman problem with time windows. The approach uses a two-level tour hashing scheme within a reactive tabu search procedure to detect unique repeated solutions and to promote a more diverse search. Strong feasibility conditions restrict the neighborhood of candidate solutions. The search returns optimal, or near-optimal, solutions within a fraction of the time required by optimal methods. The algorithm's search neighborhood is not restricted to feasible solutions, and superior marginally infeasible solutions are reported to enhance the decision making process. The paper presents computational results for 145 problems from the literature. [ABSTRACT FROM AUTHOR]

This paper discusses decision making based on intuitionistic fuzzy soft sets by means of grey relational analysis and D-S theory of evidence. An algorithm on this decision making is presented and an example is employed to show the algorithm is efficient for solving decision problems. [ABSTRACT FROM AUTHOR]

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is originally proposed to solve binary classification problem. Multi-class classification is solved by combining multiple binary classifiers, which leads to high computation cost by introducing many quadratic programming (QP) problems. To decrease computation cost, hyper-sphere SVM is put forward to compute class-specific hyper-sphere for each class. If all resulting hyper-spheres are independent, all training and test samples can be correctly classified. When some of hyper-spheres intersect, new decision rules should be adopted. To solve this problem, a multiple sub-hyper-sphere SVM is put forward in this paper. New algorithm computed hyper-spheres by SMO algorithm for all classes first, and then obtained position relationships between hyper-spheres. If hyper-spheres belong to the intersection set, overlap coefficient is computed based on map of key value index and mother hyper-spheres are partitioned into a series of sub-hyper-spheres. For the new intersecting hyper-spheres, one similarity function or same error sub-hyper-sphere or different error sub-hyper-sphere are used as decision rule. If hyper-spheres belong to the inclusion set, the hyper-sphere with larger radius is partitioned into sub-hyper-spheres. If hyper-spheres belong to the independence set, a decision function is defined for classification. With experimental results compared to other hyper-sphere SVMs, our new proposed algorithm improves the performance of the resulting classifier and decreases computation complexity for decision on both artificial and benchmark data set. [ABSTRACT FROM AUTHOR]

This paper proposes a combination weighting algorithm using relative entropy for document clustering. Combination weighting is widely used in multiple attribute decision making (MADM) problem. However, there exist two difficulties to hinder the applications of combination weighting on document clustering. First, combination weighting is based on the integration of subjective weighting and objective weighting. However, there are so many attributes in documents that the subjective weights which rely on manual annotation by experts are impracticable. Secondly, a document data object might contain hundreds or even thousands of features. It is an extremely time-consuming task to calculate the combination weights. To address the issues, we suggest to simplify the combination weighting by not distinguishing subjective weight and objective weight. Meanwhile, we choose relative entropy method to reduce running time. In our algorithm, we obtain a combination weight set with 14 combination forms. The experiments on real document data show that both on the AC/PR/RE measures and the mutual information (MI) measure, the proposed CWRE-sIB algorithm is superior to the original sequential information bottleneck (sIB) algorithm and a series of weighting-sIB algorithms, which are built by applying a single weighting scheme to the original sIB algorithm. [ABSTRACT FROM AUTHOR]

To research the internal relations of fuzzy set and grey set, and to expand the application range of grey decision, this paper proposes a conversion method for grey sets of intuitionistic fuzzy sets, and studies the decision-making method based on grey relation and grey possibility degree. By integrating membership grade, non-membership grade and hesitating degree into grey membership grade decision matrix, we conform index weight by using interval grey relation method, change grey membership grade decision matrix into solution interval grey number by using index weight, and sort the solution by grey possibility degree. In the end, an example is given to illustrate the feasibility and effectiveness of this method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an efficient optimization technique is proposed for constrained optimization problems with interval valued objective function. At first, the significance of interval-valued objective function and the meaning of the interval-valued solution of the proposed problem have been explained with graphical interpretation. Generally, this type of problems has infinitely many compromise solutions. The aim of this approach is to obtain one of such solutions with higher accuracy and lower computational cost. The proposed technique is mainly based on the splitting criterion of the accepted/prescribed search region, calculation of the interval inclusion functions and the selection of subregion depending on the modified interval order relations in the context of the decision makers’ point of view. Novel interval oriented constraint satisfaction rules are used for non-interval equality and inequality constraints. Clearly, the proposed technique is nothing but an imitation of well known interval analysis based branch and bound (B &B) optimization approach. The modified multi-section division criterion with some new interval oriented constraint satisfaction rules for non-interval-valued equality and inequality constraints and some novel interval order relations in the context of the decision makers’ point of view have been applied to increase the efficiency of the proposed algorithm. Finally, the technique is applied for solving some test problems and the results are compared with the same obtained from the existing methods. [Copyright &y& Elsevier]

