Scheduling of flexible manufacturing systems integrating petri nets and artificial intelligence methods.

We present two aspects of knitting technique, the structural properties (especially the P- and T-invariants), and the synchronized choice net (a new class of Petri net), that are of both theoretical importance and practical uses to the verification of structural correctness of a Petri net or to detect the structural problem of a Petri net. This work first proves that the ordinary Petri nets synthesized with knitting technique are structurally bounded, consistent, conservative and safe (when each home place holds one token) using the well-known linear algebra approach. It also provides a procedure for finding P- and T-invariants for Petri net synthesized using the knitting technique. We present examples for P-invariants and show that we can synthesize Petri nets more general than the "asymmetric-choice nets". The algorithm for finding P-invariants of ordinary Petri nets is extended to find the P-invariants for a general Petri net synthesized with knitting technique and the arc-ratio rules. We present a new class of Petri nets, called synchronized choice nets, which are the largest set of Petri nets that can be covered by both T-components and P-components. An algorithm is proposed to find its T-components and the P-components, respectively. The complexity of this algorithm is also presented. The theory of synchronized choice nets has the potential to simplify that for free choice nets.

In global supply chain, the scheduling problems of large products such as ships, airplanes, space shuttles, assembled constructions, and/or automobiles are complicated by nature. New scheduling systems are often developed in order to reduce inherent computational complexity. As a result, a problem can be decomposed into small sub-problems, problems that contain independently small scheduling systems integrating into the initial problem. As one of the authors experienced, DAS (Daewoo Shipbuilding Scheduling System) has adopted a two-layered hierarchical architecture. In the hierarchical architecture, individual scheduling systems composed of a high-level dock scheduler, DAS-ERECT and low-level assembly plant schedulers, DAS-PBS, DAS-3DS, DAS-NPS, and DAS-A7 try to search the best schedules under their own constraints. Moreover, the steep growth of communication technology and logistics enables it to introduce distributed multi-nation production plants by which different parts are produced by designated plants. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. No standard coordination mechanism of multiple scheduling systems exists, even though there are various scheduling systems existing in the area of scheduling research. Previous research regarding the coordination mechanism has mainly focused on external conversation without capacity model. Prior research has heavily focuses on agent-based coordination in the area of agent research. Yet, no scheduling domain has been developed. Previous research regarding the agent-based scheduling has paid its ample attention to internal coordination of scheduling process, a process that has not been efficient. In this study, we suggest a general framework for agent-based coordination of multiple scheduling systems in global supply chain. The purpose of this study was to design a standard coordination mechanism. To do so, we first define an individual scheduling agent responsible for their own plants and a meta-level coordination agent involved with each individual scheduling agent. We then suggest variables and values describing the individual scheduling agent and meta-level coordination agent. These variables and values are represented by Backus-Naur Form. Second, we suggest scheduling agent communication protocols for each scheduling agent topology classified into the system architectures, existence or nonexistence of coordinator, and directions of coordination. If there was a coordinating agent, an individual scheduling agent could communicate with another individual agent indirectly through the coordinator. On the other hand, if there was not any coordinating agent existing, an individual scheduling agent should communicate with another individual agent directly. To apply agent communication language specifically to the scheduling coordination domain, we had to additionally define an inner language, a language that suitably expresses scheduling coordination. A scheduling agent communication language is devised for the communication among agents independent of domain. We adopt three message layers which are ACL layer, scheduling coordination layer, and industry-specific layer. The ACL layer is a domain independent outer language layer. The scheduling coordination layer has terms necessary for scheduling coordination. The industry-specific layer expresses the industry specification. Third, in order to improve the efficiency of communication among scheduling agents and avoid possible infinite loops, we suggest a look-ahead load balancing model which supports to monitor participating agents and to analyze the status of the agents. To build the look-ahead load balancing model, the status of participating agents should be monitored. Most of all, the amount of sharing information should be considered. If complete information is collected, updating and maintenance cost of sharing information will be increasing although the frequency of communication will be decreasing. Therefore the level of detail and updating period of sharing information should be decided contingently. By means of this standard coordination mechanism, we can easily model coordination processes of multiple scheduling systems into supply chain. Finally, we apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly- plant-scheduling agents, and a meta-level coordination agent. A series of experiments using the real world data are used to empirically examine this mechanism. The results of this study show that the effect of agent-based platform on coordinated scheduling is evident in terms of the number of tardy jobs, tardiness, and makespan.

