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.     

Evolution of railway network flexibility: The Spanish broad gauge case

The length of the Spanish broad gauge network has decreased in the 1956–2006 period. When looking at different railway maps through this period, it seems that the network is offering fewer and fewer alternatives when a line is cut, that is, the network is becoming less and less flexible. The goal of this article is to prove that the flexibility of the Spanish broad gauge network has decreased substantially in the 1956–2006 period. We have considered the network as a graph (ignoring traditional railway quality indicators such as commercial speed, number of tracks, electrifications, signaling, …) and we have chosen two simple indicators as accurate in this sense: the number of cycles (cycles provide an alternative to reach a station if there is a problem in one line) and the number of stations of intermediate degree. To achieve this, we have developed a piece of software that is an ad hoc extension of Maple’s networks package.     

Managing incidents in a complex system: a railway case study

The authors of this paper review how complex entities, composed of many interdependent subsystems, such as international rail operators, can improve their ability to recover from incidents through the better management of key interfaces. The principles of Normal Accident Theory and resilience engineering are discussed, and the case study of the Eurostar incident of 18–19 December 2009 is considered in detail. Lessons learnt from resilience engineering are applied to the case study to extract recommendations by which incident management for open access international rail transport may be improved.     

A simulated annealing algorithm for multi-agent systems: a job-shop scheduling application

In this paper, a parallel implementation of the modular simulated annealing algorithm for classical job-shop scheduling is presented. The implementation is for a multi agent system running on the distributed resource machine, which is a novel, scalable, distributed virtual machine based on Java technology. The problems tackled are well known, difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit time. The empirical results obtained show that the method proposed is successful in comparison with a sequential version of modular simulated annealing algorithm and other methods described in the literature.     

A model predictive control approach for discrete-time rescheduling in complex central railway station areas

Railway networks are operated more and more at capacity margins, schedules are becoming more susceptible to disturbances, and delays propagate and hamper the service level experienced by the customers. As a consequence railway traffic management is becoming increasingly challenging, thus motivating the development of computer-aided systems. This paper proposes a dispatching assistant in the form of a model predictive control framework for a complex central railway station area. The closed-loop discrete-time system suggests rescheduling trains according to solutions of a binary linear optimization model. The model assigns precomputed blocking-stairways to trains while respecting resource-based clique constraints, connection constraints, platform related constraints and consistency constraints with the objective of maximizing customer satisfaction. In collaboration with the Swiss Federal Railways (SBB), the approach was successfully applied for an operational day at the central railway station area Berne, Switzerland. The model is capable of considering many alternative routing possibilities and departure timings, a potential of our approach, which can also be deduced from computational results.     

A multi-agent-based approach for personnel scheduling in assembly centers

This paper presents a multi-agent-based approach for personnel scheduling problems in the context of a paced multi-product assembly center. Our purpose is to elaborate daily assignment of employees to workstations in order to minimize simultaneously the operational costs and personnel dissatisfactions. The proposed approach considers the individual competencies, mobility and preferences of each employee, as well as the personnel and competency requirements associated with each assembly activity given both the current master assembly schedule and the line balancing for each product. To benchmark the performance of the multi-agent approach, we use optimal solutions obtained through a linear programming model resolution using a commercial solver. Experimental results show that our multi-agent approach can produce high-quality and efficient solutions in a short computational time.     

A logic approach to decision taking in a railway interlocking system using Maple

Railway interlocking systems are apparatuses that prevent conflicting movements of trains through an arrangement of tracks. A railway interlocking system takes into consideration the position of the switches (of the turnouts) and does not allow trains to be given clear signals unless the routes to be used by the trains do not intersect. A new model, based on Boolean Logic, and independent from the topology of the station is presented in this paper. According to this new model, any given proposed situation is safe if and only if a certain set of formulae (translating the position of trains and the movements allowed - the latter depend on the position of the switches and the colour of the semaphores) is consistent. The main procedure analyses the safety of a proposed situation and returns, if they exist, the sections where a collision could take place. The fact that trains could occupy more than one section is considered. The code of the corresponding Maple implementation is surprisingly brief.     

