Labouring for leisure? Achieving work-life balance through compressed working weeks

Flexible work practices spreading work times across the entire week have reduced the time to engage in leisure activities and for some have compounded the problem of a lack of defined break between work weeks. This study examines time spent outside of the workplace through a multiple case study of working time and leisure in the construction industry. A framework of synchronous leisure is used to examine the interplay of work and non-work arrangements. The effects of changing work arrangements to deliver a longer break between working weeks and the consequent impact on leisure activities are analysed. Interviews and focus groups across four construction sites revealed that while leisure is important to relieve fatigue and overwork, a work schedule allowing a long break between working weeks, specifically on a weekend, enables workers to achieve synchronous time, particularly with family, and improves work-life balance satisfaction. It was found that a well-defined break across a weekend also offers the opportunity to synchronize schedules with others to spend time away on short breaks.

A framework for analysing system intelligence of the building control system: a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

Peer state assessment of TxDOT maintenance program and practices, part II : test section survey findings and recommendations

This paper presents a road survey as part of a workshop conducted by the Texas Department of Transportation (TxDOT) to evaluate and improve the maintenance practices of the Texas highway system. Directors of maintenance from six peer states (California, Kansas, Georgia, Missouri, North Carolina, and Washington) were invited to this 3-day workshop. One of the important parts of this workshop was a Maintenance Test Section Survey (MTSS) to evaluate a number of pre-selected one-mile roadway sections. The workshop schedule allowed half a day to conduct the field survey and 34 sections were evaluated. Each of the evaluators was given a booklet and asked to rate the selected road sections. The goals of the MTSS were to: 1. Assess the threshold level at which maintenance activities are required as perceived by the evaluators from the peer states; 2. Assess the threshold level at which maintenance activities are required as perceived by evaluators from other TxDOT districts; and 3. Perform a pilot evaluation of the MTSS concept. This paper summarizes the information obtained from survey and discusses the major findings based on a statistical analysis of the data and comments from the survey participants.

Association between feeding style and weight gain in infants aged 2–7 months

Introduction: Feeding on demand supports an infant’s innate capacity to respond to hunger and satiety cues and may promote later self-regulation of intake. Our aim was to examine whether feeding style (on demand vs to schedule) is associated with weight gain in early life. Methods: Participants were first-time mothers of healthy term infants enrolled NOURISH, an RCT evaluating an intervention to promote positive early feeding practices. Baseline assessment occurred when infants were aged 2-7 months. Infants able to be categorised clearly as feeding on demand or to schedule (mothers self report) were included in the logistic regression analysis. The model was adjusted for gender, breastfeeding and maternal age, education, BMI. Weight gain was defined as a positive difference in baseline minus birthweight z-scores (WHO standards) which indicated tracking above weight percentile. Results: Data from 356 infants with a mean age of 4.4 (SD 1.0) months were available. Of these, 197 (55%) were fed on demand, 42 (12%) were fed on schedule. There was no statistical association between feeding style and weight gain [OR=0.72 (95%CI 0.35-1.46), P=0.36]. Formula fed infants were three times more likely to be fed on schedule and formula feeding was independently associated with increased weight gain [OR=2.02 (95%CI 1.11-3.66), P=0.021]. Conclusion: In this preliminary analysis the association between feeding style and weight gain did not reach statistical significance, however , the effect size may be clinically relevant and future analysis will include the full study sample (N=698).

Analysis of selection criteria for design-builders through the analysis of Request for Proposals (RFPs)

Selecting an appropriate design-builder is critical to the success of DB projects. The objective of this study is to identify selection criteria for design-builders and compare their relative importance by means of a robust content analysis of 94 Request For Proposals (RFPs) for public DB projects. These DB projects had an aggregate contract value of over US$3.5 billion and were advertised between 2000 and 2010. This study summarized twenty-six selection criteria and classified into ten categories, i.e.: price, experience, technical approach, management approach, qualification, schedule, past performance, financial capability, responsiveness to the RFP, and legal status in descending order of their relative importance. The results showed that even though price still remains as the most important selection category, its relative importance declines significantly in the last decade. The categories of qualification, experience, past performance, by contrast, have been becoming more important to DB owners for selecting design-builders. Finally, it is found that the importance weighting of price in large projects is significantly higher than that in small projects. This study provides a useful reference for owners in selecting their preferred design-builders.

