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Multi-choice goal programming (MCGP) has been widely used to find satisfying solutions for multiple criteria/objective decision problems in which the target value of “the more, the better,” or “the less, the better” can easily be obtained. This paper proposes two new models for representing the triangular and trapezoidal membership functions, which improve the efficacy of fuzzy MCGP (FMCGP). Two real-world applications are provided in this study to demonstrate the usefulness of the proposed models. Furthermore, the same problems are resolved by using the proposed nature-inspired optimization method (NIOM) to find the differences between them. While the artificial bee colony (ABC) algorithm is a well-known NIOM technique, studies have shown that it has an excellent performance with high-quality solutions. Thus, this study initially uses the ABC algorithm to find the differences between MCGP and ABC. Finally, some insightful information is obtained from the comparison to contribute to the NIOM and MCGP fields and their respective applications.

Hub covering location problem focuses on the construction of the hub network. The importance of this problem has been addressed in many fields including freight transportation and telecommunication network. As a long-term strategic decision problem, the construction of the hub network has large scale environmental influences especially the emissions of greenhouse gas. In the meanwhile, the precise information of the parameters cannot be obtained. Therefore, this work investigates the sustainable hub covering location problem with uncertain parameters. The purpose is to minimize the overall cost and impose restrictions on the total CO2 emissions, simultaneously. The travel times are depicted by uncertain variables to portray inherent indeterminacy within this problem. The incomplete network is adopted to decrease the total investment cost considerably. Accordingly, two uncertain programming models are constructed and transformed into their equivalent forms. A modified genetic algorithm is proposed to solve the proposed models. Several experiments are performed to interpret the efficiency of the proposed algorithm and the validity of the proposed models. The experimental results show that accounting for the emission restriction and the adoption of incomplete hub network will significantly affect the overall investment cost and the construction of the hub network.

In the current work, we devised a hybrid method involving a Double-Layer Neural Network (DLNN) for solving a quadratic Bi-Level Programming Problem (BLPP). For an efficient and effective solution of such problems, the proposed potential methodology includes an improved Artificial Bee Colony (ABC) algorithm, a Hopfield Network (HN), and a Boltzmann Machine (BM). The improved ABC algorithm accommodates upper-level decision problems by selecting a set of potential solutions from all combinations of solutions. However, for lower-level decision problem, HN and BM are amalgamated to manifest a DLNN that initially generates its structure by choosing a limited number of units, and will subsequently converge to an optimal solution/unit among those units and hence, constitutes an effective, efficient solution technique.We compared the accuracy, computational time and effectiveness (ability to find the true optimum) of the proposed DLNN with improved-ABC, DLNN with PSO (where PSO replaces the improved-ABC in the upper-level problem of the proposed DLNN with improved-ABC), DLNN with GA (where GAreplaces the improved-ABC in the upper-level of the proposed algorithm) and other conventional approaches and found the proposed DLNN with improved-ABC can yield high quality global optimal solutions with higher accuracy in relatively smaller time.

One of the reason that hinders ridesharing from acceptance by general public is due to the safety concern to share a vehicle. Recently, the problem to meeting trust requirements in ridesharing systems has been formulated. As this problem is a complex discrete decision problem, it can be solved by metaheuristic algorithms. In the literature, one solution algorithm based on Particle Swarm Optimization (PSO) and another solution algorithm based on Firefly algorithm were developed to solve this problem. The goal of this study is to develop another solution algorithm based on Differential Evolution (DE) approach and compare the proposed algorithm with PSO and Firefly algorithms. Several mutation strategies in DE approach have been implement to solve the optimization problems. We evaluate the DE based algorithms by conducting experiments. We analyze the results to illustrate effectiveness of the DE based algorithms.

Terminals in heterogeneous wireless networks perform horizontal handovers in a homogeneous network and vertical handovers between different types of networks. The decision of access location depends on not only the physical layer parameters, such as signal quality, hut also other attributes as communication cost, handover delay, and so on. If the vertical handover decision is classified into a multi-attribute decision problem, the difficulty of solving the attribute weights increases, with the increasing number of decision attributes. Moreover, it is more subjective to compare the relative importance of two attributes. To solve this problem, this paper proposes a vertical handoff decision algorithm in heterogeneous wireless networks, which is based on an improved KNN with less sample data and lower complexity. All decisive attributes are treated as data items and the different access locations are mapped into unique tags. The decision of terminal is determined by the distance improved. The simulation results shows that algorithm proposed in this paper can improve the throughput, reduce drop rate, which improves the resource utilization of the overall system.

As a key technology, autonomous maneuver decision for unmanned combat air vehicle (UCAV) is a hot topic. In order to better solve the maneuver decision problem, the maneuver decision model is considered as a rolling horizon control problem. In the rolling horizon optimization scheme, a novel evolutionary algorithm is proposed to solve maneuver decision problem per step. Meanwhile, four factor membership functions are designed to build objective advantage function. The simulation results for three typical scenarios illustrate the efficiency and robustness of autonomous maneuver decision method proposed.