A knowledge-based system for reactive scheduling decision-making in FMS

This paper describes research into the development of an intelligent simulation environment. The environment was used to analyze reactive scheduling scenarios in a specific flexible manufacturing systems (FMS) configuration. Using data from a real FMS, simulation models were created to study the reactive scheduling problem and this work led to the concept of capturing instantaneous FMS status data as snapshot data for analysis. Various intelligent systems were developed and tested to asses their decision-making capabilities. The concepts of History Logging and expert system learning is proposed and these ideas are implemented into the environment to provide decision-making and control across a FMS schedule lifetime. This research proposes an approach for the analysis of reactive scheduling in an FMS. The approach and system that was subsequently developed was based on the principle of automated intelligent decision-making via knowledge elicitation from FMS status data, together with knowledge base augmentation to facilitate a learning ability based on past experiences.     

Knowledge based scheduling systems: a framework

Scheduling is a complex problem which occurs often in a manufacturing environment. There are many possible variations within the context of a scheduling problem, enough to overwhelm anyone but an expert. A production manager, faced with the task of job scheduling for a particular process, might well benefit from the advice of a scheduling expert. Even though the scheduling literature is full of optimizing rules for a variety of manufacturing environments few, if any, of these rules have been used in practice. The bewildering variety of theoretical results, the lack of a mechanism to match theoretical results to practical problems and the difficulty in establishing the practical efficacy of the theoretical results are among the reasons for this gap between scheduling theory and practice. This paper describes a scheduling expert system framework which addresses these problems and helps bridge this gap. The solution techniques available in SCHEDEX produce a specific schedule for the problem according to a procedure which has been mathematically proven to provide a satisfactory and often optimal solution, given the criteria for the problem. A simulation model also tests for the effectiveness of a selected technique with respect to the chosen performance measure(s).     

A decision support system for personnel scheduling in a manufacturing environment

A Decision Support System (DSS) is developed to support managers in the task of scheduling labor in the area of manufacturing. The DSS is designed to generate labor requirements by worker category and work center based on master production schedules. It is a PC-based, menu-driven program that generates a capacity plan based on data supplied by the user of the system.     

人员定位系统在水电工程施工过程中的应用与研究

本文主要以目前国内外应用最为广泛的基站定位技术、LBS定位技术、差分GPS定位技术和BLE定位技术及大数据处理分析技术作为研究对象,对在水电施工建设中的应用做了研究,测试结果表明,定位精度和系统时延满足水电建设管理的需求,可在水电建设中推广应用.     

Developing a neural network approach for intelligent scheduling in GUESS

The generically used expert scheduling system (GUESS) intelligent scheduling toolkit has been built and applied in various scheduling domains. Previously, GUESS included three major scheduling approaches: a heuristic-based (suggestion tabulator) approach, a hill-climbing algorithm, and a genetic algorithm approach. GUESS has now been expanded to include a neural network scheduling approach. This paper discusses the development, implementation and testing results of the neural network scheduling method within GUESS.     

Distributed and centralised material handling scheduling: Comparison and results of a simulation study

Part of a larger research that employs decentralized holonic modelling techniques in manufacturing planning and control, this work proposes a holonic-based material handling system and contrasts the centralized and distributed scheduling approaches for the allocation of material handling operations to the available system resources. To justify the use of the decentralized holonic approach and assess its performance compared to conventional scheduling systems, a series of evaluation tests and a simulation study are carried out. As illustrated by the results obtained from the simulation study, the decentralized holonic approach is capable of delivering competitive feasible solutions in, practically, real-time.     

Physical simulation of work scheduling rules in a flexible manufacturing system

This paper describes a physical simulator of an actual flexible manufacturing system. The simulator was used to evaluate work scheduling rules for both part selection and machine selection. Twenty-eight decision rule sets were simulated and evaluated under six major performance criteria.     