Abstract: Soft set theory was originally proposed by Molodtsov as a general mathematical tool for dealing with uncertainty in 1999. Recently, researches of decision making based on soft sets have got some progress, but few people consider multi-experts situation. As such, this paper discusses multi-experts group decision making problems. Firstly, we give a concept of intuitionistic fuzzy soft matrix (IFSM) and prove some relevant properties of IFSM. Then, an adjustable approach is presented by means of median level soft set and p-quantile level soft set for dealing with decision making problems based on IFSM. Thirdly, we study aggregation methods of IFSM, give two kinds of aggregation operators and methods that how to determine experts’ weights under different situation with programming models, four corresponding algorithms have been proposed, too. Finally, a practical example has been demonstrated the reasonability and efficiency of these new algorithms. [Copyright &y& Elsevier]

Abstract: Pixel expansion and visual quality of the revealed secret image are two major concerns in visual secret sharing (VSS). Random grid (RG) is an alternative approach to solve the pixel expansion problem by making the share as big as the original secret image, at the expense of sacrificing the visual quality of the reconstructed secret image. In this paper, two algorithms, including a contrast-enhanced RG-based VSS and a void-and-cluster-based (VAC-based) post-processing, are introduced to improve the reconstructed image quality. Experimental results and theoretical analysis are provided, illustrating that competitive visual quality is obtained by combined use of the two proposed methods. [Copyright &y& Elsevier]

Abstract: Molodtsov’s soft set theory was originally proposed as a general mathematical tool for dealing with uncertainty. By combining the multi-fuzzy set and soft set models, the purpose of this paper is to introduce the concept of multi-fuzzy soft sets. Some operations on a multi-fuzzy soft set are defined, such as complement operation, “AND” and “OR” operations, Union and Intersection operations. Then, the DeMorgan’s laws are proved. Finally, by means of level soft set, an algorithm is presented, and a decision problem is analyzed using multi-fuzzy soft set. [Copyright &y& Elsevier]

Abstract: This paper introduces many objective robust decision making (MORDM). MORDM combines concepts and methods from many objective evolutionary optimization and robust decision making (RDM), along with extensive use of interactive visual analytics, to facilitate the management of complex environmental systems. Many objective evolutionary search is used to generate alternatives for complex planning problems, enabling the discovery of the key tradeoffs among planning objectives. RDM then determines the robustness of planning alternatives to deeply uncertain future conditions and facilitates decision makers'' selection of promising candidate solutions. MORDM tests each solution under the ensemble of future extreme states of the world (SOW). Interactive visual analytics are used to explore whether solutions of interest are robust to a wide range of plausible future conditions (i.e., assessment of their Pareto satisficing behavior in alternative SOW). Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly influence the cost-effectiveness, efficiency, and reliability of the robust alternatives. The framework is demonstrated using a case study that examines a single city''s water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. Results suggest that including robustness as a decision criterion can dramatically change the formulation of complex environmental management problems as well as the negotiated selection of candidate alternatives to implement. MORDM also allows decision makers to characterize the most important vulnerabilities for their systems, which should be the focus of ex post monitoring and identification of triggers for adaptive management. [Copyright &y& Elsevier]

Abstract: US railroad companies spend billions of dollars every year on track maintenance in order to ensure safety and operational efficiency. Optimizing the production team (i.e., large maintenance team) schedule is a very complex problem with major cost implications. In current practice, the decision making process for production team scheduling is largely manual and primarily relies on the knowledge and judgment of experts. This paper addressed the production team scheduling problem by formulating it as a time–space network model with many types of challenging side constraints. Some of these constraints are identified from industry practice and formulated for the first time. Multiple neighborhood search and other enhancement algorithms were proposed to solve the model. The proposed modeling approach has been tested through numerical experiments and also applied to large-scale real-world problem instances, and superior computational performances were found. The proposed approach has been adopted by a Class I railroad to help make annual network maintenance scheduling decisions. [Copyright &y& Elsevier]