This paper investigates the policy optimization paradigm, where a learning model is trained to find the solution of complex Markov decision problems, as a tool to address the berth allocation problem in multimodal terminals. To this purpose, we drop the typical formulation of the latter as a mixed-integer static scheduling one, and we model it instead as an evolving scenario in which berths are assigned to ships according to a parameterized policy function that drives the temporal evolution of the environment. We adopt a cross-entropy optimization scheme to optimize the policy parameters, which is a simple and highly parallelizable gradient-free technique. As compared to the static mixed-integer formulation, the proposed approach relies on a much lighter optimization problem in the continuous space of the policy parameters, thus making it feasible to replan in real time when needed. Furthermore, the generality of the policy optimization approach allows to take into account any performance metric and specific feature of the scenario straightforwardly, without the need to devise ad hoc heuristics. Simulation tests showcase the good performance of the policy approach under various conditions.

Entity Linking (EL) is the task of mapping mentions in texts to the corresponding entities in knowledge bases. Existing studies mostly focus on joint disambiguation based on the topical coherence, including graph and sequence models. Sequence models alleviate the complexity caused by graph models, but exist the error propagation that incorrectly disambiguated entities are likely to induce further errors when predicting future mentions. Moreover, it is a huge expense to construct the relationship between entities to explore structured knowledge. To address these problems, we propose a novel method, Knowledge Enhanced Sequential Entity Linking (KESEL), which converts global EL into a sequence decision problem and applies a pre-trained language model to better fuse entity knowledge. Specifically, we firstly utilize multiple features to learn local contextual representations of mentions and candidates respectively. Next, a sequential ERNIE model is introduced to generate knowledgeable representations by dynamically integrating the knowledge of previously referred entities into subsequent mentions disambiguation. Finally, by concatenating the above learned contextual and knowledgeable representations, we make full use of multi-semantic information to improve the performance of EL. Extensive experiments show that our method can achieve competitive or state-of-the-art results.

Aiming at the order acceptance decision problem of modular production enterprises, a semi Markov decision process-based order acceptance decision model is constructed. The enterprise's production capacity is divided into four independent production units, and each production unit has its daily capacity and total capacity limit. The deep reinforcement learning algorithm is used to explore and learn in the model to maximize the overall revenue when the production capacity and order delivery time are limited. At the same time, it also solves the problem that the state space is complex that the traditional Q-learning algorithm is not competent. The strategy's effectiveness is verified by comparing the trained strategy with First-Come-First-Serve(FCFS) strategy in the same environment.

In this paper, we concentrate on study interval-valued fuzzy relations in decision problems based on preference relations, and a preference structure making up of the strict preference relation, indifference relation, and incomparability relation which may be defined with the use of interval-valued aggregation and interval-valued fuzzy negation function. We analyze the influence of some new types of fusion functions on the effectiveness of the decision process. The studies concern different aggregation classes due to the type of monotonicity/order used.

With the development of 5G technology and the proliferation of various mobile applications, mobile edge computing (MEC) provides services near the user side to meet the quality constraints of different tasks. Most current works focus on the offloading decision and resource allocation issues in MEC. However, few works focus on user mobility and the personalized preferences of different applications. In this paper, we study these issues and propose a deep reinforcement learning (DRL) based context-aware online offloading strategy. To further reduce the overhead caused by user mobility for task offloading and migration, we consider the user’s future movement trajectory and calculate the potential migration cost. Considering the dynamic network environment and the incompleteness of the observed system state information, we formulate the offloading decision problem as a partially observable Markov decision process (POMDP) problem, and then devise an efficient DRL algorithm to speed it up. We use EdgeCloudSim tool and Geolife trajectory to simulate the task offloading decision problem. The simulation results show that the proposed strategy is superior to other baseline strategies in terms of the total cost, delay, energy consumption, migration cost, and can be well adapted to different preferences and the dynamic network environment.

In this paper, the application of Support Vector Machine (SVM) on multiple car crash situations for improved decision of saftey applications, e.g. airbag control systems is presented. In general, we should argue from a different perspective: we don’t want to show the latest and greatest Machine Learing (ML) results. The intention of the paper is to show how state of the art products use ML for safety critical applications. It is the goal to avoid the deployment of an airbag. Today, saftey applications depend on various detection algorithms and sensor systems. The challenge for these kind of decision problems depend on crucial constraints named decision time and sensor signals which are complicated to distinguish. The system has to decide whether to fire or not to fire an airbag within a few milliseconds. We use a (Multiclass) Support Vector Machine to account for an improved classification with the given constraints. Various multiclass classification methods are rated and the two methods One-Versus-Rest and One-Versus-One are benchmarked in terms of quantities as test error, training time, memory consumption and misclassified crashes. All methods are applied to real measurement data of car crashes for the type of full frontal crashes in various conditions. We will show that One-Versus-One performs best. The method is able to classify car crash situations and improve the detection possibility. This allows for active and passive occupant safety components in the automotive area.

Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of generating proposals and inflexibility in determining fine-grained boundaries, and the latter ones usually attempt to decide the start and end timestamps directly, which brings about much difficulty and inaccuracy. In this paper, we convert this task into a multi-step decision problem and propose a novel Cascaded Prediction Network (CPN) to generate the grounding result in a coarse-to-fine manner. Concretely, we first encode video and query into the same latent space and fuse them into integrated representations. Afterwards, we construct a segment-tree-based structure and make predictions via decision navigation and signal decomposition in a cascaded way. We evaluate our proposed method on three large-scale publicly available benchmarks, namely ActivityNet Caption, Charades-STA and TACoS, where our CPN surpasses the performance of the state-of-the-art methods.

The COVID-19 pandemic requires social distancing to prevent transmission of the virus. Monitoring social distancing is difficult and expensive, especially in "travel corridors" such as elevators and commercial spaces. This paper describes a low-cost and non-intrusive method to monitor social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behaviour of CSI with a cubic SVM classifier, we count the number of people in an elevator with an accuracy of 92%, and count the occupancy of an office to 97%. As opposed to using a multi-class counting approach, this paper aggregates CSI for the occupancies below and above a COVID-Safe limit. We show that this binary classification approach to the COVID safe decision problem has similar or better accuracy outcomes with much lower computational complexity, allowing for real-world implementation on IoT embedded devices. Robustness and scalability is demonstrated through experimental validation in practical scenarios with varying occupants, different environment settings and interference from other WiFi devices.

In this paper, optimal control decision problems are discussed for a class of dynamic advertising models with lagged effect. Here, the dynamic model with input delay is introduced to depict the impact of the lagged effect on the development of goodwill. The purpose of the addressed problem is to utilize optimal control theory such that the performance of the channel system is maximized while achieving optimal strategy level. Specifically, by utilizing the maximum principle, optimal control decisions of channel members are obtained in the integrated and decentralized systems. Our results demonstrate whether two members benefit from the integrated system is closely related to the delay time of the advertising effect. Also, the contribution of these results is to offer a reference for making optimal control decision and the choice of decision mechanism in the channel system. Finally, an illustrative example is performed to illustrate the effectiveness of the proposed results.

As the number of participants involves in decisions getting complex and the heterogeneity could be produced among decision makers, a large-scale group decision making (LGDM) method with consensus constructing need to be considered. In order to demonstrate the complex relationship and reduce heterogeneity among decision makers, a consensus process of LGDM is proposed in this paper, in which multi-granular probabilistic fuzzy linguistic preference relations (MGPFLPRs) are used to represent sub-group's preferences information. First, mathematical programming is proposed to deal with MGPFLPR based on expected multiplicative consistency and obtain the priority weight vector. Second, collective priority weights of alternative are obtained by fusing sub-group's priority weights of alternative based on the weighted averaging operator. Then, an automatic iteration consensus reaching algorithm is implemented for the purpose of reaching a consensus in LGDM with MGPFLPRs. Finally, an emergency decision problem is applied to demonstrate the effectiveness of the proposed method.

Overtaking decision under dynamic environment is one of the key research directions. Traditional methods are difficult to solve such a complex optimization decision-making problem. In recent years, reinforcement learning algorithm is developing continuously, which can be applied to solve the overtaking decision problem of unmanned vehicles. Reinforcement learning generates new data through the continuous interaction between agent and environment, and feeds back to the agent for corresponding learning behavior, so as to improve its own behavior strategy. In this paper, Deep Q-learning (DQN) is used to solve the overtaking decision-making problem on one-way through two lanes. A one-way through two lane traffic environment is built in Python to train and test the algorithm.

The computing architecture undergoes a trend toward heterogeneity, which introduces a new challenge of task offloading scheduling in multi-server mobile edge computing system. Considering the inherent heterogeneity of task parallelism, this paper investigates the interesting problem of how to exploit the parallelism of task and server by reasonably scheduling task offloading. We jointly optimize task offloading decision and resource allocation so as to minimize system-wide computation overhead in terms of task completion time and energy consumption. Leveraging the structure of the formulated problem, we decompose the original problem into task offloading decision problem and resource allocation problem. To solve for task offloading decision, we propose a heuristic algorithm based on the designed user preference, while we address resource allocation problem by quasiconvex and convex optimization techniques. Simulation results demonstrate that our proposed algorithm outperforms the existed benchmark approach, and that prominent gains can be achieved by conditionally deploying heterogeneous server.

In this paper, we present a language developed for describing group decision-making among agents. This includes the use of interaction protocols to govern agent interactions towards global decision goals, and rules govern human-agent interactions towards local decision tasks. The Contract-net protocol is used as an example, and a prototype system is implemented to demonstrate the application of the language. The multi-dimensional adaptivity of the method in solving group decision problems is discussed.

