Knowledge-based system for selection of an AGV and a workcentre for transport of a part in on-line scheduling of FMS

The operational level of flexible manufacturing systems FMS is concerned with the detailed decision making required for real-time operation. The real-time control of FMS is extremely complex. On-line scheduling is referred to as real-time control of FMS. On-line scheduling of an FMS requires decision making in various scheduling problems such as selection of an AGV and a workcentre from a set of workcentres simultaneously requesting the service for transport of a part, selection of a new part type to be released into the system, etc. This paper describes a prototype knowledge-based system for selection of an AGV and a workcentre from a set of workcentres simultaneously requesting the service for transport of a part in on-line scheduling of FMS. The knowledge-based system is evaluated by the empirical approach. On-line scheduling of FMS gives potential advantages such as productivity and low throughput time.     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

Operations research in the management of the airlines

This paper is intended to present an introduction to the use of operations research in the international airline industry, and to demonstrate the scope and significance of the airline OR activities. First, the special reasons for the viability and spread of airline OR are discussed. Then a functional framework for an airline is outlined, to be used in analyzing the problems of an airline and relating the OR work to it. Four major applications are described, corresponding to four major components of the framework. These applications are: schedule development, overbooking control, crew scheduling and engine management, respectively. In each case we describe the problem and its significance, indicate the types of solution techniques which have been developed, and assess the implemented solutions. The paper concludes with comments on the current state-of-the-art and the future of airline OR. A detailed bibliography is given.Both of the authors have been operations research directors in major airlines and more recently have focused their attention upon the industry as academic researchers. This background has provided us with an unusual opportunity to analyze the role of OR in the airlines, and our paper seeks to share with the reader some of the insights we have gained thereby.     

Development and implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process

Abstract

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.     

A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation

The hybrid flowshop scheduling (HFS) problem with the objective of minimising the makespan has important applications in a variety of industrial systems. This paper presents an effective discrete artificial bee colony (DABC) algorithm that has a hybrid representation and a combination of forward decoding and backward decoding methods for solving the problem. Based on the dispatching rules, the well-known NEH heuristic, and the two decoding methods, we first provide a total of 24 heuristics. Next, an initial population is generated with a high level of quality and diversity based on the presented heuristics. A new control parameter is introduced to conduct the search of employed bees and onlooker bees with the intention of balancing the global exploration and local exploitation, and an enhanced strategy is proposed for the scout bee phase to prevent the algorithm from searching in poor regions of the solution space. A problem-specific local refinement procedure is developed to search for solution space that is unexplored by the honey bees. Afterward, the parameters and operators of the proposed DABC are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is conducted, with the best performing algorithms presented for the HFS problem under consideration, and with adaptations of some state-of-the-art metaheuristics that were originally designed for other HFS problems. The results show that the proposed DABC performs much better than the other algorithms in solving the HFS problem with the makespan criterion.     

Combinatorial Optimization in Real-Time Scheduling: Theory and Algorithms

Real-time computer systems are essential for many applications, such as robot control, avionics, medical instrumentation, manufacturing, etc. The correctness of the system depends on the temporal correctness as well as the functional correctness of the task executions. In order to assure temporal correctness it is necessary that the resources be scheduled to meet the temporal requirements of applications. When we consider the problem of nonpreemptive scheduling of a set of tasks in a processor for which no feasible solution exists, some tasks may have to be rejected so that a schedule can be generated for the rest. In this paper, we consider the problem of generating an optimal schedule such that the number of rejected tasks is minimized, and then the finish time is minimized for the accepted tasks. We propose to use an analytic approach to solve this problem. We first discuss the super sequence based technique which was originally proposed for reducing the search space in testing the feasibility of a task set. Then we show by the Conformation theorem that the super sequence constructed from the task set also provides a valid and reduced search space for the optimization problem. While the complexity of our scheduling algorithm in the worst case remains exponential, our simulation results show that the cost is reasonable for the average case.     

论知识经济时代的人力资源管理

结合世界发达国家的知识经济的兴起 ,阐述了知识经济的特征及发展现状 ;分析了知识经济时代对人力资源要求的基本特征 ;进而 ,结合国情 ,提出了我国发展知识经济框架下人力资源管理的模式和策略 .它们可作为人力资源管理的高层决策的依据和参考 .     

Adaptive/predictive scheduling: review and a general framework

Abstract

Abstract. Effective production scheduling can have a dramatic impact on manufacturing productivity. In modern automated manufacturing systems, the ability to revise and adjust the schedule can help reduce indirect costs which comprise a large portion of manufacturing costs in those systems. The objective of the research discussed here has been to study the feasibility of automatically scheduling multi-machine complexes and adjusting the schedule on a real-time basis by a unified computer system. We arc investigating selective real-time schedule adaptation by distinguishing between schedule generation and regeneration tasks (which might not be cost-effective) from schedule adaptation/recovery tasks (basically, causing less cosily disruption of the original plans). The article describes our general framework for automatic adaptive/predictive scheduling that includes five main functions: scheduler; monitor;comparator; resolvcr; recovery adaptor. Some experimental results demonstrating the effectiveness of the framework are discussed.     

Dynamic scheduling of steel casting and milling using multi-agents

This paper presents a case study on the use of multi-agents for integrated dynamic scheduling of steel milling and casting. Steel production is an extremely complex problem requiring the consideration of several different constraints and objectives of a range of processes in a dynamic environment. Most research in steel production scheduling considers static scheduling of processes in isolation. In contrast to earlier approaches, the multi-agent architecture proposed consists of a set of heterogeneous agents which integrate and optimize a range of scheduling objectives related to different processes of steel production, and can adapt to changes in the environment while still achieving overall system goals. Each agent embodies its own scheduling model and realizes its local predictive-reactive schedule taking into account local objectives, real-time information and information received from other agents. Agents cooperate in order to find a globally good schedule, which is able to effectively react to real-time disruptions, and to optimize the original production goals whilst minimising disruption carried by unexpected events occurring in real-time. The inter-agent cooperation is based on the Contract Net Protocol with commitment.     

