A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing

The Industrial Internet of Things (IIoT) interconnects a large number of interconnected sensors, actuators, and edge computing devices in the manufacturing systems, where the massive data collected in the manufacturing process has the characteristics of multi-dimensional, heterogeneous, and time series. An effective data representation manner, which can fuse such complex information and enable cognitive manufacturing decision-making from a global perspective, is necessary and challenging. To solve this issue, this paper proposes a knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing and applies it in a Cyber-Physical Production System (CPPS) scenario. Based on the digital thread of manufacturing process data, a multi-layer manufacturing knowledge graph is established, including device sensing data, production processing data, and business processing data. With the established knowledge graph, a cognition-driven approach is proposed with a perception-cognition dual system, which achieves perception analysis and cognition decision-making in the resource allocation of the manufacturing process. Finally, responding to the orders of personalized products in a workshop is taken as an illustrative example. The performance of allocating resources of workshop devices under dynamic demand changes shows the advantages of the proposed approach. The proposed manner will lay the foundation for a human-like cognition for processing massive real-time industrial information in CPPS, thus paving a pathway towards the era of cognitive manufacturing.     

A parallel hypercube algorithm for discrete resource allocation problems

It has been suggested that parallel processing helps in the solution of difficult discrete optimization problems, in particular, those problems that exhibit combinatorial search and require large-scale computations. By using a number of processors that are connected, coordinated and operating simultaneously, the solutions to such problems can be obtained much more quickly. The purpose of this paper is to propose an efficient parallel hypercube algorithm for the discrete resource allocation problem (DRAP). A sequential divide-and-conquer algorithm is first proposed. The algorithm is then modified for a parallel hypercube machine by exploiting its inherent parallelism. To allocate N units of discrete resources to n agents using a d-dimensional hypercube of p=2/sup d/ nodes, this parallel algorithm solves the DRAP in O((n/p+log/sub 2/p)N/sup 2/) time. A simulation study is conducted on a 32-node nCUBE/2 hypercube computer to present the experimental results. The speedup factor of the parallel hypercube algorithm is found to be more significant when the number of agents in the DRAP is much greater than the number of processing nodes on the hypercube. Some issues related to load balancing, routing, scalability, and mappings of the parallel hypercube algorithm are also discussed.     

A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms

Natural resource allocation is a complex problem that entails difficulties related to the nature of real world problems and to the constraints related to the socio-economical aspects of the problem. In more detail, as the resource becomes scarce relations of trust or communication channels that may exist between the users of a resource become unreliable and should be ignored. In this sense, it is argued that in multi-agent natural resource allocation settings agents are not considered to observe or communicate with each other. The aim of this paper is to study multi-agent learning within this constrained framework. Two novel learning methods are introduced that operate in conjunction with any decentralized multi-agent learning algorithm to provide efficient resource allocations. The proposed methods were applied on a multi-agent simulation model that replicates a natural resource allocation procedure, and extensive experiments were conducted using popular decentralized multi-agent learning algorithms. Experimental results employed statistical figures of merit for assessing the performance of the algorithms with respect to the preservation of the resource and to the utilities of the users. It was revealed that the proposed learning methods improved the performance of all policies under study and provided allocation schemes that both preserved the resource and ensured the survival of the agents, simultaneously. It is thus demonstrated that the proposed learning methods are a substantial improvement, when compared to the direct application of typical learning algorithms to natural resource sharing, and are a viable means of achieving efficient resource allocations.     

Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach

Empowered by the advanced cognitive computing, industrial Internet-of-Things, and data analytics techniques, today’s smart manufacturing systems are ever-increasingly equipped with cognitive capabilities, towards an emerging Self-X cognitive manufacturing network with higher level of automation. Nevertheless, to our best knowledge, the readiness of ‘Self-X’ levels (e.g., self-configuration, self-optimization, and self-adjust/adaptive/healing) is still in the infant stage. To pave its way, this work stepwise introduces an industrial knowledge graph (IKG)-based multi-agent reinforcement learning (MARL) method for achieving the Self-X cognitive manufacturing network. Firstly, an IKG should be formulated based on the extracted empirical knowledge and recognized patterns in the manufacturing process, by exploiting the massive human-generated and machine-sensed multimodal data. Then, a proposed graph neural network-based embedding algorithm can be performed based on a comprehensive understanding of the established IKG, to achieve semantic-based self-configurable solution searching and task decomposition. Moreover, a MARL-enabled decentralized system is presented to self-optimize the manufacturing process, and to further complement the IKG towards Self-X cognitive manufacturing network. An illustrative example of multi-robot reaching task is conducted lastly to validate the feasibility of the proposed approach. As an explorative study, limitations and future perspectives are also highlighted to attract more open discussions and in-depth research for ever smarter manufacturing.     