In order to overcome the credit decision problem of MSMEs, a novel model is constructed in this paper. The model is divided into three main categories, which are comprehensive evaluation model, cluster analysis model and hierarchical analysis model. In addition, the model uses MATLAB algorithms to obtain the optimal credit strategy. Based on this, the model also performs quantitative analysis of credit risk, measuring credit risk in terms of both reputation and strength, and finally obtains a comprehensive ranking of reputation and strength, i.e. the ranking of credit risk. In addition, for the determination of the credit strategy, this paper adopts the comprehensive evaluation method, taking the quality degree of the enterprise (from the bank’s perspective) as the evaluation object, establishing a rating index system with annual yield, reputation, sales and gross profit rate as the evaluation indicators, and using the weighted average method to establish a comprehensive evaluation model. Considering the large amount of data, Excel was used to process the data. , and MATLAB was used to obtain the final research results. The results of the study showed that the enterprises considering the loans were mainly divided into five grades, with the ratio of the actual loan amount to the expected loan amount in each grade ranging from [85%, 90%], [75%, 80%], [70%, 75%], [65%, 70%] and [60%, 65%] intervals.

The concealment of submarine navigation is one of the important considerations to measure the safety of submarine navigation, especially over long distances, there are many key links and many factors to be considered, and many factors determine whether a submarine is safe or not. The safety of a submarine can be measured by the submarine's speed, dive depth, and distance from the enemy submarine. In this study, the evaluation of submarine concealment conditions is considered as a multi-objective decision problem, and a multi-level fuzzy integrated evaluation method is applied to study it. Combined with the engineering application background, the simulation experiment is established. The experimental results show that the multi-level fuzzy evaluation system combined with the simulated annealing algorithm is very effective.

The advantage of flexible structure makes it possible for reconfigurable manufacturing systems (RMS) to respond to business opportunities. One important issue is to form production process in RMS to meet the demand based on available resources. To form a process, it is required to assign manufacturing resources to carry out the operations in it efficiently. The decision problem is to find a configuration using the available resources. Recently, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms have been developed to optimize processes. In this paper, another two mutation strategies are developed for binary DE. Performance of these algorithms to form production processes will be compared.

In a group decision making problem, the group decision as a consensus of the members is usually more emphasized than the individual decisions in the group. However, in our public or private life, we often make decisions individually with others' help. In other words, the individual decision is not always completely independent of the others but affected by the others to some extend. This paper proposes the approach to derive the individual decision from a group of individual judgments. We consider two viewpoints: the preciseness of the obtained individual decision to the corresponding given judgment and the similarity of the individual decisions. The latter is an intersection of the individual decisions, and it is positive and negative when there is a common of all and not, respectively. We formulate the problem to obtain the individual decisions from a group of individual judgments as an LP problem with two degrees of preciseness and similarity. Both degrees are given so as independently to be suitable for the decision maker's preference and the decision problem. Since they have a trade-off relation, there is a Pareto optimal state. The obtained individual decision is supported by the others and is a more suitable one to the situation among the possible decisions derived from the intuitive individual judgment.

The Isabelle/HOL proof assistant provides quite advanced and reliable support for discharging proof goals with external SMT solvers by reconstructing the resulting proof tree within the Isabelle/Pure inference kernel. In particular, currently Isabelle fully and efficiently supports proof reconstruction for the quantifier-free fragments of the theories of equality and uninterpreted functions (QF_UF) and linear integer arithmetic (QF_LIA). Yet there are a number of practically relevant theories that can be decided using relatively efficient algorithms, but are not supported either by the Isabelle proof reconstruction procedure or by the SMT solvers themselves. Meanwhile, if the decision procedure for an extended logical theory can be expressed as a complete quantifier instantiation strategy that reduces the given decision problem into the supported logical fragment (QF_UFLIA), the theory can be fully supported within the existing Isabelle/HOL infrastructure with relatively little additional effort. The goal of our work was to develop a fully formal completeness proof for an efficient quantifier instantiation strategy that translates a satisfiability problem in the logic of bounded linear integer arithmetic (we call it QF_BLIA) into the equisatisfiable problem in the logic of linear integer arithmetic and uninterpreted functions (QF_UFLIA). In this paper we first give the initial informal presentation of the proof, then discuss the most relevant issues of our resulting corresponding formal development of the completeness proof within Isabelle/HOL. The resulting formal proofs tightly follow the provided informal presentation, but at the same time they also contain quite significant amount of auxiliary developments that can potentially be generalized for other similar proof formalizations and automated using theory generation facilities of Isabelle/ML.

The new video coding algorithm H.265 achieved twice compression rate than the prevailing H.264, but the encoding speed is reduced several times, making it difficult for the real-time applications such as video surveillance and remote meeting. In this paper, we design a parallel intra-frame mode decision architecture based on FPGA to accelerate the coding. We prove that the mode decision problem of multi-level large coding unit of H.265 can be transformed into the accumulation of multiple single-level small coding unit mode decision. Furthermore, we design how mode decision is accelerated by fine-grained parallelism within coding unit, and pipelining among coding units, then implement them on FPGA. The experimental results show that the proposed architecture achieves maximum $93.6\times $ acceleration compare with the ARM A53 platform, with an insignificant loss of video quality.

