Travis Waller

Scheduling Concrete Delivery Problems by a Robust Meta Heuristic Method

Finding practical methods for resources allocation in Ready Mixed Concrete (RMC) is a critical issue. In the literature, heuristic methods have been mostly used for solving the RMC problem. The introduced methods are mostly intended to find a premature solution and then to try to fix the indefeasibilities by defined iterative algorithms. In this paper a new approach is proposed for constructing the solution structure when discrete heuristic methods are used. Genetic Algorithm (GA) has been selected for implementing the proposed idea, and for evaluating the model a large scale dataset is used. The dataset covers an active RMC for a period of one month. The comprehensive tests show that the proposed method is able to find a feasible solution without any need to adjust the method.

Reconstruction of an Expert's Decision Making Expertise in Concrete Dispatching by Machine Learning

Finding the optimum solution for dispatching in concrete delivery is computationally intractable because it is a NP-hard (non-deterministic polynomial-time hard) problem. Heuristic methods are required to obtain satisfactory solutions. Inefficiencies in mathematical modeling still make concrete dispatching difficult to solve. In reality, complex dispatching systems are mostly handled by human experts, who are able to manage the assigned tasks well. However, the high dependency on human expertise is a considerable challenge for RMC (ready mixed concrete) companies. In this paper, a logical reconstruction of an experts decision making is achieved by two machine learning techniques: decision tree and rule induction. This paper focuses on the expert dispatchers prioritization of customer orders. The proposed method has been tested on a simulation model consisting of a batch plant and three customers per day. The scenarios generated by the simulation model were given to a dispatch manager who was asked to prioritize the customers in each day. The scenarios and the decisions were then input to the machine learning programs, which created generalizations of the experts decisions. Both decision trees and rules approach 80% accuracy in reproducing the human performance.

Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

Abstract Being able to precisely predict the duration of concrete operations can help construction managers to organize sites and machineries more efficiently, especially when there is limited space for equipment on site. Currently there is no theoretical method for estimating the duration of the concrete pouring process. Normally, the maximum capacity of pumping facilities on construction sites is not used, and concrete pumps are idle for a considerable time as a result of the arrival of concrete trucks being delayed. In the light of this issue, this paper considers the supply chain parameters of Ready Mixed Concrete (RMC) as a means of solving this problem. Artificial Neural Network (ANN) is hired for modelling/predicting the productivity of a concrete operation. The proposed model is tested with a real database of an RMC in the Sydney metropolitan area that has 17 depots and around 200 trucks. Results show that there is an improvement in the achieved results when these are compared to the results of relevant studies that only considered the construction parameters for predicting the productivity of concrete operations

Choice Set Formation Behavior: Joint Mode and Route Choice Selection Model

To improve the level of service of traffic and predict travel demand, it is essential to analyze the behavioral factors that affect transportation mode choice and route choice at the individual level. Such analysis requires detailed data on the behavior of people in the selection of different modes and routes. This paper presents a unique data collection endeavor, intended to observe the formation of the choice set from which the final alternative is selected. The revealed preference information about the routes people considered and used for their last work or study trip was targeted. Google Maps API (application programming interface), which has the capacity to calculate and return car and public transportation routes, was employed to program the survey and adaptively show the respondents the routes according to the reported origin and destination. A pilot survey was conducted with this survey tool at the University of New South Wales, Sydney, Australia, with a sample of 200 respondents. A preliminary analysis was carried out to analyze the effectiveness of the survey tool and the specification of the choice set. Three modeling structures—multinomial logit (MNL), nested logit, and mixed MNL—were used to estimate the parameters of the preliminary analysis. The results were fairly intuitive as far as the signs of the parameters were concerned, and travel time significantly influenced route choice.

INTEGRATED BUILDING INFORMATION MODELLING (BIM) WITH SUPPLY CHAIN AND FEED-FORWARD CONTROL

Abstract One of the major causes of delays in construction sites belongs to the delays in delivering materials. This paper introduces the integrated Building Information Modelling (BIM) model combined with a supply chain and a controlling system, along with its efficiency for alleviating the negative effects of delays caused by a lack of materials on construction sites. So, firstly the concept of feed-forward control is discussed as a prevention feature in a monitoring system. Then the integrating BIM model with a supply chain is introduced. Finally this case study has shown how the enhanced BIM model can incorporate the feed-forward control in order to decrease the risks of delays caused by a lack of materials in construction. Moreover, this case study might be used as a decision support system for managers to prevent a construction site from future delays by providing valuable information about the required materials in advanced

Choice Set Formation Behavior in Selecting Travel Routes: Application of an Interactive Online Survey Platform

One of the major challenges associated with the analysis of route choice modeling is the formulation of the choice set of alternatives that may allow a relatively accurate prediction of demand for travel routes. The subset of route alternatives in the choice set should be relevant and feasible and include the attributes considered most by travelers when they choose a route. This research investigated the role and significance of route choice set formations with a focus on the perspectives of the modeler and of travelers. Revealed preference data were collected from Sydney, Australia, residents about their choice of route for their most recent commuting trip. The survey tool was programmed to use the Google Maps application programming interfaces to collect the route choice information, including the selected route and the set of routes that were considered. Three discrete choice models were used to investigate the traveler’s inclination toward certain attributes of routes, considering both car and public transit routes with the master choice set. The effect of possible bias generated because of the formation of route choice from the perspective of the modeler was also analyzed and presented with the results. The results show the intuitive signs of various attributes, with travel time being the significant factor for route choice. The difference between the choice sets considered by the traveler and by the modeler also suggests that those considered by the modeler possess enough variation to offer the possibility of better capturing important factors affecting route choice behavior.

An investigation into the perceived quality of communications in dispersed construction project teams

Construction project teams are increasingly operating in dispersed arrangements in which communications predominantly take place via information communication technology (ICT) with a dose of face-to-face interactions. However, a review of literature reflects a scarcity of research on how shifting to dispersed teams affects the perceived quality of communications in construction project teams. The present study attempts to address such a gap in the body of knowledge. To this end, 12 major indicators of quality of communications were identified through a review of literature. This was followed by conducting 17 semi-structured interviews with experts in the industry to customize the indicators for the construction context. Based on the identified indicators, a questionnaire survey was designed for elucidating the perceptions of construction practitioners about changes in quality of communications in dispersed teams in comparison to fully collocated ones. Drawing upon the data collated from 285 duly completed questionnaires, confirmatory factor analysis (CFA) was deployed to define the major areas of change in quality of communications in dispersed teams as perceived by the respondents. The findings showed that shifting towards dispersed working results in a change in perceived quality of communications in ten areas mainly reduction in completeness and reliability of communications. The present study contributes to the field by providing a list of indicators for assessing the quality of communications for construction project teams. Additionally, the findings provide an illuminating insight into the primary changes in perceived quality of communications in disperse team arrangements.