A systematic approach for resource allocation in software projects

The completion of reliable software products within their expected time frame represents a major problem for companies that develop software applications. Today, the software industry continues to struggle with delivering products in a timely manner. A major cause for delays is the training time required for engineers and other personnel to acquire the necessary skills to complete software tasks. Therefore, it is important to develop systematic personnel assignment processes that consider complete skill sets of candidates to provide solutions that reduce training time. This paper presents a novel methodology to assign resources to tasks when optimum skill sets are not available. The methodology takes into account existing capabilities of candidates, required levels of expertise, and priorities of required skills for the task. A sample case is used to show the model capabilities, and the results are compared with the current resource assignment approach.     

A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multi-objective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies.     

A stochastic method for discrete and continuous optimization in manufacturing systems

Algorithms for discrete and continuous optimization are a very important part of decision making systems in manufacturing. Most planning, scheduling and layout problems require these algorithms. In practice, research into efficient algorithms meets two principal obstacles. The first one is linked to the fact that quite often the criteria cannot be expressed in an analytic form, so it is not possible to use existing theoretical resolution methods. The second is due to the fact that most of the problems for which the criteria can be represented in analytic form are NP-hard problems. This situation can be simplified using simulation. But the use of simulation and optimization methods together often gives a local optimum. The proposed method in this paper is based on the use of a discrete modification of -transforms jointly with some heuristics for local optimization. The originality of this approach is in the possibility to avoid a local optimum, while using models of simulation for the computation of values of the criteria. An example of the utilization of the method is given: it concerns the optimization of the launching of the parts in production in systems of the job-shop type. The proposed method is compared with a heuristic known to be very good in the same number of simulations. The results of five tests with different model sizes show the efficiency of the proposed method.     

基于贝叶斯策略的网格资源分配方法

考虑网格应用QoS需求,以最大化网格系统的经济收益为目标,提出了一种基于贝叶斯策略的网格资源分配方法。该方法基于价格可代表资源QoS综合性能的思想,利用历史QoS匹配记录,运用朴素贝叶斯定理根据用户提交的价格参数来分配与其级别相对应的符合要价范围资源,并优先考虑QoS水平较高的任务,将当前时刻QoS水平较高的资源预留给下一时刻到达的QoS水平较高的任务,而忽略当前时刻QoS水平较低的任务。实验结果表明,该资源分配方法不仅能有效地保障用户QoS,而且能使网格系统获得较大的经济收益。     

A deadlock avoidance approach for nonsequential resource allocation systems

The paper concentrates on the deadlock-avoidance problem for a class of resource allocation systems modeling manufacturing systems. In these systems, a set of production orders have to be executed in a concurrent way. To be executed, each step of each production order needs a set of reusable system resources. The competition for the use of these resources can lead to deadlock problems. Many solutions, from different perspectives, can be found in the literature for deadlock-related problems when the production orders have a sequential nature [sequential resource allocation systems (S-RAS)]. However, in the case in which the involved processes have a nonsequential nature [nonsequential resource allocation systems (NS-RAS)], the problem becomes more complex. In this paper, we propose a deadlock avoidance algorithm for this last class of systems. We also show the usefulness of the proposed solution by means of its application to a real system.     

A population game approach for dynamic resource allocation problems

We consider a water distribution system as an example of resource allocation, and investigate the use of a population game for its control. We use a game-theoretic approach based on two evolutionary dynamics, the Brown–von Neumann–Nash and the Smith dynamics. We show that the closed-loop feedback interconnection of the water distribution system and the game-theoretic-based controller has a Nash equilibrium as an asymptotically stable equilibrium point. The stability analysis is performed based on passivity concepts and the Lyapunov stability theorem. An additional control subsystem is considered for disturbance rejection. We verify the effectiveness of the method by simulations under different scenarios.     

