Real-time truck dispatching using a transportation algorithm

Temeng, Victor Amoako; Otuonye, Francis O.; Frendewey, James O.

doi:10.1080/09208119708944093

Cited by 39 publications

(17 citation statements)

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“…Constraint (12) ensures that if shovel 's' was assigned to face 'f' and to only one face in period 'p' , it will continue to be assigned to face 'f' in the next period. Constraint (12) works in conjunction with constraint (10) to eliminate any scenario where a face is mined out in a period and shovel movement cannot be finished in that period, model tries to assign the shovel to the new face in the next period without modelling the movement time and the loss in production.…”

Section: Constraintsmentioning

confidence: 99%

“…The shovel allocation decision-making tool at this stage is required to optimise the operational objectives by providing optimal shovel placements and target productions. The scope of the existing multi-stage dispatching models [8][9][10][11][12][13][14] in the literature has remained limited to truck allocations for better operational results. The multi-stage model proposed by Soumis et al [10] provides shovel allocation decisions using man machine interaction, which render it unusable as a dynamic tool.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic shovel allocation approach to short-term production planning in open-pit mines

Upadhyay

Askari–Nasab

2017

International Journal of Mining, Reclamation and Environment

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Mining optimization laboratory (Mol), university of alberta, edmonton, canada ABSTRACT The complexity and uncertainties associated with mining operations often lead to deviations from short-term plans. A proactive approach will predict such deviations and help minimise opportunity losses, providing economic and operational gains. This paper presents an optimisation tool as a dynamic shovel allocation decision-maker for mining operation simulation models. The objectives of the optimisation tool are to provide shovel allocations to mining faces in order to maximise production, meet desired head grade and tonnage at the crushers, and minimise shovel movements. This paper presents the development and implementation of the optimisation tool with an iron ore mine case study.

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Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic shovel allocation approach to short-term production planning in open-pit mines

Upadhyay

Askari–Nasab

2017

International Journal of Mining, Reclamation and Environment

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“…Two major outcomes follow in all dispatching systems these are: To maximise the application of the shovel (to minimise the interruption time of the shovel) and to maximise the application of the trucks (to minimise waiting time of trucks i.e., queuing). A secondary outcome would be to maximise the loading capacities for trucks along different routes to ensure the operation reaches an accurate and required rate of production in each period (Temeng et al, 1997).…”

Section: Application Of Dispatching In Open Pit Minesmentioning

confidence: 99%

Real –time dispatching modelling for trucks with different capacities in open pit mines / Modelowanie w czasie rzeczywistym przewozów ciężarówek o różnej ładowności w kopalni odkrywkowej