An integrated survey of deterministic project scheduling

There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.     

The Use of Stochastic Simulation in Knowledge-Based Systems

Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge-based systems.     

Production control for just-in-time tool-and-die manufacturing

The strategy to implement production control varies in different types of manufacturing systems. We address the issue of production control in unit-based manufacturing systems where the production batch size of the final product is one or two, the product is custom designed and is made up of numerous individual components with similar process routeings lor special machine tools, turbines, boilers, tool-and-die, injection moulding equipment, custom fabrication, etc. We have developed algorithms to generate master production schedules in this particular domain of manufacturing systems using the concept of workgroups. We show how a Kanban-based JIT shopfloor control and purchasing system can be implemented in such systems in tandem with an inventory management system by utilizing the concept of demand lists. We also propose a model for practical implementation of production control in this domain. The methodology is illustrated at a tool-and-die plant where a CIM software is used to apply the JIT-based production control. Preliminary results show a significant reduction in lead times. We try to show the relationship between research and industrial applications of productions of production control and how to bridge the gap between them.     

The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

Opportunities for research about managing the knowledge-based enterprise

Potentially valuable directions for new research into the management of knowledge-based enterprises are identified in this paper. This was done by reviewing relevant literature to develop research questions, using a model of knowledge-based capabilities to focus the review. The model highlights six knowledge capabilities: acquisition, creation, capture, storage, diffusion and transfer. A knowledge-based enterprise would have to engage in (if not excel at) these activities simply to manage its key resource – knowledge. Forty-two research questions were proposed based on the review. The focus of the research questions varies widely, representing potential opportunities for researchers from many different areas to further our understanding of managing knowledge-based enterprises.     

TWO DESIGN PRINCIPLES FOR KNOWLEDGE-BASED SYSTEMS*

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem-solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role.     

Heuristic Estimation of the Efficient Frontier for a Bi-Criteria Scheduling Problem

We examine a single-machine scheduling problem where the objective is to minimize the mean and the variance of the job completion times simultaneously. We seek to identify the efficient frontier which is obtained by parametrically solving a weighted combination of the two criteria. The identification of the true efficient frontier for this problem is notoriously difficult. To estimate the frontier, we propose a heuristic procedure which is quite general and can be applied to other bi-criteria problems as well. It involves repeated applications of a relatively new technique called beam search in an adaptive manner. To evaluate the proposed procedure, we introduce two measures of performance and conduct a computational study. The results of the study indicate that the procedure is highly effective.     

Lot sizing and scheduling: a survey of practices in Brazilian companies

The lot sizing and scheduling (LSS) problem has motivated much research in production planning and control. However, while there are many papers on modelling this problem, there is scant research on how it is addressed in the industry. We present a survey of Brazilian industry practices in LSS, intending to understand the problem faced by operations managers, drawing parallels with academic findings to identify gaps between theory and practice and opportunities for future research. We confirm that the main objective of practitioners in companies operating under MTS strategy is to maximise service level, followed by inventory reduction. Availability of raw material, demand uncertainty and safety stock levels, lack of decision support software and the impact of sequence-dependent set-up times are other major concerns highlighted by the practitioners. Lastly, we confirmed that despite the relevant body of research on this problem, there is still a large gap between theory and practice.     

A simple linear time approximation algorithm for multi-processor job scheduling on four processors

Multiprocessor job scheduling problem has become increasingly interesting, for both theoretical study and practical applications. Theoretical study of the problem has made significant progress recently, which, however, seems not to imply practical algorithms for the problem, yet. Practical algorithms have been developed only for systems with three processors and the techniques seem difficult to extend to systems with more than three processors. This paper offers new observations and introduces new techniques for the multiprocessor job scheduling problem on systems with four processors. A very simple and practical linear time approximation algorithm of ratio bounded by 1.5 is developed for the multi-processor job scheduling problem P ₄|fix|C _max, which significantly improves previous results. Our techniques are also useful for multiprocessor job scheduling problems on systems with more than four processors.     

Inter-city bus scheduling under variable market share and uncertain market demands

Bus scheduling is essential to a carrier's profitability, its level of service and its competitiveness in the market. In past research most inter-city bus scheduling models have used only the projected (or average) market share and market demand, meaning that the variations in daily passenger demand that occur in actual operations are neglected. In this research, however, we do not utilize a fixed market share and market demand. Instead, passenger choice behaviors and uncertain market demands are considered. Stochastic and robust optimizations and a passenger choice model are used to develop the models. These models are formulated as a nonlinear integer program that is characterized as NP-hard. We also develop a solution algorithm to efficiently solve the models. They are tested using data from a major Taiwan inter-city bus operation. The results show the good performance of the models and the solution algorithm.     

A flight scheduling model for Taiwan airlines under market competitions

In this research, we develop a short-term flight scheduling model with variable market shares in order to help a Taiwan airline to solve for better fleet routes and flight schedules in today's competitive markets. The model is formulated as a non-linear mixed integer program, characterized as an NP-hard problem, which is more difficult to solve than the traditional fixed market share flight scheduling problems, often formulated as integer/mixed integer linear programs. We develop a heuristic method to efficiently solve the model. The test results, mainly using the data from a major Taiwan airline's operations, show the good performance of the model and the solution algorithm.