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... is noted that the termination criterion "Cost N+1 > Cost N" means that employing one more truck will lead to total direct cost increase. Having the basis of truck number optimization, truck/excavator type optimization can be made possible by adding one more loop by iterating all the possible truck/excavator combination, as shown in Figure 2. The total project cost (consist of multiple haul jobs) under different truck/excavator combinations will be calculated and compared to result in the optimal truck/excavator selection in terms of minimum total project cost. ...
Context 2
... is noted that the termination criterion "Cost N+1 > Cost N" means that employing one more truck will lead to total direct cost increase. Having the basis of truck number optimization, truck/excavator type optimization can be made possible by adding one more loop by iterating all the possible truck/excavator combination, as shown in Figure 2. The total project cost (consist of multiple haul jobs) under different truck/excavator combinations will be calculated and compared to result in the optimal truck/excavator selection in terms of minimum total project cost. ...
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The digging arm of underground excavator truck has many movable joints, complex structure and variable operation properties which results in the complicated loading. So it is difficult to obtain the external working load directly through the test. For this problem, the calculation of the external load on the digging arm of the underground excavator...
Citations
... They propose methodologies, including multimodal-process data mining, to capture operational knowledge and create realistic simulation models [8]. Optimization efforts focus on minimizing both cost and emissions, considering factors such as equipment availability and project indirect costs [9,10,11,12]. ...
... Pada pekerjaan pemindahan tanah dengan volume relatif besar dan waktu penyelesaian pekerjaan yang ketat, untuk memperoleh hasil yang optimal, setiap project manager dituntut untuk dapat memperhitungkan kebutuhan alatalat berat dalam berbagai kemungkinan kombinasi dan distribusi. Namun pada praktiknya tidak jarang analisis kebutuhan alat berat hanya didasarkan pada tingkat produksi rata-rata dan pengalaman pekerjaan serupa yang pernah dikerjakan sebelumnya [3]. Kondisi ini berakibat luaran hasil perencanaan tidak dapat mereprentasikan kondisi riil di lapangan. ...
... nD2Tabel 1. Variasi Jenis Alat BeratModel mengilustrasikan kondisi di mana suatu operasi pekerjaan pemindahan tanah dilakukan oleh satu jenis alat muat dengan variasi dua tipe alat angkut yang berbeda. Gambar 2 (b) menunjukkan salah satu contoh pemodelan Skema-II di mana satu jenis excavator PC 200 yang melayani dua jenis truck dengan kapasitas masing-masing 7 m 3 dan 20 m3 .Pada Skema-III pemodelan operasi mengakomodasi kombinasi alat di mana dua unit excavator dengan kapasitas yang berbeda melayani beberapa truck dengan kapasitas yang sama. Gambar 2 (c) menunjukkan contoh pemodelan di mana dua unit excavator yakni excavator PC 200 kapasitas 1 m 3 dan excavator PC 120 kapasitas 0,5 m 3 yang didukung oleh beberapa unit truck dengan kapasitas 7 m 3 . ...
Earthmoving work is often performed based on simple calculation and experience. This practice may work on a small project. However, for a big project in which a large excavation volume must be accomplished, managing the work become more complex. Some different equipment in various types and sizes need to be assigned. Project managers should determine the best combination of equipment with the correct amount to achieve a satisfactory result. This research was conducted to optimize the productivity-cost index in earthmoving works using a discrete event simulation (DES) analysis in four different schemes. An underpass project in Yogyakarta was used as the case study. The optimal combination of equipment was determined by the highest value of the productivity-cost index. The result shows that the highest productivity-cost index of 18.22 was achieved by the combination of four big excavators and 90 small trucks (2E4 90D1). It offered a better productivity-cost index compare to the contractors existing equipment combination which could only reach a productivity-cost index of 5.3.
... Siu et al. (2017) defined a crew job allocation and schedule optimization problem in connection with snow removal operations; the resulting optimization framework encompassed (1) analysis of snow plowing and sanding (or salting) time with consideration of field and managerial constraints, (2) optimization of snow plowing and sanding time based on the modified Floyd-Warshall algorithm, and (3) optimizing shop-road assignments in terms of plowing and sanding operation efforts. Yi and Lu (2018) proposed a fleet optimization solution for earthwork projects using discrete event simulation, revealing the impact of employing different combinations of excavators and trucks upon completing earth-moving jobs. Biruk et al. (2019) proposed a mixed integer binary-based optimization solution for preparing a project schedule based on the subcontractor's bid value and availability. ...
The problem of planning drainage services in a certain timeframe by a typical municipal infrastructure maintenance organization lends itself well to existing solutions for resource constrained project scheduling optimization. However, the optimized schedule could be deemed insufficient from a practitioner's perspective as the very crucial soft constraint on the competence of a particular crew handling different jobs is excluded. We define a crew-job matching index ranging from 0 to 1 to allow for a planner's assessment to be factored in job schedule simulation and optimization. The total crew-job matching index (TCJMI) is further defined, accounting for all the crew-job assignments and indicating the fitness of a formulated plan. TCJMI is maximized in the resource-constrained schedule optimization by applying Excel Solver add-in. As such, the planner's preference and experience can be represented and factored in crew-job scheduling optimization, which is demonstrated through conducting "what-if" simulation scenario analyses.
... Then, collect cycle time data for selected equipment in consideration of job conditions being analyzed; apply simulation modeling to identify the optimum configuration of the fleet and determine the optimum production rate of the fleet in terms of UN/HR according to the procedure commonly used to guide simulation experiments. For instance, Yi and Lu (2018) developed a methodology based on the use of a simulation model in analysis of planned haul jobs one at a time, identifying the best number of trucks and determining the best combination of trucks and excavators, given the excavator being the governing resource. ...
One limitation of previous productivity-driven research on equipment selection and operation simulation lies in the fact that the green aspects of construction activities have been largely neglected in analysis of cost-efficiency of construction operations. On the other hand, studies attempting to measure greenhouse gas emission due to construction activities have yet to develop a methodology that correlates their findings and implications with construction productivity. In order to address the immediate need for improving the sustainability performance of construction projects, it is imperative for the construction industry to evaluate greenhouse gas emission as a cost factor in construction planning, equipment selection, and cost estimating. In this context, this paper formalizes an integrative framework for equipment cost analysis based on the concepts of lean construction and green construction, aimed to guide the selection of appropriate construction equipment considering exhaust emission and productivity performance at the same time. The framework is elaborated in earthwork construction in order to evaluate the impact of greenhouse gas emission in estimating equipment hourly rates and assessing greenness and sustainability for alternative equipment options.
There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant of the successful delivery of site operations. Although manufacturers provide equipment performance handbooks, additional monitoring mechanisms are required to depart from measuring performance on the sole basis of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated image libraries are used to train and test several backbone architectures. Experimental results reveal the precision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of potentials to influence current practice of articulated machinery monitoring in projects.
