Determining operations affected by delay in predictive train timetables

Constructing train schedules is vital in railways. This complex and time consuming task is however made more difficult by additional requirements to make train schedules robust to delays and other disruptions. For a timetable to be regarded as robust, it should be insensitive to delays of a specified level and its performance with respect to a given metric, should be within given tolerances. In other words the effect of delays should be identifiable and should be shown to be minimal. To this end, a sensitivity analysis is proposed that identifies affected operations. More specifically a sensitivity analysis for determining what operation delays cause each operation to be affected is proposed. The information provided by this analysis gives another measure of timetable robustness and also provides control information that can be used when delays occur in practice. Several algorithms are proposed to identify this information and they utilise a disjunctive graph model of train operations. Upon completion the sets of affected operations can also be used to define the impact of all delays without further disjunctive graph evaluations.

A hybrid constructive heuristic and simulated annealing for railway crew scheduling

Railway crew scheduling problem is the process of allocating train services to the crew duties based on the published train timetable while satisfying operational and contractual requirements. The problem is restricted by many constraints and it belongs to the class of NP-hard. In this paper, we develop a mathematical model for railway crew scheduling with the aim of minimising the number of crew duties by reducing idle transition times. Duties are generated by arranging scheduled trips over a set of duties and sequentially ordering the set of trips within each of duties. The optimisation model includes the time period of relief opportunities within which a train crew can be relieved at any relief point. Existing models and algorithms usually only consider relieving a crew at the beginning of the interval of relief opportunities which may be impractical. This model involves a large number of decision variables and constraints, and therefore a hybrid constructive heuristic with the simulated annealing search algorithm is applied to yield an optimal or near-optimal schedule. The performance of the proposed algorithms is evaluated by applying computational experiments on randomly generated test instances. The results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time for large-sized problems.

A demand-responsive decision support system for coal transportation

In this paper, a demand-responsive decision support system is proposed by integrating the operations of coal shipment, coal stockpiles and coal railing within a whole system. A generic and flexible scheduling optimisation methodology is developed to identify, represent, model, solve and analyse the coal transport problem in a standard and convenient way. As a result, the integrated train-stockpile-ship timetable is created and optimised for improving overall efficiency of coal transport system. A comprehensive sensitivity analysis based on extensive computational experiments is conducted to validate the proposed methodology. The mathematical proposition and proof are concluded as technical and insightful advices for industry practice. The proposed methodology provides better decision making on how to assign rail rolling-stocks and upgrade infrastructure in order to significantly improve capacity utilisation with the best resource-effectiveness ratio. The proposed decision support system with train-stockpile-ship scheduling optimisation techniques is promising to be applied in railway or mining industry, especially as a useful quantitative decision making tool on how to use more current rolling-stocks or whether to buy additional rolling-stocks for mining transportation.

Mindfulness-meditation on the move : implementation of an ACT-based mindfulness practice intervention and training within a University Dance program

As part of the introduction of a broader dance medicine and science related health and wellbeing program, a 9 week mindfulness-meditation ACT-based program was delivered to all students undertaking full-time University dance training (N = 106). The aim of the program was to assist students in the further development of performance psychology skills that could be applied in both performance and non-performance settings. Participant groups were comprised of both male (N = 12) and female (N = 94) students from across all three year levels of two undergraduate dance courses, divided into three groups by mixed year levels due to timetable scheduling requirements. Pre- and post-testing was undertaken utilising the Mindful Attention Awareness Scale (MAAS-15), a uni-dimensional measure of mindfulness, in addition to qualitative questions checking the current level of awareness and understanding of mindfulness practice and its application. Weekly sessions were conducted by qualified sport and exercise psychologists and covered key practices such as: Mindfulness of Body, Mindfulness of Breathing, Mindfulness of Sounds, ACT-based and general Imagery exercises, Developing Open Awareness, Mindfulness of Emotions, and Developing Inner Stillness. Students were required to maintain a reflective journal that was utilised at the end of each weekly session, in addition to completion of a mid-Semester reflective debrief. Teaching staff additionally attended the weekly sessions and linked the mindfulness practice learnings into the student’s practical dance and academic classes where appropriate. Anecdotal feedback indicates that participation in the mindfulness-meditation sessions and the development of these mental skills has resulted in positive performance and personal outcomes. Observations collated from staff and students, results from the data collection phases and recommendations regarding future applications within dance training settings will be discussed within the presentation.

Planning to Learn : Creating and Using a Personal Planner with Young People on the Autism Spectrum

Late intervention often means that young people on the autism spectrum appear to act on impulse, seem disorganized, or fail to learn from past experiences. In this practical, effective resource, the authors share tried and tested techniques for creating and using a personal planner to help individuals on the autism spectrum to develop independence. "Planning to Learn" is split into three parts. The first part guides adults in helping young people to make sense of the world and to develop and practise coping strategies for any given situation. The authors also explain how simple visual and verbal cues can help people to cope successfully in stressful situations. The second part provides worksheets for the young person to complete to learn how to use plans in different situations, for example staying calm when waiting for a doctor, or coping with a change in the school timetable. Each individual makes a unique planner with procedures to refer to, such as responding to pressure, calming down, being organised, and being around people. The third part includes useful cards, schedules and plans for photocopying and including in the planner. This illustrated photocopiable workbook is packed with guidance, support and helpful notes for those new to, or experienced in, working with children and young people with ASD. It can be used within educational and community settings or at home.

An operational-level multi-stage mine production timetabling model for optimally synchronising drilling, blasting and excavating operations

This paper proposes a new multi-stage mine production timetabling (MMPT) model to optimise open-pit mine production operations including drilling, blasting and excavating under real-time mining constraints. The MMPT problem is formulated as a mixed integer programming model and can be optimally solved for small-size MMPT instances by IBM ILOG-CPLEX. Due to NP-hardness, an improved shifting-bottleneck-procedure algorithm based on the extended disjunctive graph is developed to solve large-size MMPT instances in an effective and efficient way. Extensive computational experiments are presented to validate the proposed algorithm that is able to efficiently obtain the near-optimal operational timetable of mining equipment units. The advantages are indicated by sensitivity analysis under various real-life scenarios. The proposed MMPT methodology is promising to be implemented as a tool for mining industry because it is straightforwardly modelled as a standard scheduling model, efficiently solved by the heuristic algorithm, and flexibly expanded by adopting additional industrial constraints.

A new constraint programming approach for optimising a coal rail system

Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimise the coal railing operations under constraints of limited resources (e.g., limited number of locomotives and wagons). In the literature, most studies considered the train scheduling problem on a single-track railway network to be strongly NP-hard and thus developed metaheuristics as the main solution methods. In this paper, a new mathematical programming model is formulated and coded by optimization programming language based on a constraint programming (CP) approach. A new depth-first-search technique is developed and embedded inside the CP model to obtain the optimised coal railing timetable efficiently. Computational experiments demonstrate that high-quality solutions are obtainable in industry-scale applications. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and specific criteria. Keywords Train scheduling · Rail transportation · Coal mining · Constraint programming