Clustering high dimensional data: A graph-based relaxed optimization approach

There is no doubt that clustering is one of the most studied data mining tasks. Nevertheless, it remains a challenging problem to solve despite the many proposed clustering approaches. Graph-based approaches solve the clustering task as a global optimization problem, while many other works are based on local methods. In this paper, we propose a novel graph-based algorithm “GBR” that relaxes some well-defined method even as improving the accuracy whilst keeping it simple. The primary motivation of our relaxation of the objective is to allow the reformulated objective to find well distributed cluster indicators for complicated data instances. This relaxation results in an analytical solution that avoids the approximated iterative methods that have been adopted in many other graph-based approaches. The experiments on synthetic and real data sets show that our relaxation accomplishes excellent clustering results. Our key contributions are: (1) we provide an analytical solution to solve the global clustering task as opposed to approximated iterative approaches; (2) a very simple implementation using existing optimization packages; (3) an algorithm with relatively less computation time over the number of data instances to cluster than other well defined methods in the literature.     

A mean field approach for optimization in discrete time

This paper investigates the limit behavior of Markov decision processes made of independent objects evolving in a common environment, when the number of objects (N) goes to infinity. In the finite horizon case, we show that when the number of objects becomes large, the optimal cost of the system converges to the optimal cost of a discrete time system that is deterministic. Convergence also holds for optimal policies. We further provide bounds on the speed of convergence by proving second order results that resemble central limits theorems for the cost and the state of the Markov decision process, with explicit formulas for the limit. These bounds (of order \(1/\sqrt{N}\)) are proven to be tight in a numerical example. One can even go further and get convergence of order \(\sqrt{\log N}/N\) to a stochastic system made of the mean field limit and a Gaussian term. Our framework is applied to a brokering problem in grid computing. Several simulations with growing numbers of processors are reported. They compare the performance of the optimal policy of the limit system used in the finite case with classical policies by measuring its asymptotic gain. Several extensions are also discussed. In particular, for infinite horizon cases with discounted costs, we show that first order limits hold and that second order results also hold as long as the discount factor is small enough. As for infinite horizon cases with non-discounted costs, examples show that even the first order limits may not hold.     

A novel self-optimizing approach for knowledge acquisition

The attribute reduction and rule generation (the attribute value reduction) are two main processes for knowledge acquisition. A self-optimizing approach based on a difference comparison table for knowledge acquisition aimed at the above processes was proposed. In the attribute reduction process, the conventional logic computation was transferred to a matrix computation along with some added thoughts on the evolution computation used to construct the self-adaptive optimizing algorithm. In addition, some sub-algorithms and proofs were presented in detail. In the rule generation process, the orderly attribute value reduction algorithm (OAVRA), which simplified the complexity of rule knowledge, was presented. The approach provided an effective and efficient method for knowledge acquisition that was supported by the experimentation.     

Predefined-time distributed multiobjective optimization for network resource allocation

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted L<...     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.     

A reinforcement learning approach to dynamic resource allocation

This paper presents a general framework for performing adaptive reconfiguration of a distributed system based on maximizing the long-term business value, defined as the discounted sum of all future rewards and penalties. The problem of dynamic resource allocation among multiple entities sharing a common set of resources is used as an example. A specific architecture (DRA-FRL) is presented, which uses the emerging methodology of reinforcement learning in conjunction with fuzzy rulebases to achieve the desired objective. This architecture can work in the context of existing resource allocation policies and learn the values of the states that the system encounters under these policies. Once the learning process begins to converge, the user can allow the DRA-FRL architecture to make some additional resource allocation decisions or override the ones suggested by the existing policies so as to improve the long-term business value of the system. The DRA-FRL architecture can also be deployed in an environment without any existing resource allocation policies. An implementation of the DRA-FRL architecture in Solaris 10 demonstrated a robust performance improvement in the problem of dynamically migrating CPUs and memory blocks between three resource partitions so as to match the stochastically changing workload in each partition, both in the presence and in the absence of resource migration costs.     

A novel resource allocation mechanism for live cloud-based video streaming service

With the recent emergence of cloud computing, growing numbers of clients are using online cloud services through the Internet such as video streaming service. The rent costs of cloud service providers increase when the resource utilizations of the cloud-servers are not well. Therefore, resource allocation is a crucial problem for cloud data centers. The resource allocation problem is an NP-hard problem. This paper proposes a novel cloud resource allocation mechanism based on a winning strategy for a Nim game. This mechanism offers all clients an effective number of running cloud servers, and allocates cloud resources rapidly and effectively by using a pre-pairing approach. The proposed mechanism does not require searching for remaining resources of the running cloud server; hence, it can reduce the time taken to arrange resources. The experimental results show that the proposed mechanism can improve utilization of cloud servers and reduce the rent costs of the cloud service providers. The proposed mechanism can reach the utilization of cloud servers by as much as 99.96 %. The proposed mechanism is approximately 9 % more efficient than the market-based grid resource allocation algorithm, and 19 % more efficient than the modified best fit decreasing algorithm.