Swarm Intelligence is about emergency of collective intelligence from groups of homogeneous robotic devices deployed for a purpose. Ant Colony Systems, in particular, are inspiring. They commonly have drawn inspiration from the behaviors of real ants in nature in order to construct routes between the food sources and the nest. There are still gaps in alternative options for path decision in ant agents. Bellman's equation has been successfully used to solve path decision problems in machine learning. We proposed to investigate impact of a Bellman's equation inspired algorithm for path decision on stigmergic ant agent robotic devices. A design science research paradigm was used to design our research experiment in which a simulated environment was designed to simulate the behavior of ant agents when using a Bellman's equation inspired algorithm for path decision. We introduced a reward function to the orientation process of ant agents. Reward function rewards a decision made when an ant moves from one point to an adjacent cell. The Bellman's inspired algorithm for ant orientation led to convergence of ant agents even though there was reduced quality of convergence. Evaluation of results show that Bellman's equation can be used in path decision processes for ant agent robotic devices. Our results contributed to adding an alternative way of implementing path decision for ant agents. This will help in growing the knowledge around ant agents and finding better ways to implementing path decisions for ant agents.

For the multiple criteria decision problem in the fuzzy environment of Pythagorean theorem, the existing set operators seldom consider the relation between criterions, and choose optimal alternative only through a single measure when the alternatives are sorted. In order to solve the problems, the Bonferroni operator is extended to the Pythagorean fuzzy set (PFS) to provide the Pythagorean fuzzy Bonferroni method, and the alternatives is ranked by the synthetic measure. In this paper, firstly, triangular fuzzy numbers(TFNs) are used to represent parameter values, and then the Pythagorean triangular fuzzy interaction geometric Bonferroni mean(PTFIGBM) operator and the Pythagorean triangular fuzzy interaction weighted geometric Bonferroni mean (PTFIWGBM) operator of Pythagorean TFNs environment are proposed. Some special cases of the proposed new aggregation operators are studied. A multi-criteria decision making method based on PTFIGBM operator and PTFIWGBM operator is developed. The distance measure in the Pythagorean TFNs environment is innovatively proposed in this paper, and the score function and the distance measure are aggregated into a comprehensive measure to sort the alternatives. Finally, a numerical example is given to illustrate the new method, and some parameters are analyzed and compared with other methods to further demonstrate the advantages of the proposed new method.

Supporting the objective of reaching consensus is a notable research area in the context of group decision making. Recently, several approaches based on an allocation of information granularity have been proposed for supporting it. Those approaches improve the consensus among the individuals participating in the decision problem by means of the required flexibility provided by the information granularity level. However, they do not take into account that the adjusted preferences could be very different from the preferences communicated by the individuals. Furthermore, they cannot be applied in decision problems where various criteria are considered to evaluate the choices. To overcome these shortcomings, we introduce a new consensus approach based on minimum adjustment for multi-criteria group decision making with an allocation of information granularity. An experimental study is also provided to illustrate this consensus approach and analyze its performance.

One of the key challenges to AI is autonomous Decision Making (DM). This paper strives towards a formal approach to model cognitive DM in order to enable AI machines to conduct autonomous DM. On the basis of the theoretical models for rational decisions and rigorous DM developed in our Lab, we implement a set of algorithms for autonomously handling primitive, single, and serial decisions rigorously. An autonomous DM supporting tool is designed and implemented based on the algorithms. Experiments on various decision problems have demonstrated the capability of the tool and the methodology in many cases beyond those of human DM outcomes in both complexity and speed.

This paper discusses an uncertain loaning decision problem for electric vehicles in the blockchain Internet of energy. Blockchain guarantees the security and privacy of transactions. In view of the uncertainty of electricity price, the scenario method is introduced. A new Stackelberg game model between multi plug-in hybrid electric vehicles and a loan bank is established. For each player, through the optimal solution of the loaning decision problem, the decision of each electric vehicle and the bank is obtained. Differing from the deterministic decision problem, a robust decision problem is proposed for the uncertainty of electricity price. In particular, in the loaning decision for each plug-in hybrid vehicle, a two scenario subset robust optimization method is used. A relaxation algorithm is proposed to solve the robust decision problem. The equilibrium of robust Stackelberg game model is achieved at the optimal loaning amount of all electric vehicles and the optimal loaning rate of the bank. Finally, an experiment was conducted to test the established game model in three different instances. The effectiveness and advantages of the established game model were verified by the computational results.