In order to obtain efficiency and flexibility, assignment of machines' layout and determination of jobs' schedule on each machine are among the most important decisions. These decisions are interrelated and may impact each other but they are often treated separately or as a sequential decision in prior researches. In this paper, we propose a new approach to concurrently make the layout and scheduling decisions in a job shop environment. In other words, we consider an extension of the well-known job shop scheduling problem with transportation delay in which in addition to decisions made in the classic problem, the locations of machines have to be selected among possible sites. The only goal of the problem is the minimization of the makespan. A hybrid metaheuristic approach based on the scatter search algorithm is developed to tackle this problem. Using 43 randomly generated benchmark instances, the performance of the scatter search and its components are evaluated. We also applied our procedure to the classic job shop scheduling problems. Computational results show that our procedure is efficient. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study designed to compare the processes followed by practitioners of three design methods: the algorithm of inventive problem solving, axiomatic design, and environment-based design. Prior literature has postulated the complementary nature of these design methods, and in some cases, has provided case studies of their mutual application on a design problem. However, prior studies have not focused on the detailed activities used in each method to examine the similarities and differences in the outputs of the activities. In this study, a series of three one-day and three three-day design exercises were conducted simultaneously by three international research groups, each focusing on one method. The objectives of this study were to examine the early stages of the design process that deal with macro activities: problem analysis, problem synthesis, and design evaluation and decision making. Several micro design activities were conducted within these, depending on the design method: clarification of requirements, gathering information on existing technologies, initial conceptualization of an assembly of technologies, the identification of system contradictions/coupling, and the solution of contradictions. The objectives of this comparative study were to establish, from observations of practitioners - rather than from a theoretical point of view - the differences and complementarities between the design methods. The problems presented to designers covered a range of design tasks that spanned multiple disciplines, multiple levels of openendedness/specificity of the task, and various levels of inventiveness required. The comparison showed the complementary nature of the design methods, highlighted their respective strengths, and suggested the outlines of an integrated method based on the main benefit of each. [ABSTRACT FROM AUTHOR]

Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM. [ABSTRACT FROM AUTHOR]

Abstract: Railway dispatchers reschedule trains in real-time in order to limit the propagation of disturbances and to regulate traffic in their respective dispatching areas by minimizing the deviation from the off-line timetable. However, the decisions taken in one area may influence the quality and even the feasibility of train schedules in the other areas. Regional control centers coordinate the dispatchers’ work for multiple areas in order to regulate traffic at the global level and to avoid situations of global infeasibility. Differently from the dispatcher problem, the coordination activity of regional control centers is still underinvestigated, even if this activity is a key factor for effective traffic management. This paper studies the problem of coordinating several dispatchers with the objective of driving their behavior towards globally optimal solutions. With our model, a coordinator may impose constraints at the border of each dispatching area. Each dispatcher must then schedule trains in its area by producing a locally feasible solution compliant with the border constraints imposed by the coordinator. The problem faced by the coordinator is therefore a bilevel programming problem in which the variables controlled by the coordinator are the border constraints. We demonstrate that the coordinator problem can be solved to optimality with a branch and bound procedure. The coordination algorithm has been tested on a large real railway network in the Netherlands with busy traffic conditions. Our experimental results show that a proven optimal solution is frequently found for various network divisions within computation times compatible with real-time operations. [Copyright &y& Elsevier]

Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM--an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a exact solution algorithm to explicitly obtain the exact optimal solution of a second-order cone programming problem with box constraints of decision variables. The proposed solution algorithm is based on a parametric solution approach to determine the optimal strict region of parameters, and the main procedures are to perform deterministic equivalent transformations for the main problem and to solve the KKT condition of auxiliary problem without the loss of optimality. [ABSTRACT FROM AUTHOR]