Combined Petri net modelling and AI based heuristic hybrid search for flexible manufacturing systems—part 1. Petri net modelling and heuristic search

In this thesis, we study the theoretical approach on energy-efficient scheduling problems arising in demand response management in the modern electrical smart grid. Consumers send in power requests with flexible feasible timeslots during which their requests can be served. The grid controller, upon receiving power requests, schedules each request within the specified interval. The electricity cost is measured by a convex function of the load in each timeslot. The objective is to schedule all requests with the minimum total electricity cost. We study the smart grid scheduling problem in different models. For the offline model, we prove the problem is NP-hard for the general case. We propose a polynomial time algorithm for special input where jobs have unit power request and unit time duration. By adapting the polynomial time algorithm for unit-size jobs, we propose an approximation algorithm for more general input. On the other hand, we also present an exact algorithm to find the optimal schedule for the problem with general input. For the online model, we propose an online algorithm for jobs with jobs with arbitrary power request, arbitrary time duration, and arbitrary contiguous feasible intervals. We also show a lower bound of the competitive ratio for the smart grid scheduling problem with unit height and arbitrary width. For special cases, we design different online algorithms with better competitive ratios. Finally, we look at other optimization problems and show how to solve them by adapting our techniques. We prove that our online algorithm can solve the machine minimization problem with an asymptotically optimal competitive ratio. We also show that our exact algorithm can be adapted to solve other demand response management problems.

An efficient assignment and scheduling of tasks of a multiprocessor program is one of the key elements in the effective utilization of multiprocessor systems. This problem being hard to solve exactly, is the reason that so many heuristic methods for finding a suboptimal schedule exist (Hou et al., 1994; Tsuchiya et al., 1997). This paper addresses the problem of multiprocessor scheduling represented as a directed acyclic task graph (DAG) without communication costs to fully connected multiprocessors. We proposed an algorithm, called PGA/SPF (Priority-based Genetic Algorithm/Shortest Processor First schedule) where a priority-based genetic algorithm is improved with the introduction of some knowledge about the scheduling problem, which is represented by the use of genetic operators as crossover and mutation operators. And, a new mapping method, shortest processor first schedule, assigns the selected task to a processor that can minimize a task execution time efficiently. The proposed algorithms generate similar or even better solutions than the previous algorithms in terms of the completion times of the resulting schedules.

In job shop scheduling problems, the previously used nonpreemptive schedules are semiactive, active, and non-delay schedules, and they are initially designed for traditional scheduling performances and do not take energy efficiency into consideration. To fill the gap, this article presents a novel class of nonpreemptive schedules by investigating the special properties of job shop scheduling problems. Then, an energy-efficient generation procedure of the novel nonpreemptive schedules and the standby policy are proposed. An improved genetic algorithm is introduced with three superior features, including the elitist selection, the improved generation alternation model, and the operator decision mechanism. The Friedman test and the Holm multiple comparison test are conducted on experimental results to determine the appropriate parameter for the proposed genetic algorithm. Finally, the computation results and two-sample t-tests show that both energy-efficient generation procedure and standby policy help to reduce energy consumption without worsening production efficiency.

For solving the transmission-line maintenance scheduling optimization problem(TMSOP),the uncertainties of power system load,generator output,line failure rate and maintenance resource were analyzed.An optimization model based on stochastic expected value two-level programming(SEVTP) was established,taking the static safety risk of electrical system into consideration.This model synthetically evaluates the possibility and outcomes of uncertainty,plus the objective function quantifies the risk item in forms of static safety controlling cost.A hybrid intelligent optimization algorithm,combining Monte Carlo method,interior point method,particle swarm optimization(PSO) and tabu list,was presented to solve the Nash balance and Stackelberg-Nash balance of this model.The optimization outcome includes the line maintenance schedule and the arrangement for the maintenance resources,as well as the least-risk-operation style for the power system during the maintenance.Finally,the feasibility of this model and the solution was verified by an IEEE-RTS example.