One efficient approach to control chip-wide thermal distribution in multi-core systems is the optimization of online assignments of tasks to processing cores. Online task assignment, however, faces several uncertainties in real-world systems and does not show a deterministic nature. In this paper, we consider the operation of a thermal-aware task scheduler, dispatching tasks from an arrival queue as well as setting the voltage and frequency of the processing cores to optimize the mean temperature margin of the entire chip (i.e., cores as well as the NoC routers). We model the decision process of the task scheduler as a semi-Markov decision problem (SMDP). Then, to solve the formulated SMDP, we propose a reinforcement learning algorithm that is capable of computing the optimal task assignment policy without requiring the statistical knowledge of the stochastic dynamics underlying the system states. The proposed algorithm also relies on function approximation techniques to handle the infinite length of the task queue as well as the continuous nature of temperature readings. Compared to related research, the simulation results show nearly 6 centigrade reduction in system average peak temperature and 66 milliseconds decrease in mean task service time.

In this paper, we propose an adaptive dynamic programming algorithm based on policy iteration to solve the two-player nonlinear Stackelberg differential game. Stackelberg differential game is a hierarchical decision problem that allows the leader to choose its own optimal decision by predicting the follower’s response to its decision. In this paper, Stackelberg feedback equilibrium solution is obtained by solving the coupled partial differential equations to ensure the stability of the system. A new strategy iterative algorithm for Stackelberg differential game problem is proposed. The effectiveness of the algorithm is illustrated by an example.

This paper considers a Mobile Edge Computing scenario with multiple mobile units (MUs), multiple access points (APs) and one cloudlet server. The MUs have to decide whether offloading their computation tasks to the cloudlet is energy wise beneficial. As there are multiple APs available to connect the MUs to the cloudlet and communication and computation resources have to be shared among all MUs, each MU also has to choose the AP for transmission that minimizes its offloading energy under the given fraction of the overall resources. The problem is formulated as a energy minimization problem with a maximum offloading time constraint. MUs not only need to consider the energy required for local computation or offloading, but simultaneously avoid an overlong processing time of offloaded computation. This joint offloading decision and resource allocation is divided into two subproblems in the proposed approach. The resource allocation problem is reformulated by using Lagrange multipliers and closed-forms for the calculation of the shared resources are found. These results can be integrated into the proposed game theoretic algorithm for the offloading decision problem. The algorithm is based on a potential game and therefore, can be proven to converge to a Nash equilibrium. Numerical results show a benefit of the proposed resource allocation strategy, a performance of the proposed game algorithm near the optimal solution and a fast algorithm execution time that can even be significantly improved by proposed sorting metrics. mobile edge computing, joint optimization, resource allocation strategy, game theory

International audience; In this paper we propose methods that can help the decision-makers to find a compromise between willingness to do and ability to do by introducing the difficulty considerations in the multiple criteria decision analysis problems. Two problems are considered: ranking alternatives and improving existing solution. Usually, in the classical approaches of multiple criteria decision analysis, only the degree of satisfaction is considered to compare alternatives. However, sometimes a good alternative is difficult to implement by a decision-maker even if he spends necessary cost for it. First we give the definition of the concept of difficulty function, then we show how to introduce it in decision problems using operators based on fuzzy measures. This allows us to consider interactions between criteria under two aspects: 1) the overall satisfaction resulting from the simultaneous satisfaction or not of certain criteria; 2) the overall difficulty resulting from the difficulty or not of satisfying certain criteria simultaneously. After that, we present two examples of the difficulty function assessment in the case of a non-linear model. Finally, we propose an illustration concerning the problem of managing the students effort when improving their scores on a set of subjects. This illustration focus on the extension of the concept of worth index which quantifies the gain of improvement related to a subset of objectives when it is difficult to improve all the objectives simultaneously.

In Multi-Criteria Decision Making, one of the most used algorithm designed to deal with decision making is the Technical Order by Preference to Ideal Solution (TOPSIS), which is based on finding a solution that is close to the best possible solution and distant from the worst possible solution. The Group Modular Choquet Random TOPSIS (GMC-RTOPSIS) is a generalization of the TOPSIS method capable of dealing with multiple and heterogeneous data types and interaction among criteria by means of the discrete Choquet integral. On the other hand, CT-integrals are a generalization of the Choquet integral using t-norms, which are more flexible than the standard Choquet integral. CT-integrals are pre-aggregation functions, which means that we do not require them to be monotonic in the whole domain, just in some specific directions, that is, they are directionally monotonic. Due to the excellent performance of CT-integrals in classification and multimodal fuzzy fusion decision problems, the objective of this paper is to generalize the GMC-RTOPSIS by using CT-integrals and to analyze the results provided by the use of five different t-norms in an example of a decision making problem.

