Robert L. Burdett

A railway capacity determination model and rail access charging methodologies

This article discusses approaches to the determination of railway capacity and the significance of the following factors on capacity: mix of trains, length and weight of trains, direction of train travel, acceleration and deceleration, stopping protocols of trains, location and length of crossing loops, location of signals, length of sections, dwell times and sectional running times. A more accurate method to calculate railway capacity is developed using previously unaddressed aspects for capacity determination. Capacity and pricing are two key issues for organizations involved with open track access regimes. A train access charging methodology is therefore developed and incorporated into a railway capacity determination model.

Block models for improved earthwork allocation planning in linear infrastructure construction

Earthwork planning is considered in this article and a generic block partitioning and modelling approach is devised to provide strategic plans of various levels of detail. Conceptually, this new approach is more accurate and comprehensive than others, for instance those that are section based. In response to recent environmental concerns, the metric for decision making was fuel consumption or emissions. Haulage distance and gradient, however, are important components of these metrics and are also included. Advantageously, the fuel consumption metric is generic and captures the physical difficulties of travelling over inclines of different gradients, which is consistent across all hauling vehicles. For validation, the proposed models and techniques are applied to a real-world road project. The numerical investigations demonstrate that the models can be solved with relatively little CPU time. The proposed block models also result in solutions of superior quality, i.e. they have reduced fuel consumption and cost. Furthermore, the plans differ considerably from those based solely on a distance-based metric, thus demonstrating a need for the industry to reflect on its current practices.

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

Techniques for restricting multiple overtaking conflicts and performing compound moves when constructing new train schedules

In this paper a discrete sequencing approach for train scheduling is extended firstly by incorporating essential composite perturbation operations and secondly by restricting unnecessary multiple overtaking. Unnecessary multiple overtaking occurs when a train overtakes another train using a siding but is itself overtaken at a later time on a different siding by the train that it previously passed. Compound perturbation operations may be needed in order to restrict precedence impossibilities from occurring when passing facilities do not separate adjacent sections of rail since the sequences are linked by common precedences. Both features affect the sequencing process and can significantly improve the solution quality if handled efficiently and correctly. From a numerical investigation significant benefits are demonstrated on benchmark problems of a previous paper.

Scheduling Trains on Parallel Lines with Crossover Points

In this article a sequencing approach is proposed for train scheduling on parallel lines separated by crossover points. The primary feature that is introduced is a modeling device called a compound buffer that is very powerful, particularly because it has widespread applicability. It may be used to maintain the correct occupancy levels of lines while allowing trains to pass through the crossover points without additional routing decisions. The compound buffer is a collection of machines that collectively acts as a traditional capacitated buffer. The machines that are part of the compound buffer, however, maintain their independence and are not treated differently in the scheduling process. To demonstrate the validity and effectiveness of this new approach, a variety of typical railway infrastructure were considered. Extensive numerical investigations show that the approach successfully and consistently creates train schedules of high quality. It also shows that compound buffers accurately portray the technical constraints of the real system.

Performance profiling for predictive train schedules

In this article an alternate sensitivity analysis is proposed for train schedules. It characterises the schedules robustness or lack thereof and provides unique profiles of performance for different sources of delay and for different values of delay. An approach like this is necessary because train schedules are only a prediction of what will actually happen. They can perform poorly with respect to a variety of performance metrics, when deviations and other delays occur, if for instance they can even be implemented, and as originally intended. The information provided by this analytical approach is beneficial because it can be used as part of a proactive scheduling approach to alter a schedule in advance or to identify suitable courses of action for specific “bad behaviour”. Furthermore this information may be used to quantify the cost of delay. The effect of sectional running time (SRT) deviations and additional dwell time in particular were quantified for three railway schedule performance measures. The key features of this approach were demonstrated in a case study.

Sequencing and scheduling in flowshops with task redistribution

In some manufacturing situations, station tasks or operations can be shifted or redistributed to adjacent stations. This can be done when these stations have the appropriate equipment, and the workers on that station can perform the shifted work to a reasonable level of competency. This paper addresses such an environment and provides a general framework for applying the shifting or redistribution of tasks methodology to the intermediate storage, no-intermediate storage and no-wait flowshop problems. The outcome of this research is a way in which to utilise more efficiently the general-purpose facilities of this type of production environment. It includes mathematical models, recurrence equations and solution techniques for sequencing and scheduling. From an extensive numerical investigation, the benefits achieved by the application of this methodology are detailed.

A multi-criteria approach for hospital capacity analysis

Hospitals are critical elements of health care systems and analysing their capacity to do work is a very important topic. To perform a system wide analysis of public hospital resources and capacity, a multi-objective optimization (MOO) approach has been proposed. This approach identifies the theoretical capacity of the entire hospital and facilitates a sensitivity analysis, for example of the patient case mix. It is necessary because the competition for hospital resources, for example between different entities, is highly influential on what work can be done. The MOO approach has been extensively tested on a real life case study and significant worth is shown. In this MOO approach, the epsilon constraint method has been utilized. However, for solving real life applications, with a large number of competing objectives, it was necessary to devise new and improved algorithms. In addition, to identify the best solution, a separable programming approach was developed. Multiple optimal solutions are also obtained via the iterative refinement and re-solution of the model.

EVOLUTIONARY ALGORITHMS FOR RESOURCE CONSTRAINED NON-SERIAL MIXED FLOW SHOPS

In this paper the resource-constrained flow shop (RCF) problem is addressed. A number of realistic extensions are incorporated, including non-serial precedence requirements, mixed flow shop situations, and the distribution of the human workforce among a number of pre-determined groups. The RCF is then solved by meta-heuristics, primarily of the evolutionary type. An extensive numerical investigation, including a case study of a particular industrial situation, details the implementation and execution of the heuristics, and the efficiency of the proposed algorithms.

An integrated approach for scheduling health care activities in a hospital

To effectively utilise hospital beds, operating rooms (OR) and other treatment spaces, it is necessary to precisely plan patient admissions and treatments in advance. As patient treatment and recovery times are unequal and uncertain, this is not easy. In response, a sophisticated flexible job-shop scheduling (FJSS) model is introduced, whereby patients, beds, hospital wards and health care activities are respectively treated as jobs, single machines, parallel machines and operations. Our approach is novel because an entire hospital is describable and schedulable in one integrated approach. The scheduling model can be used to recompute timings after deviations, delays, postponements and cancellations. It also includes advanced conditions such as activity and machine setup times, transfer times between activities, blocking limitations and no wait conditions, timing and occupancy restrictions, buffering for robustness, fixed activities and sequences, release times and strict deadlines. To solve the FJSS problem, constructive algorithms and hybrid meta-heuristics have been developed. Our numerical testing shows that the proposed solution techniques are capable of solving problems of real world size. This outcome further highlights the value of the scheduling model and its potential for integration into actual hospital information systems.