Measuring complexity for building projects : a Delphi study

Purpose---The aim of this study is to identify complexity measures for building projects in the People’s Republic of China (PRC). Design/Methodology/Approach---A three-round of Delphi questionnaire survey was conducted to identify the key parameters that measure the degree of project complexity. A complexity index (CI) was developed based on the identified measures and their relative importance. Findings---Six key measures of project complexity have been identified, which include, namely (1) building structure & function; (2) construction method; (3) the urgency of the project schedule; (4) project size/scale; (5) geological condition; and (6) neighboring environment. Practical implications---These complexity measures help stakeholders assess degrees of project complexity and better manage the potential risks that might be induced to different levels of project complexity. Originality/Value---The findings provide insightful perspectives to define and understand project complexity. For stakeholders, understanding and addressing the complexity help to improve project planning and implementation.

Scheduling Trains with Priorities : a no-wait blocking parallel-machine job-shop scheduling model

The paper investigates train scheduling problems when prioritised trains and non-prioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, non-prioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop-Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively-used sub-algorithms (i.e. Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-up Procedure and Fine-tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling and solving real-world scheduling problems.

Optimising a coal rail network under capacity constraints

This research deals with an innovative methodology for optimising the coal train scheduling problem. Based on our previously published work, generic solution techniques are developed by utilising a “toolbox” of standard well-solved standard scheduling problems. According to our analysis, the coal train scheduling problem can be basically modelled a Blocking Parallel-Machine Job-Shop Scheduling (BPMJSS) problem with some minor constraints. To construct the feasible train schedules, an innovative constructive algorithm called the SLEK algorithm is proposed. To optimise the train schedule, a three-stage hybrid algorithm called the SLEK-BIH-TS algorithm is developed based on the definition of a sophisticated neighbourhood structure under the mechanism of the Best-Insertion-Heuristic (BIH) algorithm and Tabu Search (TS) metaheuristic algorithm. A case study is performed for optimising a complex real-world coal rail system in Australia. A method to calculate the lower bound of the makespan is proposed to evaluate results. The results indicate that the proposed methodology is promising to find the optimal or near-optimal feasible train timetables of a coal rail system under network and terminal capacity constraints.

Using operations research methodologies to improve operating theatre scheduling

A hospital consists of a number of wards, units and departments that provide a variety of medical services and interact on a day-to-day basis. Nearly every department within a hospital schedules patients for the operating theatre (OT) and most wards receive patients from the OT following post-operative recovery. Because of the interrelationships between units, disruptions and cancellations within the OT can have a flow-on effect to the rest of the hospital. This often results in dissatisfied patients, nurses and doctors, escalating waiting lists, inefficient resource usage and undesirable waiting times. The objective of this study is to use Operational Research methodologies to enhance the performance of the operating theatre by improving elective patient planning using robust scheduling and improving the overall responsiveness to emergency patients by solving the disruption management and rescheduling problem. OT scheduling considers two types of patients: elective and emergency. Elective patients are selected from a waiting list and scheduled in advance based on resource availability and a set of objectives. This type of scheduling is referred to as ‘offline scheduling’. Disruptions to this schedule can occur for various reasons including variations in length of treatment, equipment restrictions or breakdown, unforeseen delays and the arrival of emergency patients, which may compete for resources. Emergency patients consist of acute patients requiring surgical intervention or in-patients whose conditions have deteriorated. These may or may not be urgent and are triaged accordingly. Most hospitals reserve theatres for emergency cases, but when these or other resources are unavailable, disruptions to the elective schedule result, such as delays in surgery start time, elective surgery cancellations or transfers to another institution. Scheduling of emergency patients and the handling of schedule disruptions is an ‘online’ process typically handled by OT staff. This means that decisions are made ‘on the spot’ in a ‘real-time’ environment. There are three key stages to this study: (1) Analyse the performance of the operating theatre department using simulation. Simulation is used as a decision support tool and involves changing system parameters and elective scheduling policies and observing the effect on the system’s performance measures; (2) Improve viability of elective schedules making offline schedules more robust to differences between expected treatment times and actual treatment times, using robust scheduling techniques. This will improve the access to care and the responsiveness to emergency patients; (3) Address the disruption management and rescheduling problem (which incorporates emergency arrivals) using innovative robust reactive scheduling techniques. The robust schedule will form the baseline schedule for the online robust reactive scheduling model.

