Knowledge-Based Design of Scheduling Systems

The purpose of this paper is to present a computerized integrated project management system and report results of a survey on the effectiveness of the system. The system consists of a scheduling system, material management system, labor/equipment system, and safety/quality control system. The backbone system is a scheduling system that adopts a production planning system and a project scheduling system. The lowest level in the scheduling system is a daily work management system, which is linked to each functional management system (i.e. material management system, labor/equipment system, and safety/quality control system). The paper focuses on the material management and scheduling systems to implement a material “pull” system to reduce material inventories on site. Details of material management and scheduling systems are discussed, and a sample application is presented to demonstrate the features of the proposed computer application system. The paper presents practitioners and researchers with a practical tool to integrate material management and scheduling systems for site personnel.

Architecture of manufacturing scheduling systems: Literature review and an integrated proposal

A number of successful applications which have been developed using modern planning and scheduling methods are described. The applications cover a number of areas of current business interest and include manufacturing, logistics and crisis planning. In addition to the techniques and benefits described through the applications, a number of other planning and scheduling techniques are introduced and described. While certain techniques have had a major impact in some areas, further research and development is required. These future developments are targeted at providing integrated intelligent planning rather than the `islands' of techniques which currently exist. A description is given of an integrated planning and scheduling system which could address the current problems in integrating legacy systems with modern planning and scheduling technologies.

This paper describes an intelligent fuzzy decision support system for real-time scheduling and dispatching of parts in a flexible manufacturing system (FMS), with alternative routing possibilities for all parts. A fuzzy logic approach is developed to improve the system performance by considering multiple performance measures and at multiple decision points. The characteristics of the system status, instead of parts, are fed back to assign priority to the parts waiting to be processed. A simulation model is developed and it is shown that the proposed intelligent fuzzy decision support system keeps all performance measures at a good level. The proposed intelligent system is a promising tool for dealing with scheduling FMSs, in contrast to traditional rules.

Evaluation and comparison of production schedules

The intelligent production line is a new intelligent production line resource formed by highly integrating modern sensor technology, communication technology, information technology, computer technology and control technology on the large physical real basis of the existing production line. It can finish data acquisition, information processing, dynamic virtual reconfiguration of variable batch production line, embedded algorithm, algorithm optimize the management of the production line manufacturing field, change the dynamic virtual reorganization of the mass production line, and become the choice of the development direction of the next generation of new production lines for mankind. Scheduling is the nerve center of the production line virtual reorganization system. The traditional main auxiliary decision-making tool management system can no longer fully meet the requirements of production line operation monitoring in the new environment, so the construction of an intelligent scheduling system is also one of the important contents of the construction of the production line virtual reorganization. Based on this, this article explores and researches intelligent service scheduling decision-making technology under artificial intelligence technology. The research topic of this article is intelligent scheduling of production lines, so this article first analyzes the current research status of intelligent service scheduling and decision support systems; then conducts research based on Agent decision support systems, and analyzes the framework and characteristics of decision support systems. realizes the intelligent production line scheduling DSS system; then this paper designs the system’s scheduling model with stochastic disturbance, system database and system fault diagnosis function, and realizes an intelligent service scheduling decision support system based on multi-agent technology. Finally, this article tests the data transmission speed and fault diagnosis function of the system. The test results show that the system is relatively fast in data transmission speed and the fault diagnosis function is also very reliable.

Scheduling of energy storage systems of distributed generation is a key element for optimal renewable energy sources (RES) exploitation in smart grids, demand side response strategies implementation and prosumers interaction with Distribution System Operators (DSOs). In this context, this paper presents the implementation of a management system for energy storage scheduling, which allows taking appropriate account of the storage system behavior in real cases. To properly consider the real system operation, the employed battery model is modified, in order to include also the hysteresis phenomena in state of charge estimation in both charging and discharging operation. The validation of the proposed methodology is supported by experimental tests which allow comparing two different battery models. The obtained results show that better results can be obtained with the model including the hysteresis effect.

