Optimizing the Schedule of Dispatching Earthmoving Trucks through Genetic Algorithms and Simulation

Lin, Chih-tsang; Hsie, Machine; Hsiao, Wen-Ta; Wu, Hsien-Tang; Cheng, Tao-Ming

doi:10.1061/(asce)cf.1943-5509.0000219

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…Some studies work exclusively with homogeneous fleets [10,11], i.e., do not consider the need for compatibility constraints between trucks and shovels. As large mines generally involve heterogeneous truck fleets [2,4,5,[7][8][9], specialist algorithms for truck dispatching must consider equipment size to perform the correct allocation.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Souza et al [5] present a single-objective hybrid algorithm that combines characteristics of two meta-heuristics: Greedy Randomized Adaptive Search Procedure (GRASP) and General Variable Neighborhood Search (GVNS). Lin et al [11] use a Genetic Algorithm (GA) to solve a problem of managing truck fleets in earthmoving operations, a similar problem to the truck dispatch problem in open-pit mines. Bastos et al [4] present an algorithm that uses probability functions to select the destination of the truck and the probability value can vary over time.…”

Section: Related Workmentioning

confidence: 99%

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Multi-objective evolutionary algorithms for the truck dispatch problem in open-pit mining operations

Alexandre¹,

Campelo²,

Vasconcelos³

2019

Learn. Nonlin. Mod.

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This work is concerned with the efficient allocation of trucks to shovels in operation at open-pit mines. As this problem involves high-value assets, namely mining trucks and shovels, any improvement obtained in terms of operational efficiency can result in considerable financial savings. Thus, this work presents multi-objective strategies for solving the problem of dynamically allocating a heterogeneous fleet of trucks in an open-pit mining operation, aiming at maximizing production and minimizing costs, subject to a set of operational and physical constraints. Two Multi-objective Genetic Algorithms (MOGAs) were specially developed to address this problem: the first uses specialized crossover and mutation operators, while the second employs Path-Relinking as its main variation engine. Four test instances were constructed based on real open-pit mining scenarios, and used to validate the proposed methods. The two MOGAs were compared to each other and against a Greedy Heuristic (GH), suggesting of of the MOGAs as a potential strategy for solving the multi-objective truck dispatch problem for open-pit mining operations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-objective evolutionary algorithms for the truck dispatch problem in open-pit mining operations

Alexandre¹,

Campelo²,

Vasconcelos³

2019

Learn. Nonlin. Mod.

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“…Along with the diversified development of construction simulation technology, its application scope has been extended, such as multiobjective optimization of construction allocation schemes (Cheu et al, 2004;Lin et al, 2012), hybrid simulation considering both strategic and operational aspects (Alvanchi et al, 2011;Alzraiee et al, 2015), real-time data-driven simulation of construction operations (Vahdatikhaki and Hammad, 2014), visualized construction simulation (Chen and Huang, 2013), impact evaluation of project management strategies on environment (Tang et al, 2013), schedule risk analysis study (Bi et al, 2015), and integrated scheduling platform establishment (Karim and Adeli, 1999a;Liu et al, 2015).…”

Section: Construction-oriented Discrete Event Simulationmentioning

confidence: 99%

Earth Dam Construction Simulation Considering Stochastic Rainfall Impact

Zhang

Zhong

et al. 2017

Computer aided Civil Eng

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Effective construction scheme planning is critical for schedule management, but heavy rain can affect construction processes. In previous studies, stochastic rainfall characteristics are often ignored, and their impact on macro‐ and microconstruction states are not depicted comprehensively. This research presents a construction simulation model to design reasonable construction schemes considering impact of stochastic rainfall. First, a rainfall model suitable for areas with heavy rainfall and uneven seasonal rainfall distribution is built. Then, multiaspect indicators are defined to intuitively quantify rainfall impact. Two case studies are conducted to evaluate applicability of the proposed method. Results demonstrate that the developed rainfall model aligns closely with observed data. Simulation findings reveal that if stochastic rainfall characteristics are ignored, the schedule and queuing probability of trucks will be underestimated, while machinery utilization will be overestimated. This research provides an effective simulation tool for determining adaptive measures to mitigate impacts of rainfall events.

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“…The new model widens the optimization scale of earth-moving operations to accommodate heavy civil engineering projects. Lin et al (2012) combined discrete event simulation techniques with GAs to optimize the time schedule for dispatching earthmoving trucks.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum Planning for Multi-Project Earthmoving Operations

Abu-Samra¹,

Hakea²,

Khalil³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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By virtue of its complex nature, the construction industry comprises a wide spectrum of interrelated variables and factors. This multifaceted character of the construction industry puts the use of engineering modeling tools and techniques on top of project management necessities. This research introduces a macro-level earthmoving management system using Genetic Algorithms (GAs) to reach the optimum allocation of earthmoving equipment. The Earthmoving Equipment Management System (EEMS) functions through four integrated modules: (1) A Central Database containing information about projects and available equipment; (2) An Equipment per Segment Selection module that calculates the cut and fill quantities, plots the Mass Haul Diagrams, and selects the equipment types to be used for each segment; (3) An Equipment Pool module which determines the production and cost for each available equipment, based upon the project site conditions; and (4) An Optimization Engine equipped with a GA optimization solver. This engine formulates the optimum earthmoving cost for all projects, by changing the number of allocated equipment. The Optimization Engine takes into account the number of available equipment and calculates the weekly equipment allocation. The EEMS model was implemented on an earthmoving company conducting five different projects on hand. The model proved to be an effective tool in providing decision makers with optimum equipment utilization on a multi-project scale to minimize the earthmoving cost. The results were then compared with the company's existing micro-level management system, demonstrating better performance on the level of savings, amounting to 13% of total earthmoving cost.

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Optimizing the Schedule of Dispatching Earthmoving Trucks through Genetic Algorithms and Simulation

Cited by 19 publications

References 11 publications

Multi-objective evolutionary algorithms for the truck dispatch problem in open-pit mining operations

Multi-objective evolutionary algorithms for the truck dispatch problem in open-pit mining operations

Earth Dam Construction Simulation Considering Stochastic Rainfall Impact

Optimum Planning for Multi-Project Earthmoving Operations

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