Matching truck-and-shovel operations in open-pit mines using statistical data - dispatching strategies, match factor, and age-based maintenance

Patarawan Chaowasakoo

Research output: ThesisDoctoral ThesisCollection of Articles


Economics today force mining companies to maximize profit over the life time of a mine. Especially in the context of open-pit mines, it is essential to acquire production with minimum cost. The ability to reduce operation costs can be directly achieved by utilizing trucks and shovels in an efficient manner. Dispatching approaches in the literature have considered different objectives in varying degrees of sophisticated assignments ranging from simple heuristic rules to complex mathematical programming. However, most approaches assume that all trucks and shovels have the same operating performance or ignore the stochastic nature of the truck-and-shovel operations. This thesis investigates one of the primary problems in an open-pit mine: efficient matching trucks and shovels. In other words, the aim is to determine the required number of trucks and shovels and their types to make the best match in order to satisfy the production target. This problem is investigated using different simulation and optimization models, which contain the behaviours of dispatching strategy, match factor, and age-based maintenance under an ideal operation and breakdown event. The results of this thesis show that the match factor ratio is able to determine limits for an appropriate fleet size selection, and can be used to estimate the relative efficiency of existing fleets. However, it cannot be used alone for fleet optimization. The choice of truck dispatching strategies and heuristic truck dispatching methods plays a crucial role to minimize the queuing time. Maintenance schedules are necessary to reduce breakdown, directly influencing equipment availability. Optimal preventive and corrective maintenance schedules are proposed for different truck age levels, providing cost savings. These proposed models offer potential applications to any situations in which truck fleets are used to transport material.
Translated title of the contributionKuorma-autojen ja kauhakuormaajien toimintojen yhteensovittaminen avokaivoksissa käyttäen tilastollista dataa - ajojärjestysstrategiat, yhteensovituskerroin ja kaluston ikäperusteiset huollot
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
  • Zhou, Quan, Supervising Professor
  • Koivo, Heikki, Thesis Advisor
Print ISBNs978-952-60-7492-4
Electronic ISBNs978-952-60-7491-7
Publication statusPublished - 2017
MoE publication typeG5 Doctoral dissertation (article)


  • simulation
  • match factor
  • heuristic truck dispatching methods
  • maintenance


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