Presenting a more efficient mathematical model for the problem of integrating lot sizing, loading, and scheduling in capacitated flexible flow shops with sequence-dependent setups is the main novelty of this paper. In comparison to the former model, fewer variables and constraints of the current model makes it easier to be solved. Comparison between this model and the former one shows the superiority of this model. To validate the new model, an efficient lower bound has been provided. Mixed integer programming-based algorithms have been used to solve this model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider sequential decision problems in which the decision at each time is taken as a convex-combination of observations and whose performance metric is multiplicatively compounded over time. Such sequential decision problems arise in gambling, investing and in a host of signal processing applications from statistical language modeling to mixed-modality multimedia signal processing. Using a competitive algorithm framework, we construct sequential strategies that asymptotically achieve the performance of the best piecewise-convex strategy that could have been chosen by observing the entire sequence of outcomes in advance. Using the notion of context-trees, a mixture approach is able to asymptotically achieve the performance of the best choice of both the partitioning of the space of past observations and convex strategies within each region, for every sequence of outcomes. This performance is achieved with linear complexity in the depth of the context-tree, per decision. For the application of sequential investment, we also investigate transaction costs incurred for each decision. An explicit algorithmic description and examples demonstrating the performance of the algorithms are given. Our methods can be used to sequentially combine probability distributions produced by different statistical language models used in speech recognition or natural language processing and by different modalities in multimedia signal processing. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a new generalization of the well-known Fréchet distance between two polygonal curves, and provide an efficient algorithm for computing it. The classical Fréchet distance between two polygonal curves corresponds to the maximum distance between two point objects that traverse the curves with arbitrary non-negative speeds. Here, we consider a problem instance in which the speed of traversal along each segment of the curves is restricted to be within a specified range. We provide an efficient algorithm that decides in time whether the Fréchet distance with speed limits between two polygonal curves is at most ε, where n is the number of segments in the curves, and is an input parameter. We then use our solution to this decision problem to find the exact Fréchet distance with speed limits in time. [Copyright &y& Elsevier]

Abstract: To tackle the problem of rough set on single-universe, we discuss the rough set model over dual-universes in aspect of building connection between single-universe model and dual-universes model. The rough set model over dual-universes denoted as RSMDU in this paper is built through inspecting the relation between the two universes. Firstly, we propose the RSMDU and study its property using character function and relation matrix. The algorithm for obtaining the lower and upper approximations is then presented. Secondly, we show that Pawlak rough set model can be induced using RSMDU. The theorem inferring the connection between Pawlak model induced by RSMDU and RSMDU is presented. Finally, the applications of RSMDU are studied. According to proposed model, we demonstrate that the existing models of rough set are special cases of RSMDU and that the set of conditional attribute and the set of decision attribute can be regarded as dual-universes in decision-making system, where the model can be utilized to handle decision processing. [Copyright &y& Elsevier]

Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what followup actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problemsolving process from problem definition, framework/model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified- Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making. [ABSTRACT FROM AUTHOR]

The aim of this paper is to apply TOPSIS method for negotiation support. The support we focus on concerns the pre-negotiation preparation and the process of negotiation template evaluation, which results in building a scoring system for the negotiation offers. Since the negotiation template may contain different types of criteria (negotiation issues), both quantitative (price, time) and qualitative (verbal description of warranty), the mechanisms of measuring distances for different types of data need to be incorporated into TOPSIS scoring procedure. We will use GDM (generalized distance measure) for interval and ordinal data. For weakly structured negotiation templates an alternative approach is proposed, one that does not use pair-wise comparisons of the evaluated alternatives. To illustrate the performance of TOPSIS in negotiation support we present a numerical example of business negotiations. [ABSTRACT FROM AUTHOR]

There are increasing challenges for optimally distributing fresh products while adequately considering the uncertain demand of customers and maintaining the freshness of products. Taking the nature of fresh products and the characteristics of urban logistics systems into consideration, this paper proposes an improved genetic algorithm for effectively solving this problem in a computationally efficient manner. Such an algorithm can adequately account for the uncertain demand of customers to select the optimal distribution route to ensure the freshness of the product while minimizing the total distribution cost. Iterative optimization procedures are utilized for determining the optimal route by reducing the complexity of the computation in the search for an optimal solution. An illustrative example is presented that shows the improved algorithm is more effective with respect to the distribution cost, the distribution efficiency, and the distribution system's reliability in optimally distributing fresh products. [ABSTRACT FROM AUTHOR]

