Intelligent Scheduling System Research Articles

Scheduling printed circuit board production systems using the two-level scheduling approach

Abstract In a multiple flowline manufacturing system, such as a typical printed circuit board (PCB) production system, scheduling tasks include determining due dates of customer orders, assigning products to daily production, and releasing boards to production lines each day. The main challenge for the scheduling system is to coordinate different production resources so they can work together to improve system performance. The Bi-Level Intelligent Scheduling System (BLISS) was developed, using a two-level scheduling approach integrating product-level and board-level scheduling, to schedule jobs for PCB manufacturing systems under a daily through weekly planning horizon. A utility function is added to determine lot size, and four sequencing rules are evaluated to find the most suitable rule for the scheduling schemes under study. The lot sizing and sequencing decisions are integrated with the simulation module to generate more accurate schedules, with special emphasis put on the need for coordination. Performance of the two-level scheduling approach is evaluated and compared with that of using product-level or board-level scheduling alone under a variety of manufacturing scenarios. The performance is analyzed under various environments such as different degrees of due date tightness, order variation, and product variation. Results show the strength of the two-level scheduling approach, especially in “mission-critical” situations of tight due dates, high variation of orders, and highly changeable products. The findings also provide guidelines for applying those scheduling schemes effectively and efficiently.

SCaRC: Space-Constrained Resource-Constrained Scheduling System

Current intelligent scheduling systems do not address the effect of limited work space on the generation of schedules. This paper describes SCaRC—Space-Constrained and Resource-Constrained knowledge-based system—for scheduling repetitive floors in multistory projects. The prototype system incorporates space-based scheduling techniques to recognize the space requirements of activities as a scheduling constraint. The space-based techniques are integrated with resource-based techniques for the production of schedules. The system also incorporates procedures to define and verify construction logic constraints associated with linear scheduling of vertical construction. An example illustrating the scheduling techniques implemented in SCaRC is presented.

Intelligent scheduling: past, present and future

It has been more than a decade since the first intelligent scheduling system ISIS was reported. Numerous intelligent scheduling systems have been developed since then, in the USA and in Europe, new search techniques have been invented, new theoretical results are emerging, and scheduling applications have been developed from general-purpose toolkits. This paper summarises achievements in intelligent scheduling over the decade and takes a brief look forward.

A comparison of two intelligent scheduling systems for flexible manufacturing systems

Despite the existence of hardware suitable for the development of advanced automated manufacturing systems, the implementation of such systems has been hampered by the lack of appropriate software necessary for the scheduling and control of these systems. Artificial Intelligence (AI) has been suggested as a methodology suited to the development of this software. As a result, in this paper two intelligent scheduling and control systems are developed with the cooperation of an “expert” at an existing FMS in Aiken, South Carolina, USA. The literature related to scheduling FMS using AI methodologies is unclear as to whether scheduling should be done in a real-time manner similar to simple job-shop scheduling or in a predictive manner that shows detailed start times and finish times for some scheduling horizon. As such, one intelligent scheduler developed in this research utilizes a real-time scheduling methodology, while the second utilizes a predictive methodology. Both systems were developed in conjuction with a scheduling expert at the FMS. Results from a simulation model of the FMS using each of the two scheduling methodologies are compared in an effort to address the issue of which methodology is better suited for the scheduling and control of automated manufacturing systems such as FMS.

Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion

Although a significant amount of research has been carried out in the scheduling of flexible manufacturing systems (FMSs), it has generally been focused on developing intelligent scheduling systems. Most of these systems use simple scheduling rules as a part of their decision process. While these scheduling rules have been investigated extensively for a job shop environment, there is little guidance in the literature as to their performance in an FMS environment. This paper attempts to investigate the performances of machine and AGV scheduling rules against the mean flow-time criterion. The scheduling rules are tested under a variety of experimental conditions by using an FMS simulation model.

Intelligent scheduling of automated machining systems

An automated machining system involves concurrent use of manufacturing resources, alternative process plans and flexible routings. High investment in the installation of automated facilities requires an efficient scheduling system that is able to allocate the resources specified for operations over a scheduling horizon. The primary emphasis of this paper is to generate schedules that accurately reflect details of the automated environment and the objectives stated for the system. In this paper, a rule for dispatching operations, named the Most Dissimilar Resources (MDR) dispatching rule, is introduced. A scheduling algorithm for automated machining is presented. Using the previous simulation research for this topic, a rule-based scheduling system is constructed. An architecture for an intelligent scheduling system is proposed, and the system has a high potential to provide efficient schedules based on the task-specific knowledge for the dynamic scheduling environment.

Modeling and scheduling of flexible manufacturing systems using timed/stochastic petri nets

This paper deals with methodologies for modeling and scheduling of Flexible Manufacturing Systems (FMS) which are the typical discrete event dynamic systems. Since Petri nets provide a powerful tool for modeling dynamical behavior of discrete concurrent processes, we use them for modeling FMS. For the purpose of off-line scheduling of FMS we use timed Petri nets, and for on-line real-time scheduling of FMS we use timed-stochastic Petri nets with hierarchical structure where we deal with uncertain events in FMS such as variation of processing time, failure of machine tools, variation of repairing time, and so forth.We deal with off-line and on-line rule-based scheduling. The role of the rule base is to generate an appropriate priority rule for resolving conflicts, that is for selecting one of enabled transitions to be fired in a conflict set of the Petri net. This corresponds to select a part type to be processed in FMS.Towards developing more Intelligent Manufacturing Systems (IMS) we propose a conceptual framework of a futuristic intelligent scheduling system.

A knowledge-based system for stacker crane control in a manufacturing environment

A knowledge-based intelligent crane scheduling (INCS) system is developed for controlling a stacker crane in a CIM environment where PCBs are made. INCS involves two types of rules, one for cyclic scheduling, which is shown to be near optimal if there is no machine breakdown, and another for handling machine breakdowns, which adjusts the cyclic schedule to real-time disturbances. After briefly reviewing the literature on knowledge-based systems for manufacturing, the authors describe the problem environment. A framework is used to develop not only INCS but also several other knowledge-based systems that use dispatching rules in place of cyclic schedule. The knowledge-based systems are tested by using data from an existing CIM environment, the feasibility and superiority of INCS are demonstrated, and its advantages and disadvantages are discussed. >

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.

Intelligent planning and scheduling systems

Intelligent planning and scheduling systems