Machinima

The research field was curatorship of the Machinima genre - a film-making practice that uses real time 3D computer graphics engines to create cinematic productions. The context was the presentation of gallery non-specific work for large-scale exhibition, as an investigation in thinking beyond traditional strategies of white cube. Strongly influenced by the Christiane Paul (Ed) seminal text, 'New Media in the White Cube and Beyond, Curatorial Models for Digital Art', the context was the repositioning of a genre traditionally focussed on delivery through small-screen, indoor, personal spaces, to large exhibition hall spaces. Beyond the core questions of collecting, documenting, expanding and rethinking the place of Machinima within the history of contemporary digital arts, the curatorial premise asked how to best invert the relationship between context of media production within the gaming domain, using novel presentational strategies that might best promote the 'take-home' impulse. The exhibition was used not as the ultimate destination for work but rather as a place to experience, sort and choose from a high volume of possible works for subsequent investigation by audiences within their own game-ready, domestic environments. In pursuit of this core aim, the exhibition intentionally promoted 'sensory overload'. The exhibition also included a gaming lab experience where audiences could begin to learn the DIY concepts of the medium, and be stimulated to revisit, consider and re-make their own relationship to this genre. The research was predominantly practice-led and collaborative (in close concert with the Machinima community), and ethnographic in that it sought to work with, understand and promote the medium in a contemporary art context. This benchmark exhibition, building on the 15-year history of the medium, was warmly received by the global Machinima community as evidenced by the significant debate, feedback and general interest recorded. The exhibition has recently begun an ongoing Australian touring schedule. To date, the exhibition has received critical attention nationally and internationally in Das Superpaper, the Courier Mail, Machinimart, 4ZZZ-FM, the Sydney Morning Herald, Games and Business, Australian Gamer, Kotaku Australia, and the Age.

Optimal sitting and sizing of distributed generators considering plug-in electric vehicles

Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule, that of a wind unit and that of a photovoltaic generation source, volatile fuel prices and future uncertain load growth, all together could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distributed systems. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal sitting and sizing problem of DGs. First, a mathematical model of CCP is developed with the minimization of DGs investment cost, operational cost and maintenance cost as well as the network loss cost as the objective, security limitations as constraints, the sitting and sizing of DGs as optimization variables. Then, a Monte Carolo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method. This work is supported by an Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Project on Intelligent Grids Under the Energy Transformed Flagship, and Project from Jiangxi Power Company.