This paper presents the Tempus real-time middleware, which supports real-time CORBA 2.0's distributable threads (DTs) as an end-to-end programming abstraction for distributed real-time systems. DTs in Tempus can have time constraints, including time/utility functions (TUFs), can have resource constraints, including mutual exclusion, and can be scheduled according to utility accrual (UA) disciplines. Tempus propagates the scheduling parameters of DT's as they transit objects and hence perhaps node boundaries. Node-local instances of a UA scheduling algorithm use the propagated parameters to construct local schedules and resolve resource dependencies for local timeliness optimization, toward approximate, system-wide timeliness optimality. Tempus uses an application-level scheduling framework for node-local TUF/UA scheduling on real-time POSIX-compliant operating systems. Our experimental measurements demonstrate the effectiveness of the middleware in scheduling DTs.

As a main distributed computing system, Spark has been used to solve problems with more and more complex tasks. However, the native scheduling strategy of Spark assumes it works on a homogenized cluster, which is not so effective when it comes to heterogeneous cluster. The aim of this study is looking for a more effective strategy to schedule tasks and adding it to the source code of Spark. After investigating Spark scheduling principles and mechanisms, we developed a stratifying algorithm and a node scheduling algorithm are proposed in this paper to optimize the native scheduling strategy of Spark. In this new strategy, the static level of nodes is calculated, the dynamic factors such as the length of running tasks, and CPU usage of work nodes are considered comprehensively. And through a series of comparative experiments in alienation cluster, the new strategy costs less running time and lower CPU usage rate than the original Spark strategy, which verifies that the new schedule strategy is more effective one.

In the area of Petri nets, many different developments have taken place within the last 30 years, in academia as well as in practice. For an adequate use in practice, a coherent and application oriented combination of various types and techniques for Petri nets is necessary. In order to attain a formal basis for different classes of Petri nets we introduce the concept of abstract Petri nets. The essential point of abstract Petri nets is to allow different kinds of net structures as well as the combination of various kinds of data types. This means that in abstract Petri nets the data type and the net structure part can be considered as abstract parameters which can be instantiated to different concrete net classes. We show that several net classes, like place/transition nets, elementary nets, S-graphs, algebraic high-level nets, and predicate/transitions nets are instantiations of abstract Petri nets. Moreover, we discuss coloured Petri nets in this context.KeywordsPredicate LogicForgetful FunctorUniform ApproachSpeci CationSignature Morphism

A power plant converts energy from a non-electric form to an electric one. Depending on the energy conversion, power plants are classified as minerals, nuclear, solar, geothermal, hydroelectric, and so on. The main goal is to perform this transformation as best as possible. Criteria of safety, efficiency, reliability and affordability are taken into account as benchmarks. The station consists of several units that generate and work together to meet electricity needs. For a fossil fuel power plant, each unit consists of three main components: a boiler, a turbine and a generator. The complexity of the operation is due to the variability of the load and high efficiency required in a wide range of operations. The main difficulties for the management task then arise due to the strong link between process variables and process nonlinearity. The purpose of this article is to analyze and develop a proposal for the introduction of artificial intelligence in the power industry of Ukraine. To achieve this goal, we will offer the following tasks: - analyze existing systems for the use of artificial intelligence in industry; - develop proposals for the introduction of artificial intelligence in the electricity system of Ukraine. In this study, methods of statistical, factorial, historical, comparative, logical, economic-mathematical and systems analysis, the method of expert assessments were used, which allowed to formulate proposals for the introduction of artificial intelligence in the power industry. Artificial neural networks are the first step towards a fundamentally new system of information analysis. They are mathematical and computer models that simulate the work of biological neurons, ie a system of interacting processes, built on the principle of connecting nerve cells of the human brain. It should be noted that they differ from conventional machine algorithms in the ability to learn, memorize and reproduce images, determine patterns, memorize and analyze information and obtain results. Output signals that arrive at the next processor and continue to be converted. Thus, artificial neurons form networks and layers in which connections are created, restored, maintained and destroyed by special signals. With regard to artificial intelligence, there is no generally accepted definition of artificial intelligence, therefore, by artificial intelligence we mean a machine system capable of learning using objective knowledge and experience, to solve creative problems like the human brain and inventive tasks, not going through options, build strategies and apply abstract concepts. Digitalization and artificial intelligence are penetrating most sectors of the economy, including the electricity sector. The digitalization of energy requires the development and widespread use of end-to-end technologies, including industrial Internet, components of robotics, wireless communications, artificial intelligence and others. Thus, artificial intelligence technologies have prospects for development. Of course, significant targeted funding is needed to achieve significant results. The interaction of research institutes and universities with business is very important, where artificial intelligence technologies are also being developed. Attention of the authorities and society, their understanding of the importance of the tasks facing countries to achieve the goal of implementing artificial intelligence. With regard to electricity, the integration of artificial intelligence into the industry will help optimize and increase efficiency in all aspects of energy production, transmission and consumption. It should be noted that the development of electricity is a step towards the development of other industries. That is why the transition to the digital industry is impossible without the digitalization and intellectualization of the power industry

