Knowledge-based Scheduling System Research Articles

A knowledge-based real-time scheduling system for steam turbine assembly under CPS environment

PurposeSteam turbine final assembly is a dynamic process, in which various interference events occur frequently. Currently, data transmission relies on oral presentation, while scheduling depends on the manual experience of managers. This mode has low information transmission efficiency and is difficult to timely respond to emergencies. Besides, it is difficult to consider various factors when manually adjusting the plan, which reduces assembly efficiency. The purpose of this paper is to propose a knowledge-based real-time scheduling system under cyber-physical system (CPS) environment which can improve the assembly efficiency of steam turbines.Design/methodology/approachFirst, an Internet of Things based CPS framework is proposed to achieve real-time monitoring of turbine assembly and improve the efficiency of information transmission. Second, a knowledge-based real-time scheduling system consisting of three modules is designed to replace manual experience for steam turbine assembly scheduling.FindingsExperiments show that the scheduling results of the knowledge-based scheduling system outperform heuristic algorithms based on priority rules. Compared with manual scheduling, the delay time is reduced by 43.9%.Originality/valueA knowledge-based real-time scheduling system under CPS environment is proposed to improve the assembly efficiency of steam turbines. This paper provides a reference paradigm for the application of the knowledge-based system and CPS in the assembly control of labor-intensive engineering-to-order products.

A knowledge-based scheduling system for Emergency Departments

A knowledge-based reactive scheduling system is proposed to answer the requirements of Emergency Departments (EDs). The algorithm includes detailed patient priority, arrival time, flow time and doctor load. The main aim is to determine the patients who have higher priorities initially, and then minimize their waiting times. To achieve this aim, physicians and the other related workers can use an interactive system. In this study, we evaluated the existing system by comparing the proposed system. Also, reactive scheduling cases were evaluated for some items such as decreasing the number of doctors, changing durations and entering of an urgent patient to the system. All experiments were performed with proposed algorithm and right shift rescheduling approach.

A framework for developing intelligent real-time scheduling systems

The authors describe the adaptation of a hierarchical management-control framework to the design of intelligent real-time scheduling systems. Originally developed for the analysis of organizations where dynamics are governed solely by the reactions of human decision makers, the control framework provides insights concerning design requirements for decision systems that react in human-like fashion within narrow domains of expertise. The framework emphasizes the dynamic balance of autonomy and decentralization required to achieve planned goals within a hierarchical organization. Applying this framework, design specifications for knowledge-based scheduling systems are developed, which are responsive to organizational dynamics as required for schedule control and replanning. An illustration is given that addresses kitchen scheduling in full-service restaurants. © 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 385–408, 2006.

Assessment of productivity and duration of highway construction activities subject to impact of rain

Rainfall is regarded as a major uncertainty factor that has adverse impacts on productivity and duration of highway construction activities. In practice, given the location, type, start date, and original duration of the activities, a common approach for construction schedulers to assess the effect of rain is by adding a certain percentage of time to tasks. However, this method depends mainly on the experience and subjective judgment of the schedulers, who may be unfamiliar with the rainfall pattern and its impact on productivity of the operations, and thus, oftentimes produces inaccurate results. This paper presents a model that utilizes historical daily rainfall data and experts' knowledge, and employs fuzzy set concept for assessing the impact of rain on project completion. Based on the model, a fuzzy reasoning knowledge-based scheduling system (FRESS) is proposed. A case study involving a highway construction project implemented in two geographic areas with different rainfall environments is presented to illustrate the salient features of the system that allows users to simulate experts' judgment and to demonstrate the capability and effectiveness of the system that can assist contractors to better estimate activity durations for projects in geographical locations having rainfall data.

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).

Ramp Activity Expert System for Scheduling and Coordination at an Airport

In this project, we have developed the ramp activity coordination expert system (races) to solve aircraft-parking problems. races includes a knowledge-based scheduling system that assigns all daily arriving and departing flights to the gates and remote spots with domain-specific knowledge and heuristics acquired from human experts. races processes complex scheduling problems such as dynamic interrelations among the characteristics of remote spots-gates and aircraft with various other constraints, for example, customs and ground-handling factors, at an airport. By user-driven modeling for end users and near-optimal knowledge-driven scheduling acquired from human experts, races can produce parking schedules for about 400 daily flights in approximately 20 seconds; human experts normally take 4 to 5 hours to do the same. Scheduling results in the form of Gantt charts produced by races are also accepted by the domain experts. races is also designed to deal with the partial adjustment of the schedule when unexpected events occur. After daily scheduling is completed, the messages for aircraft change, and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as the rules, and the scenarios of the graphic user interfaces are designed. Because the modification of the aircraft dispositions, such as aircraft changes and cancellations of flights, is reflected in the current schedule, the modification should be sent to races from the mainframe for the reactive scheduling. The adjustments of the schedule are made semiautomatically by races because there are many irregularities in dealing with the partial rescheduling.