Factors influencing speeding in school zones : the role of mindfulness

Background Excessive speed contributes to the incidence and severity of road crashes. The Theory of Planned Behaviour (TPB) has successfully explained variance in speeding intentions and behaviour. However, studies have shown that more than 40% of the variance in outcome measures of speeding remains unexplained, thus, suggesting additional constructs may help to enhance the TPB’s predictive power. Therefore, this study examined mindfulness; a promising construct which has not yet been tested as an additional TPB predictor. Aims The aims of this study were to explore drivers’ beliefs about speeding in school zones using the extended TPB as a framework and to examine the effect that mindfulness had on driver speeding behaviour in school zones. Methods Australian drivers (N = 17) participated in one of four focus group discussions. The overall sample was comprised of five males and twelve females who were aged between 17 to56 years. All participants were recruited via purposive sampling among 1st year psychology students at a large South East Queensland University. The group discussions took approximately one hour and were guided by a structured interview schedule which sought to elicit drivers’ beliefs, thoughts and opinions on speeding in school zones and the factors which motivate such behaviour. Results Overall, thematic analysis revealed some similar issues emerged across the groups. . In particular and perhaps somewhat unsurprisingly, given public concerns regarding the want to ensure the safety of school children, there was much agreement that speeding in school zones was dangerous and unacceptable. Somewhat paradoxically however, some participants also agreed that they had unintentionally or mindlessly sped in school zones. There were several factors that drivers believed influenced their speeding in school zones including their current mood (e.g., if in a bad mood, anxious, or excited they may be more likely to drive without awareness of, and being attentive to, their driving environment) and the extent to which they were familiar with the environment (i.e., more familiar contexts, more likely to drive mindlessly). Thus, although drivers expressed a belief that speeding in school zones was dangerous and acceptable, the extent to which a driver is mindful does influence whether or not a driver may actually engage in speeding in this context. Discussion and conclusions This study highlights the potential role of mindfulness in helping to explain speeding behaviour in school zones. Mindless drivers may speed unintentionally and while unintentional still be endangering the safety and lives of school children. The findings of this research suggest that unintentional speeding, especially in school zones, may be reduced by countermeasures which heighten the extent to which drivers are mindful of approaching and/or driving through a school zone, such as street markings and engineering measures (e.g.,flashing lights and speed bumps).

A new approach to automatically producing schedules for cane railways

The scheduling of locomotive movements on cane railways has proven to be a very complex task. Various optimisation methods have been used over the years to try and produce an optimised schedule that eliminates or minimises bin supply delays to harvesters and the factory, while minimising the number of locomotives, locomotive shifts and cane bins, and also the cane age. This paper reports on a new attempt to develop an automatic scheduler using a mathematical model solved using mixed integer programming and constraint programming approaches and blocking parallel job shop scheduling fundamentals. The model solution has been explored using conventional constraint programming search techniques and found to produce a reasonable schedule for small-scale problems with up to nine harvesters. While more effort is required to complete the development of the full model with metaheuristic search techniques, the work completed to date gives confidence that the metaheuristic techniques will provide near optimal solutions in reasonable time.

Determining the optimal proportion of design in design-build request for proposals (RFPs)

The design-build (DB) system is a popular and effective delivery method of construction projects worldwide. After owners decide to procure their projects through the DB system, they may wish to determine the optimal proportion of design to be provided in the DB request for proposals (RFPs), which serve as solicitations for design-builders and describe the scope of work. However, this presents difficulties to DB owners and there is little, if any, systematic research in this area. This paper reports on an empirical study in the USA entailing both an online questionnaire survey and Delphi survey to identify and evaluate the factors influencing owners’ decisions in determining the proportion of design to include in DB RFPs. Eleven factors are identified, i.e. (1) clarity of project scope; (2) applicability of performance specifications; (3) desire for design innovation; (4) site constraints; (5) availability of competent design-builders; (6) project control requirements; (7) user group involvement level; (8) third party requirements; (9) owner experience with DB; (10) project complexity; and (11) schedule constraints. A statistically significant agreement on the eleven factors was also obtained from the (mainly non-owner) Delphi experts. Although some of the experts hold different opinions on how these factors affect the proportion of design, these findings furnish various stakeholders with a better understanding of the delivery process of DB projects and the appropriate provision of project information in DB RFPs. As the result is mainly industry opinion concerning the optimal proportion of design, in addition and for completeness, future studies should be conducted to obtain a big picture of the optimal proportion of design by means of seeking owners’ inputs.

Modeling train delays in urban networks

The reliability of urban passenger trains is a critical performance measure for passenger satisfaction and ultimately market share. A delay to one train in a peak period can have a severe effect on the schedule adherence of other trains. This paper presents an analytically based model to quantify the expected positive delay for individual passenger trains and track links in an urban rail network. The model specifically addresses direct delay to trains, knock-on delays to other trains, and delays at scheduled connections. A solution to the resultant system of equations is found using an iterative refinement algorithm. Model validation, which is carried out using a real-life suburban train network consisting of 157 trains, shows the model estimates to be on average within 8% of those obtained from a large scale simulation. Also discussed, is the application of the model to assess the consequences of increased scheduled slack time as well as investment strategies designed to reduce delay.