AJ-System柔性辅助调度系统的设计及其开发

随着越来越多的企业采用多品种小批量的生产方式,企业生产过程日趋复杂化,如何进行生产作业计划调度是一个迫需解决的问题。从柔性设计的角度出发,围绕一个具体机械加工车间的实际生产状况,分别从规则库设计、启发式算法以及界面设计3个方面详细阐述了AJ-System系统的柔性设计方法,并简单介绍了系统的框架及其应用。这种柔性设计方法也为其它相关系统的开发提供借鉴。     

Implementation of a computer job scheduling algorithm: A case history

Multiprogramming systems require that a fair, equitable algorithm be used for the scheduling of jobs. This paper discusses some of the problems associated with this and proposes an automatic job scheduling algorithm. The major parts of the algorithm have been implemented and have been in use for over one year. The user interface is simplified and the operational complexities are minimized. The parameters used for the algorithm are the estimates of the central processor time and the memory required by the job. All types of jobs including those requiring operator attention during execution are covered under the scheme. Operational data and the reactions from the users indicate that the results have been as expected.     

Combined planning and scheduling in a divergent production system with co-production: A case study in the lumber industry

Many research initiatives carried out in production management consider process planning and operations scheduling as two separate and sequential functions. However, in certain contexts, the two functions must be better integrated. This is the case in divergent production systems with co-production (i.e. production of different products at the same time from a single product input) when alternative production processes are available. This paper studies such a context and focuses on the case of drying and finishing operations in a softwood lumber facility. The situation is addressed using a single model that simultaneously performs process planning and scheduling. We evaluate two alternative formulations. The first one is based on mixed integer programming (MIP) and the second on constraint programming (CP). We also propose a search procedure to improve the performance of the CP approach. Both approaches are compared with respect to their capacity to generate good solutions in short computation time.     

Pre-run-time scheduling in real-time systems: Current researches and Artificial Intelligence perspectives

This paper presents the taxonomy of real-time systems with special emphasize on pre-run-time scheduling problem. Firstly, we present real-time systems, real-time tasks, timing, precedence and exclusion constraints. Then, we describe the problem of pre-run-time scheduling of tasks under constraints. After that, we present the most existing efficient techniques to deal with the latter problem. We summarize the discussion of existing techniques and possible research perspectives after surveying the Artificial Intelligence’s point of view about the problem of pre-run-time scheduling of real-time tasks. The Artificial Intelligence survey includes Constraint Satisfaction Problems class since pre-run-time scheduling belongs to the latter class. The Artificial Intelligence survey includes also Path-finding Problems from which intelligent algorithms could be observed such as Learning-Real-Time-A1(LRTA1) thanks to its important properties (optimality, linear space complexity and determinism). The development of an algorithm like LRTA1 to solve Constraints Satisfaction Problems and particularly the pre-run-time scheduling of real-time tasks problem is one clear research direction to deal with large-scale real-time systems. The overall objective of this paper is to show what are the perspectives to Artificial Intelligence literature that could be beneficial firstly to Artificial Intelligence community itself and secondly to real-time systems community.     

DFTS: A dynamic fault-tolerant scheduling for real-time tasks in multicore processors

This paper presents a dynamic scheduling for real-time tasks in multicore processors to tolerate single and multiple transient faults. The scheduling is performed based on three important issues: (1) current released tasks, (2) current available processor cores, and (3) consideration of the number of faults and their occurrences. Using tasks utilization along with a defined criticality threshold in the proposed scheduling method, current ready tasks are divided into critical- and noncritical ones. Based on whether a task is critical or noncritical, an appropriate fault-tolerance policy is exploited. Moreover, scheduling decisions are made to fulfill two key goals: (1) increasing scheduling feasibility and (2) decreasing the total tasks execution time. Several simulation experiments are carried out to compare the proposed method with two well-known methods, called checkpointing with rollback recovery and hardware replication. Experimental results reveal that in the presence of multiple transient faults, the feasibility rate of the proposed method is considerably higher than the other well-known fault-tolerance methods. Moreover, the average timing overhead of this method is lower than the traditional methods.