Ahangaran¹,

Yasrebi²,

Wetherelt³

et al. 2012

Archives of Mining Sciences

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Application of fully automated systems for truck dispatching plays a major role in decreasing the transportation costs which often represent the majority of costs spent on open pit mining. Consequently, the application of a truck dispatching system has become fundamentally important in most of the world's open pit mines. Recent experiences indicate that by decreasing a truck's travelling time and the associated waiting time of its associated shovel then due to the application of a truck dispatching system the rate of production will be considerably improved. Computer-based truck dispatching systems using algorithms, advanced and accurate software are examples of these innovations. Developing an algorithm of a computer-based program appropriated to a specific mine's conditions is considered as one of the most important activities in connection with computer-based dispatching in open pit mines. In this paper the changing trend of programming and dispatching control algorithms and automation conditions will be discussed. Furthermore, since the transportation fleet of most mines use trucks with different capacities, innovative methods, operational optimisation techniques and the best possible methods for developing the required algorithm for real-time dispatching are selected by conducting research on mathematical-based planning methods. Finally, a real-time dispatching model compatible with the requirement of trucks with different capacities is developed by using two techniques of flow networks and integer programming. Keywords 40Zastosowanie w pełni zautomatyzowanych systemów dysponowania przewozami ciężarówek w poważny sposób przyczynia się do zmniejszenia kosztów transportu, które częstokroć mają poważny udział w kosztach funkcjonowania kopalni odkrywkowych. Dlatego też zastosowanie systemu dysponowania transportem ma kluczowe znaczenie w kopalniach odkrywkowych na świecie. Niedawne doświadczenia wskazują, że poprzez zmniejszenie czasu przejazdu ciężarówek oraz zmniejszenie czasu oczekiwania dzięki zastosowaniu systemu dysponowania przewozami możliwe jest znaczne podniesienie poziomu produkcji. Wspomagany komputerowo system dysponowania przewozami wykorzystujący odpowiednie algorytmy a także zaawansowane oprogramowanie stanowią przykłady nowoczesnych rozwiązań w tej dziedzinie. Opracowanie algorytmu komputerowego dostosowanego do konkretnych warunków panują-cych w danej kopalni odkrywkowej jest jedną z kluczowych czynności dla uruchomienia komputerowego systemu dysponowania przewozami ciężarówek w kopalni. W artykule przedstawiono zmieniające się trendy w zakresie programowania, algorytmów sterowania przewozami i automatyzacji. Ponadto, ponieważ większość kopalni odkrywkowych wykorzystuje ciężarówki o różnej ładowności, wybrano najlepsze i najnowocześniejsze metody i techniki optymalizacji oraz najskuteczniejsze metody opracowywania algorytmów wspomagających systemy dysponowania przejazdami ciężarówek w czasie rzeczywistym, wybrano je w oparciu o metody planowania wykorzystujące aparat matematyczny. W części ko...

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“…Several simulation algorithms based on mixed-integer linear programming [13][14][15], queuing theory [2,[16][17][18][19], linear programming [20][21][22], goal programming [23], and stochastic programming [24] have been proposed for fleet allocation. Simulation techniques based on the assignment-problem [25,26] and transportation-problem [27] approaches have also been developed for truck dispatching. Several other products for sophisticating truck dispatching, such as Modular Mining Systems' DISPATCH ® [28] and Caterpillar's CAT ® MINESTAR ™ [29], have also been commercialized.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively. truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, Tan et al. [39] identified discrete haulage-operation events by analyzing historical GPS truck-tracking data and implemented a truck-dispatch simulation algorithm using the Arena ® software. Chaowasakoo et al. [40] developed a real-time truck-dispatch system that plans truck-haulage strategies using truck-shovel activity-time data obtained from GPS.However, providing a comprehensive and effective truck-haulage systems simulation algorithm is difficult. First, open-pit mines are characterized by frequent changes in loading points during operations along with the use of multiple equipment-movement paths, unlike underground mines [1]. Additionally, several environmental factors, such as the weather, rock characteristics, and road conditions, affect haulage operations [12,41]. Thus, it is difficult to design a simulation algorithm for truck-haulage systems in large open-pit mines, where ore loading and haulage operations are simultaneously performed at multiple locations. Moreover, several types of simulation algorithms can be developed for a haulage plan that changes in real time, and it is difficult to alter these algorithms depending on variations in the haulage plan. Therefore, it is necessary to develop a simulation strategy that understands not only the characteristics of truck-haulage systems but also the sequence of discrete haulage operations. Additionally, the simulation technique must accurately predict ore production and equipment utilization using equipment-tracking data previously accumulated without use of prior-knowledge-based algorithms.Deep learning methods have recently attracted increased research interest because they facilitate easy analysis ...

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Real-time truck dispatching using a transportation algorithm

Cited by 39 publications

References 0 publications

Dynamic shovel allocation approach to short-term production planning in open-pit mines

Dynamic shovel allocation approach to short-term production planning in open-pit mines

Real –time dispatching modelling for trucks with different capacities in open pit mines / Modelowanie w czasie rzeczywistym przewozów ciężarówek o różnej ładowności w kopalni odkrywkowej

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

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