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the staleness of a sample is conservative and therefore, introduce \textit{age penalty} for this purpose. We study two optimization problems: minimize average age penalty subject to an average sampling frequency constraint, and minimize average sampling frequency subject to an average age penalty constraint; both are Constrained Markov Decision Problems. We solve them using linear programming approach and compute Markov policies that are optimal among all causal policies. Our numerical results demonstrate that the computed Markov policies not only outperform optimal periodic sampling policies, but also achieve sampling frequencies close to or lower than that of an optimal clairvoyant (non-causal) sampling policy, if a small age penalty is allowed., 6 pages, published in IEEE INFOCOM AoI Workshop 2020

We study an extension of the Artifical Hormone System (AHS). The AHS is a decentralized task allocation system which has self-organizing properties. We extend the AHS by negator hormones where a task sending such a negator suppresses the execution of other tasks. We can realize conditional task structures in this way and use them to execute a task set on heterogeneous processors: If high-performance processors are available, high-performance tasks can be instantiated by the AHS. If the high-performance processors are failing, another task set running on general purpose processors is allocated (but with lower performance as before). We evaluate the negator approach by realizing two different control strategies for a self-balancing vehicle.The introduction of negators increases the complexity of deciding whether a communication path leading along several different tasks reaches its target eventually. We call this decision problem NEGATOR-PATH and prove its NP-completeness.

This document presents Analytical Hierarchy Process (AHP), and Geographic Information Systems (GIS) as a decision support tool for household and similar waste management. A set of management plans and impact categories create a challenging decision-making framework for waste management in Marrakech Prefecture. GIS has been used to enrich this study by exploring parameters involved in decision making, and to visualize inaccessible sites.AHP is used to integrate the relative importance of different impact categories and subcategories into the decision-making scheme. The results of AHP application show that among impact categories presented, technical feasibility and economic aspect are higher ranked than environmental impact, political support and social involvement. In a broader context, the use of AHP to structure the decision problem allowed the development of four different plans for waste management. In this analysis, Plan IV has been considered the most suitable plan to follow.

This paper considers the compound channel where the actual channel realization is unknown. It is only known that it comes from a given uncertainty set and that it remains constant throughout the entire duration of transmission. The capacity has been established providing a complete characterization and a simple formula for the computation of the maximal transmission rate. This is no longer the case for the ϵ-capacity of a compound channel, which characterizes the maximum transmission rate when a non-vanishing average error ϵ is tolerated. In this case, the compound channel is known to have no strong converse under the average error criterion and, therewith, the ϵ-capacity may be larger than the capacity for a vanishing error. As the capacity of compound channels is unknown, Ahlswede raised the question of whether or not there exists a (simple) recursive formula for it. This paper approaches this question from a fundamental, algorithmic point of view by studying whether or not such formulas can be found algorithmically in principle (without putting any constraints on the computational complexity of the algorithms). To this end, it is shown that there exists no algorithm or Turing machine that takes the compound channel and error as inputs and computes the corresponding ϵ-capacity. Accordingly, there is also no recursive formula for the ϵ-capacity providing a negative answer to Ahlswede’s initial question. The developed framework is subsequently applied to the question of the existence of a strong converse, the existence of an optimal second order coding rate, and whether or not the pessimistic and optimistic definitions of the ϵ-capacity coincide. Cast as decision problems, it is shown that these questions are undecidable and therewith impossible to be answered algorithmically.

This paper studies the production planning problem through the linear programming method. The study examines that it is an efficient method of operation research in the field of industrial engineering. Using the data of a Chinese company Z, which is a famous food company, the study analyzes the empirical results and obtains the following evidences. Firstly, the linear programming theory is very consistent with the actual production situation in China. Secondly, the real data of the company Z is used to derive the gross margin of one-unit product especially via the opportunity cost principle. Finally, one-period decision problem can be easily solved by the linear programming method and the results are reasonable and robust. In addition, the multi-period decision problem can be easily extended from one-period decision problem.

Dissolved gas analysis (DGA) is considered one of the simplest and most widely used tools to diagnose incipient faults of power transformers. Several methods have been proposed for DGA in power transformers, such as Roger’s ratio method, IEC method and Duval Triangle method. However, these methods suffer from the low accuracy or no decision problems. Accordingly, in this paper, a combined method is designed and built using MATLAB/SIMULINK to construct enhanced DGA diagnostics of transformer faults. This method will be referred as Decision Transformer Fault Diagnostics (DTFD) method. DTFD method is built based on the conventional methods, but, with a combined framework in order to give an enhanced accuracy. First, each individual method is built separately with an output representing the fault in a matrix form. Then, the outputs of all individual methods are used as the input to DTFD system. Through this method, the fault states of each matrix are compared giving a combined matrix. The fault state that has the highest number within the matrix represents the dominant fault. 386 dataset samples are used to validate the proposed method. A comparison of DTFD method with conventional ones indicated that the proposed method has the highest classification.

Digital microfluidic biochips (DMFB) are a part of lab-on-a-chip (LOC) devices. DMFBs are designed to carry out biochemical analysis efficiently. Scheduling DMFB operations is a constrained optimization problem, and the respective decision problem is NP-complete. We propose a hybrid artificial bee colony (ABC) algorithm using generalized N-point crossover (GNX) based Scheduling for DMFB operations. There are several schedules possible for a given set of microfluidic operations. Heuristic algorithms are capable of finding one such possible schedule from search space (possible schedules) while our proposed ABC-GNX perturbs through search space, evaluates several possible schedules, and outputs optimal schedule among the evaluated schedules. While several iterative improvement based algorithms exist, which can do the same but, the proposed ABC-GNX algorithm finds the optimal solution in less execution time as compared to existing algorithms. ABC-GNX produces a higher number of optimal assay completion times and less execution time on evaluated benchmarks.