Auto-bias selection for developing learning-based scheduling systems

Inductive learning techniques have shown encouraging achievements in reducing the effort for knowledge acquisition in the development of knowledge-based scheduling systems. However, there is little research on selecting the proper learning biases to facilitate the development of knowledge bases (KBs), so that the prediction accuracy of the resulting KBs is enhanced. In this study, we propose an auto-bias selection procedure that is capable of determining good learning biases, such as a proper feature set (system attributes) and a suitable learning algorithm. Through the case study conducted, the proposed procedure shows its capability to select proper learning biases and the results of experiments demonstrate that the scheduling KBs, developed by the learning biases selected, have a superior ability to produce the correct scheduling strategy over the scheduling KBs developed without exploring the proper learning biases.

Knowledge-based system for railway scheduling

In this paper, we describe a Knowledge-Based railway scheduling system (called RSS) for Taiwan Railway Administration's (TRA) railway scheduling operations. In our approach, the scheduling process is divided into two levels: global scheduling and local scheduling. In global scheduling, an initial train diagram is established by the Global Scheduler according to the master scheduling plan without considering conflicts. In local scheduling, scheduling conflicts are repaired by Local Scheduler. An embedded knowledge base provides advice for conflict resolution. Since cycles may exist among a sequence of repairs during the repair process, we also propose a cycle detection and resolution scheme in this paper. Through a graphic user interface, the user can interact with the Automatic Scheduler to solve the large-scale complex train scheduling problem in a cooperative and flexible fashion.

Towards a knowledge-based scheduling system for semiconductor testing

This paper describes our efforts towards the development of a knowledge-based scheduling system for the scheduling of semiconductor testing operations. Semiconductor testing is the final phase of a four-phase semiconductor manufacturing process. The scheduling problem is a static deterministic version of what we call the workstation scheduling problem (WS). WS can be characterized as a generalized job shop problem with both parallel workstation clusters and batch processors; it requires sequencing and routeing decisions on discrete (i.e. nonbatch) and batch workstations. The proposed system consists of two distinct components: a knowledge base developed using a frame-based knowledge representation scheme, and a solution strategy based on filtered beam search. The scheduling mechanism and the associated knowledge representation scheme have been implemented on a Sun Sparc station 1+ using the programming language Common Lisp. We report on our experience to date with the system on three dimensions: computational experience, decision support capability, and knowledge management. Further research directions are suggested.

Knowledge-based scheduling systems in industry and medicine

Production management is crucial for achieving the timely and cost-effective execution of industrial production processes. In recent years, interest has increased in the use of artificial intelligence technologies for production planning and scheduling. However, scheduling research typically has been theoretical, has had a narrow focus, and has not covered adaptation to unforeseen events. The authors' objective has been to use computer-based scheduling systems to enhance the problem-solving capabilities of human domain experts. They have developed a generic framework for building practical scheduling systems. This framework fosters the reuse of algorithms and the integration of knowledge-based technology into the organizational environment. It also supports dynamic adaptation. The authors have used it to implement scheduling systems for dye production, pipeline-fittings production and heart surgery.

Indentifying attributes for knowledge-based development in dynamic scheduling environments

Inductive learning techniques have shown inspiring success in reducing the effort for knowledge acquisition in the development of knowledge-based scheduling systems. However, there is little research on selecting the proper attributes to facilitate development of knowledge bases and to enhance the generalization ability of resulting knowledge bases. In this study, we proposed a neural network based approach to identify the essential attributes for knowledge-based scheduling systems. Through the case study conducted, the proposed identification procedure is capable of selecting the essential attributes out of a given set of candidate attributes. Not only can this procedure consider several primary performance measures simultaneously, but also, by using a modularized representation, the previous results of attribute identification have the flexibility to cope with the new control strategies which may be introduced in the future. The results of experiments show that the scheduling knowledge bases developed by the set of selected attributes have superior ability to select the correct control strategy over other knowledge bases under new production conditions in a Mazak flexible manufacturing system.

Expert scheduling system for tool room operations

Abstract Operation scheduling for a tool room is a complex task because each set of tools has a unique operation sequence, and tool components are to be produced by attaching priorities depending on various technical aspects, such as the production methods, intricacy and complexity of tool components, dimensional tolerances, requirement of heat treatment, sub-assembly, final-assembly, try-out, etc. This paper discusses the design of a prototype knowledge-based scheduling system which utilizes quantitative factors, such as the estimated/actual operation times, the due dates and the resource availability, as well as non-quantitative factors, such as the nature of the order, the importance of the tool, the relative priorities of various tool components, etc. The non-quantitative factors are determined by the expert scheduler, based on his experience amassed over a long period of time. This expert system can be used on- or off-line and is characterized by IF-THEN rules. In an existing CAPP system this could be integrated to utilize master scheduling and process planning information for detailed operation scheduling.