This paper discusses the implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process. It is an interactive, real-time, menu-driven system written in Prolog. The system architecture is delineated and the rules and problem-solving logic to be used under various dynamic situations are described. Results of a sample system test session are included to illustrate its use.

Smart grid is an inevitable choice for the future development of the grid. Smart scheduling is one of the cores to build the strong and smart grid in China. Optimization and Management System for Grid Maintenance Scheduling is an in-depth exploration to achieve dispatching digitalization, intellectualization and visualization. This paper gives a rather detailed description about the functionality and implementation of Optimization and Management System for Grid Maintenance Scheduling. With received maintenance schedules as the basic data, OCCI-based data processing techniques and PSO-based optimization methodology are adopted to achieve the standardization and intellectualization of the grid maintenance scheduling. Moreover, the maintenance schedules generated and related information are managed in a standardized way. The design from this paper together with its programs has been applied in practice, achieving satisfying effect.

This chapter describes briefly the concept of hybrid (multi-model) systems and presents two implementations of such systems. The first one is a hybrid system for job-shop scheduling. It consists of a job-shop simulation model used to build a schedule and calculate its quality and an expert system optimising the schedule. A hypothetical job-shop (inspired from a real industrial shop) containing seven machine tools processing parts of ten types with complex processing routes is used to illustrate the possibilities of the system. The second implementation is a multimodel system for scheduling in the chemical industry. It also combines simulation, rules-based decision making, object-oriented representation of the system state as well as a set of well-known algorithms for solving particular problems used together for scheduling discrete-continuous processes.

Modular control system with intelligent scheduling

Fuzzy adaptive scheduling and control systems

UpStream is a framework for load management over data streams with update semantics. It provides a novel storage manager architecture that can be plugged into data stream processing engines for serving streaming applications that require low-staleness results over real-time continuous queries. We propose to demonstrate the key aspects of the UpStream architecture as well as its performance using two different application scenarios: One that models a continuously updating financial market dashboard, and another one that is based on an intelligent transportation system for monitoring moving vehicles on a road traffic network. The demonstration will illustrate how UpStream can provide low-staleness query results for these applications under highly overloaded situations, by using a number of update scheduling and storage management techniques. This will be done through a number of interactive visual monitoring tools for the application interface as well as for monitoring the run-time operation of the UpStream system itself.

Hierarchical Scheduling (HS) systems manage a set of real-time applications through a scheduling hierarchy, enabling partitioning and reduction of complexity, confinement of failure modes, and temporal isolation among system applications. This plays a crucial role in all industrial areas where high-performance microprocessors allow growing integration of multiple applications on a single platform.We propose a formal approach to the development of real-time applications with non-deterministic Execution Times and local resource sharing managed by a Time Division Multiplexing (TDM) global scheduler and preemptive Fixed Priority (FP) local schedulers, according to the scheduling hierarchy prescribed by the ARINC-653 standard. The methodology leverages the theory of preemptive Time Petri Nets (pTPNs) to support exact schedulability analysis, to guide the implementation on a Real-Time Operating System (RTOS), and to drive functional conformance testing of the real-time code. Computational experience is reported to show the feasibility of the approach.

Based on the experience in manufacturing production scheduling problems which the AI community has amassed over the last ten years, a workshop was held to provide a forum for discussion of the issues encountered in the design of AI-based scheduling systems. Several topics were addressed including : the relative virtues of expert system, deep method, and interactive approaches, the balance between predictive and reactive components in a scheduling system, the maintenance of convenient scheduling descriptions, the application of the ideas of chaos theory to scheduling, the state of the art in schedulers which learn, and the practicality and desirability of a set of benchmark scheduling problems. This article expands on these issues, abstracts the papers which were presented, and summarizes the lengthy discussions that took place.