We consider the problem of scheduling with renewable speed-up resources. Given m identical machines, n jobs and c different discrete resources, the task is to schedule each job non-preemptively onto one of the machines so as to minimize the makespan. In our problem, a job has its original processing time, which could be reduced by utilizing one of the resources. As resources are different, the amount of the time reduced for each job is different depending on the resource it uses. Once a resource is being used by one job, it can not be used simultaneously by any other job until this job is finished, hence the scheduler should take into account the job-to-machine assignment together with the resource-to-job assignment. We observe that, the classical unrelated machine scheduling problem is actually a special case of our problem when m=c, i.e., the number of resources equals the number of machines. Extending the techniques for the unrelated machine scheduling, we give a 2-approximation algorithm when both m and c are part of the input. We then consider two special cases for the problem, with m or c being a constant, and derive PTASes (Polynomial Time Approximation Schemes) respectively. We also establish the relationship between the two parameters m and c, through which we are able to transform the PTAS for the case when m is constant to the case when c is a constant. The relationship between the two parameters reveals the structure within the problem, and may be of independent interest.

When a Petri net is synthesised from a labelled transition system, it is frequently desirable that certain additional constraints are fulfilled. For example, in circuit design, one is often interested in constructing safe Petri nets. Targeting such subclasses of Petri nets is not necessarily computationally more efficient than targeting the whole class. For example, targeting safe nets is known to be NP-complete while targeting the full class of place/transition nets is polynomial, in the size of the transition system. In this paper, several classes of Petri nets are examined, and their suitability for being targeted through efficient synthesis from labelled transition systems is studied and assessed. The focus is on choice-free Petri nets and some of their subclasses. It is described how they can be synthesised efficiently from persistent transition systems, summarising and streamlining in tutorial style some of the authors’ and their groups’ work over the past few years.

Scheduling task graphs under hard (end-to-end) timing constraints is an extensively studied NP-hard problem of critical importance for predictable software mapping on Multiprocessor System-on-chip (MPSoC) platforms. In this work we focus on an off-line (design-time) version of this problem, where the target task graph is known before execution time. We address the issue of scheduling robustness, i.e. providing hard guarantees that the schedule will meet the end-to-end deadline in presence of bounded variations of task execution times expressed as min-max intervals known at design time. We present a robust scheduling algorithm that proactively inserts sequencing constraints when they are needed to ensure that execution will have no inserted idle times and will meet the deadline for any possible combination of task execution times within the specified intervals. The algorithm is complete, i.e. it will return a feasible graph augmentation if one exists. Moreover, we provide an optimization version of the algorithm that can compute the shortest deadline that can be met in a robust way.

The scheduling of academic courses is a problem in which a weekly schedule is produced for educational purposes. Many different types of scheduling problems exist at various universities in accordance with their laws, needs, and constraints. These problems fall into the category of NP-hard problems and are incredibly complex. In this paper, an intelligent system for scheduling courses using the deep-belief network is proposed. The reason why the proposed system is intelligent is that it can learn the constraints, inputs, and other necessary parameters in one step by receiving the inputs as well as the training needed by the deep-belief network. The deep-belief network used has one output layer, four hidden layers, and four input layers. The experimental results of this research show that the deep-belief network proposed for the scheduling of academic courses provides a better score, less error, and execution time compared with Sequence-Based Selection Hyper-Heuristic (SSHH) approach.