Intelligent scheduling of FMSs with inductive learning capability using neural networks

With the growing uncertainty and complexity in the manufacturing environment, most scheduling problems have been proven to be NP-complete and this can degrade the performance of conventional operations research (OR) techniques. This article presents a system-attribute-oriented knowledge-based scheduling system (SAOSS) with inductive learning capability. With the rich heritage from artificial intelligence (AI), SAOSS takes a multialgorithm paradigm which makes it more intelligent, flexible, and suitable than others for tackling complicated, dynamic scheduling problems.

Knowledge Elicitation and the Development of a Knowledge-Based System for Production Scheduling

A prototype knowledge-based system for automated production scheduling has been developed for a press shop, which manufactures laminations for stator and rotor packs and cores for electric motor production. The system is PC (personal computer) based, user friendly, and may be interfaced to the existing computer-based MRP (material requirements planning) system, making the transfer of production data quick and easy. The knowledge-based system has been tested and validated over a range of production circumstances and the predicted production schedules show close agreement with those manually produced, over longer time-scales, by the human scheduler. The knowledge elicitation procedures developed are described and, in particular, the use of a large flow diagram to describe the decision-making process during scheduling is discussed. The visual representation of the structure of the decision-making process is of particular benefit in aiding clarity of communication between the knowledge engineer and the domain expert. The techniques for knowledge acquisition within the production scheduling environment have been formalized and their use is advocated for use in the development of scheduling systems. For the prototype system to be of practical benefit within the press shop, further development of the system is necessary to enable all the presses and lamination combinations to be scheduled. This is currently being carried out within the company.

An Approach to Standardizing the Simulation to Knowledge System Interface

This paper presents a formal model for the interface between a simulation and a knowledge-based manager, and discusses the application of this model to practical simulation problems. Models of both the simulation and the knowledge components are presented, and issues of variable selection, time representation and communication costs are addressed. The model is then applied to a case study involving simulation of a small flexible manufacturing system. A knowledge-based scheduling system in the C-Language Production System (CLIPS) is interfaced to the FMS simulation written in SIMSCRIPT. In the context of this case study, a set of heuristics for selecting interface variables and for analyzing and reducing communications overhead are presented and discussed.

Intelligent dynamic production scheduling

This paper describes the initial research and subsequent development of a knowledge-based system for finite capacity scheduling in dynamic production environments. The requirements of the problem domain are described, and our approach is contrasted with conventional simulation or algorithmic systems. An analysis of benefits is also presented from early applications case histories.

Knowledge Aquisition Methods for Building Knowledge-based Scheduling Systems

Knowledge Aquisition Methods for Building Knowledge-based Scheduling Systems

Flexible assembly scheduling using a knowledge-based approach

Abstract This paper presents a knowledge-based scheduling system that generates a detailed production schedule for jobs in a flexible assembly system (FAS) by exploiting routing and operation flexibilities. Past research in flexible manufacturing did not take into account the flexibilities of such environments that allow alternative sequencing and routing of operations presumably to avoid the computational overhead that would result from these added dimensions of complexity. Moreover, jobs in an FAS require both machining and assembly operations. In our system, the resulting computational burden is reduced by using (1) a two-level hierarchical scheduling approach, (2) a search strategy that uses knowledge from the knowledge base to control the amount of search required, and (3) a computationally efficient post analysis of the schedule that often results in local improvements and is based on knowledge about solution structures stored in the knowledge base. The system has been implemented and tested using Common Lisp on a Macintosh SE/30 with 4 MB main memory. Computational experience with the system is reported.

A Production Scheduling System

A knowledge-based scheduling system is described for a real case study. The problem is solved for a plant with multipurpose machines and job-shop batch characteristics. Given the available machines, the list of production orders, the products' due-dates and process description for each product, the system sequences machines for all required products, using an heuristic approach based on the information of the status of the plant and the characteristics of the orders. This work describes in detail several concepts defined in this project. A qualitative analysis of the advantages and disadvantages of these ideas is given, and empirical results are exhibited using real data from the plant. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

KBSS: a knowledge-based job-shop scheduling system*

Abstract Abstract. A knowledge based job-shop scheduling system lKBSSr, which employs artificial intelligence techniques to solve general job-shop problems, is presented. The system consists of two main components: a knowledge base lKBSS modelr and a control mechanism. The KBSS model has a three-level architecture which captures, in frames, knowledge about the job-shop domain and the characteristics of individual job-shop problems. The control mechanism consists of two modules: the primary scheduler lPSr and the heuristic scheduler lHSr. PS is a rule-based scheduler built around a time-transcending heuristic which attempts to minimize the makespan and meet the due dates. If PS fails to meet the due dates then HS applies a combination of Aa and beam search techniques in the space of alternative solutions and returns the best alternative it finds. The emphasis on due dates is a distinctive feature of KBSS. Experience with evaluating the presented scheduling system...