The optimization of spare parts inventory is very important in the modern aerospace engineering system, especially in the environment with low management effectiveness and a wide variety of spare parts. At present, there are many optimization models for spare parts inventory, and the single-objective optimization inventory is mostly used. But the single-objective optimization model has some limitations. First, in the applications of practical engineering, a single-goal decision problem is generally rare, and most of the decisions we have experienced involve many complicated goals. Second, it is difficult to truly present the actual situation when the mathematical programming model is used to discuss the optimization problem in practical engineering application. The solution to solve the model is a hybrid intelligent algorithm by combining the genetic algorithm with the inverse uncertainty distribution function. Finally, an example is given to illustrate the feasibility of the optimization model.

The decision problem of quantified Boolean formulas (QBFs) is the archetypical problem for the complexity class PSPACE. Beside such theoretical aspects QBF also provides an attractive framework for encoding and solving various application problems ranging from symbolic reasoning in artificial intelligence to the formal verification and synthesis of computing systems. In this paper, we survey the different application areas that exploit QBF technology for solving their specific problems.

Today, decision making problems are continually evolving due to the new needs of society, caused mainly by continuous technological advances. Many times, in order to face these new decision problems, it is no longer enough with the participation of only a few experts, but hundreds or thousands are necessary. The engagement of many experts implies, in turn, the appearance of new challenges such as the management of greater uncertainty, scalability, opinions’ polarization etc. This contribution is focused on group decision making problems in a large-scale context in which uncertainty is modeled by linguistic information and how to deal with the scalability problem under these conditions through clustering methods. Clustering methods are used to manage the scalability problem by grouping the initial large group of experts into smaller subgroups according to the similarity of their opinions and assign different weights to such subgroups. The weights assignment is a key issue due to its influence in the final solution of the problem and classically, it has been carried out by taking into account exclusively the size of the subgroups, by ignoring other features such as the cohesion of the opinions in the subgroups, which may provoke a misassignment of the importance in experts’ subgroups and thus, unfair solutions. Therefore, this paper introduces a new way to calculate and assign properly the relevance of experts’ subgroups in clustering methods by taking into account both size and cohesion of such subgroup under uncertainty conditions in which experts use linguistic assessments.

In Analytic Hierarchy Process (AHP), a decision problem is structured hierarchically with the criteria and alternatives. The propriety weight vector of the alternatives is obtained as the sums of their local weights considering the importance of the criteria. Once a decision maker finds out his/her preference as the importance of criteria, s/he can evaluate the other sets of alternatives. The preference for criteria is essential and independent of the alternatives. However, the criteria are intangible so that it sometimes difficult for a decision maker to give judgments on them directly. Hence, we propose a method to help a decision maker to find out his/her preference based on his/her experience. It is much easier for him/her to give the judgments on the alternatives that s/he knows well. The preference is denoted as a fuzzy vector of criteria from a possibilistic view. We also propose the model to obtain an interval score vector of new alternatives from the derived preference and their given local scores. Both models of deriving a preference vector and a score vector are based on interval inclusion relation.

Ant Colony Optimization (ACO) was developed to be a metaheuristic and widely used for combinatorial optimization problems. It is confirmed that the parameters setting is of vital importance for performance of ACO. This paper proposed a strategy to perform ACO Parameters Control based on Evolutionary Strength (ACOP_ES). First, we transform four parameters setting problem to decision making problem. Then, by learning the evolutionary strength curve, we set sum, peak, and kurtosis of evolutionary strength curve as the objective function, such that the continuous ant colony algorithm can be used to optimize this decision problem. Further, via characterizing an excellent evolutionary curve, a relatively optimal parameters setting can be found. Compared with existing methods, our proposed method focuses on the performance of the running process rather than the results only. To the best of our knowledge, we are among the first to introduce process performance evaluation of ACO to guide parameters control, and determine the relatively optimal parameters of ACO. At last, we apply our method to Multi-dimensional Knapsack Problem (MKP), the results look promising that parameters returned by ACOP_ES are better than the ones returned by other methods on datasets: WEISH*.DAT and WEING*.DAT.

We introduce Boundary Focused Thompson sampling (BFTS), a new Bayesian algorithm to solve the anytime m-top exploration problem, where the objective is to identify the m best arms in a multi-armed bandit. First, we consider a set of existing benchmark problems that consider sub-Gaussian reward distributions (i.e., Gaussian with fixed variance and categorical reward). Next, we introduce a new environment inspired by a real world decision problem concerning insect control for organic agriculture. This new environment encodes a Poisson rewards distribution. For all these benchmarks, we experimentally show that BFTS consistently outperforms AT-LUCB, the current state of the art algorithm.