Odey Alshboul, Ali Shehadeh, Omer Tatari, Ghassan Almasabha and Eman Saleh
Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify…
Abstract
Purpose
Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.
Design/methodology/approach
This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.
Findings
The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.
Originality/value
This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.
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In British industry alone, the cost of corrosion is estimated at over £1,000 million annually. A high proportion of this cost is caused by two of the most widely used—and…
Abstract
In British industry alone, the cost of corrosion is estimated at over £1,000 million annually. A high proportion of this cost is caused by two of the most widely used—and corrosive—industrial chemicals: sulphuric and hydrochloric acids. Yet the corrosive damage caused by these two chemicals can be controlled and often eliminated by correct selection of construction materials. In the chemical and allied process industries many companies have been using glass reinforced plastic equipment to handle these two acids for a number of years with excellent results. Yearly, the number of reported applications increases as experience widens and fabrication techniques become more advanced.
THE luncheon given by the Lord Mayor, Sir Ralph Perring, to more than 700 guests at Guildhall on November 14, officially launched the country on National Productivity Year. Apart…
Abstract
THE luncheon given by the Lord Mayor, Sir Ralph Perring, to more than 700 guests at Guildhall on November 14, officially launched the country on National Productivity Year. Apart from representatives of the 120 local committees these were people from trade and employers' associations, trades unions, professional bodies and research organisations. It was, in effect, a token mobilisation of Britain's industrial might, because behind it stands a large army devoted to the task of increasing the country's output.
The purpose of this paper is to assess life cycle costing (LCC) of the equipment in a more realistic, precise, and applicable manner, and to apply it to a real industrial problem…
Abstract
Purpose
The purpose of this paper is to assess life cycle costing (LCC) of the equipment in a more realistic, precise, and applicable manner, and to apply it to a real industrial problem.
Design/methodology/approach
Based on the failure rates of the components of a machine, the LCC is assessed, mathematically modeled, and incorporated to the parallel machine replacement problem with capacity expansion consideration. The problem is modeled as mixed integer programming which intends to minimize the total costs incurred during a planning horizon of several periods for the machines of the same type with different ages. The decision variables are the number of machines to be purchased/salvaged in each period. A genetic algorithm (GA) is developed for solving the problem and its efficiency is verified.
Findings
In conventional models presented for calculation of LCC, corrective maintenance (CM) costs of the machines are incorporated to the model as a whole which may result in inaccurate calculations. Obtaining this value is also very difficult and it can be different for machines with different ages. By calculating the CM costs of a machine based on the failure rates of its components, the LCC can be properly estimated in a realistic and precise manner. The presented GA is also proven to be efficient for solving problems of almost any size with different number of machines, components, and planning periods.
Practical implications
The presented model and GA are applied to a real case of a construction company that needs to determine a purchase/salvage schedule for its loaders in the next ten years. Results of the calculated schedule imply that employing new loaders rather than maintaining the aged ones generally results in the minimum LCC.
Originality/value
This paper presents a novel approach for precise, meaningful, and practical LCC calculation. The mathematical model and its solving method can be utilized by both the manufacturers and buyers of equipment as a tool which determines a parallel machine purchase/salvage schedule for a planning horizon of several periods which incurs minimum overall cost. The presented material can be also applied to other industrial problems and cases.
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Jani Saastamoinen, Helen Reijonen and Timo Tammi
This paper examines entry barriers to involvement in public procurement of small and medium-sized enterprises and the role of training in dismantling those barriers. We find that…
Abstract
This paper examines entry barriers to involvement in public procurement of small and medium-sized enterprises and the role of training in dismantling those barriers. We find that firms' perceptions of barriers are of five main types. Regression analysis shows that a lack of ongoing training is associated with SMEs' perceptions of resource constraints and practical skills that hinder their participation in public procurement. We also observe a positive connection between a positive attitude toward training and SMEs' participation rates in public procurement. As a managerial implication, the value of training should be appraised at the firm level, and organizing training and providing information concerning public procurement could be a recommended policy to improve the SME participation rate in public procurement.
Julie Stubbs, Sophie Russell, Eileen Baldry, David Brown, Chris Cunneen and Melanie Schwartz
This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking…
Abstract
Purpose
This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.
Design/methodology/approach
The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.
Findings
The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.
Originality/value
The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).
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This paper presents a model for equipment selection in earthmoving operations, utilizing multi‐attribute utility theory, analytical hierarchy process and computer simulation…
Abstract
This paper presents a model for equipment selection in earthmoving operations, utilizing multi‐attribute utility theory, analytical hierarchy process and computer simulation. Fleet configurations in the developed model are generated randomly from predefined fleet scenarios within a specified range. Simulation experiments are conducted for these generated configurations. The performance of these configurations is obtained from simulation experiments in the form of four measures which represent loader utilization, hauler utilization, project duration and project total cost. The utility values which represent the degree of satisfaction with those measures are estimated. These utility values are multiplied by their corresponding measures’ weights, calculated utilizing the analytical hierarchy process, in order to estimate the expected utility for each configured fleet. The fleet configuration that has the largest utility value is selected as the optimum fleet for the case at hand. A numerical example is presented to illustrate the different features of the developed model.
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Nehal Elshaboury and Mohamed Marzouk
There have been numerous efforts to tackle the problem of accumulated construction and demolition wastes worldwide. In this regard, this study develops a model for identifying the…
Abstract
Purpose
There have been numerous efforts to tackle the problem of accumulated construction and demolition wastes worldwide. In this regard, this study develops a model for identifying the optimum fleet required for waste transportation. The proposed model is validated through a case study from the construction sector in New Cairo, Egypt.
Design/methodology/approach
Various fleet combinations are assessed against the time, cost, energy and emissions generated from waste transportation. Genetic algorithm optimization is performed to select the near-optimum solutions. Complex proportional assessment and operational competitiveness rating analysis decision-making techniques are applied to rank Pareto frontier solutions. These rankings are aggregated using an ensemble approach based on the half-quadratic theory. Finally, a sensitivity analysis is implemented to determine the most sensitive attribute.
Findings
The results reveal that the optimum fleet required for construction and demolition wastes (CDW) transportation consists of one wheel loader of bucket capacity 2.5 cubic meters and nine trucks of capacity 22 cubic meters. Furthermore, consensus index and trust level of 0.999 are obtained for the final ranking. This indicates that there is a high level of agreement between the rankings. Moreover, the most sensitive criterion (i.e. energy) is identified using a sensitivity analysis.
Originality/value
This study proposes an efficient and effective construction and demolition waste transportation strategy that will lead to economic gains and protect the environment. It aims to select the optimum fleet required for waste transportation based on economic, social and environmental aspects. The usefulness of this study is establishing a consensual decision through the aggregation of conflicting decision makers' preferences in waste transportation and management.