In project development it is hardly possible to get exhaustive and accurate information. As a result, the situations occur, the consequences of which can be very damaging to the project. Inaccurate evaluation of the strategy related to capital investment and project implementation is one of the reasons why such estimates are not required in practice. Instead, a classification approach may be used for this purpose. Classification is a very important aspect of decision making. In the present paper, a novel algorithm CLARA is offered for ordering multicriteria alternatives. It differs from the existing similar methods in the wider range of application allowing it to be used with various scales of criteria evaluation, a random number of the solution classes, incomplete order on the criteria scales as well as in the considerably rarefied space of the alternatives. The suggested algorithm is more effective in terms of the time spent by an expert. A comparative calculation of the efficiency of the algorithms used in classifying the objects in the order of their significance has shown that CLARA is much more effective than CLANSH and other algorithms. At the same time, its general effectiveness was found to be lower than that of the algorithms CYCLE which has a narrower scope of application. [ABSTRACT FROM AUTHOR]

Purpose - This paper sets out to propose a new cut-off optimization algorithm for effective decision making at the open pit mine planning stage. Design/methodology/approach - The determination of optimum cut-off grade to maximize the net present value of an open pit mining operation is influenced by the economic parameters including metal price and operating costs, the capacities of mine, mill, and refining stages, and grade distribution of the mineral deposit. The market plays a vital role in changing the economic parameters; therefore, they may escalate during mine life. The effect of these changes could be enormous on optimum cut-off grade policy. The main motive is to introduce economic parameters escalation into the established theory of optimum cut-off grades and study the impact of these changes on overall economics of the operation. Therefore, a cut-off grade optimization algorithm is developed, which considers dynamic metal price and cost escalation during mine life. Findings - A copper deposit case study shows that, keeping the metal price escalation at a minimum, the impact of mining and milling costs escalation is relatively higher than refining and administrative costs. Hence, a high-escalation rate in mining and milling costs may change an economic operation into an uneconomic scenario. Research limitations/implications - Management of stockpiles as a policy may be introduced in the algorithm for improvement in economy through maximum utilization of mineral resources. Originality/value - The algorithm due to its flexibility allows analysis of various options in the least possible time, which makes it valuable to mine planners in decision making for major mining investments. [ABSTRACT FROM AUTHOR]

Agreement is at the heart of distributed computing. In its simple form, it requires a set of processes to decide on a common value out of the values they propose. The time-complexity of distributed agreement problems is generally measured in terms of the number of communication rounds needed to achieve a global decision, i.e., for all nonfaulty (correct) processes to reach a decision. This paper studies the time-complexity of local decisions in agreement problems, which we define as the number of communication rounds needed for at least one correct process to decide. We explore bounds for early local decision that depend on the number f of actual failures (that occur in a given run of an algorithm), out of the maximum number t of failures tolerated (by the algorithm). We first consider the synchronous message-passing model where we give tight local decision bounds for three variants of agreement: consensus, uniform consensus, and (nonblocking) atomic commit. We use these results to (1) show that, for consensus, local decision bounds are not compatible with global decision bounds (roughly speaking, they cannot be reached by the same algorithm), and (2) draw the first sharp line between the time-complexity of uniform consensus and atomic commit. Then we consider the eventually synchronous model, where we give tight local decision bounds for synchronous runs of uniform consensus. (In this model, consensus and uniform consensus are similar, atomic commit is impossible, and one cannot bound the number of rounds to reach a decision in nonsynchronous runs of consensus algorithms.) We prove a counterintuitive result that the early local decision bound is the same as the early global decision bound. We also give a matching early deciding consensus algorithm that is significantly better than previous eventually synchronous consensus algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews recent developments in evolutionary algorithms and visualization in the context of multiobjective spatial decision making. A synthetic perspective is employed to bridge these two areas and to create a unified conceptual framework that can be used to address a broad range of multiobjective spatial decision problems. In this framework, evolutionary algorithms are employed to generate optimal, or near-optimal, solutions to a problem being addressed. Alternatives created are then displayed in an interactive visual support system that can be used by decision makers to discover the competing nature of multiple objectives and to gain knowledge about the tradeoffs among alternatives. [Copyright &y& Elsevier]

Abstract: Ranking technology suppliers is an important area to which insufficient attention has been paid by researchers. Present models of technology suppliers ranking are all based on both buyer’s perspective as well as on complete discretionary of decision making factors. The objective of this paper is to propose an innovative algorithm for ranking technology suppliers in the presence of nondiscretionary factors from supplier’s perspective. A numerical example demonstrates the application of the proposed method. [Copyright &y& Elsevier]